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DEGREE PROJECT IN, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020
A SUMO analysis of the railway
traffic flow on the SOWETO
corridor
Factors influencing train operation
ZANDILE TSHABALALA
KTH ROYAL INSTITUTE OF TECHNOLOGY
SCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT
TRITA-ABE-MBT 20-670
A SUMO analysis of the railway traffic flow on the
SOWETO corridor
Factors influencing train operation
Zandile Tshabalala
Master’s Thesis
August 2020
School of Architecture and the Built Environment
KTH Railway Group
KTH Royal Institute of Technology
Stockholm, Sweden
i
FOREWARD
This thesis is dedicated to the 5M2A, a train set that has moved millions of passengers,
generation after generation.
It has been an interesting journey researching and compiling this thesis. It would not have
been possible without the help and contribution from the wonderful staff and students from
the Transport planning, economy and Engineering division at the School of Architecture and
the Built Environment at KTH.
A special acknowledgement and gratitude to Oskar Fröidh for steering me in the greatest of
paths with this thesis, unlocking my brain, making me think, and assisting in the evaluation of
this work. Anders Lindahl for creating an environment which made me to be more intrigued
and interested in railway I thoroughly enjoyed the railway courses you oversaw. Oskar and
Anders my gratitude for the comments, time, and energy you spent working with me, it was
very enjoyable working with you. Thank you, Albania “Bibbi” Nissan for being one of the
greatest educators I have met, your dedication and investment in your students was wonderful
to experience. I appreciate and thank you for ALL your help in my master’s journey.
To my mentor and friend Moreetsi Keraang whom this thesis would not have been fulfilled
without his enduring support and contribution, I thank you. I thank the former and present
staff from PRASA whose contributions and knowledge made the work what it is. Special
thanks to the guys from movinggauteng.co.za, Neville, Calvin and Tlhompho “Phaahla”
Phahlamohlaka your contribution and assistance in the realisation of this thesis cannot go
unsung. All the information you furnished me with played a tremendous role in this thesis,
thank you very much for all your hard work. Benedicta Nana Ama Ewusiwa Osam-Pinanko
and Martina Komuhendo, I appreciate the comments you have made to the thesis; they were
valuable and the sisterhood we formed made me feel at home in foreign land. Fortunate
Msimango and Duduzile Mtsweni, thank you for keeping sane me with your crazy stories
through what was sometimes a rough period, I love you guys!!
As they say, save the best for last. My family, the Mavuso clan, my biggest supporters, and
cheerleaders. It is a privilege and a blessing to be born in this family I love and appreciate
you. To my number one fan the matriarch of the family, everybody’s best friend, my
grandmother Samariet “MaSibiya” Shabalala thank you for your undying love and support
without you I would not exist, akwande Ngwane.
Zandile Tshabalala, Stockholm, August 2020
ii
iii
ABSTRACT
40% of public transport users in the Johannesburg Metropolitan use the train as a preferred
mode of transport for home to work journeys. Out of the 40% train users in Johannesburg,
35% travel from Johannesburg South to the East of Johannesburg and the 5% travel to the
North side of Johannesburg.
There are three main interconnecting stations in Johannesburg that is the Johannesburg Park
Station (Central Johannesburg), Germiston Station (East of Johannesburg) and New Canada
Station (South of Johannesburg). The study investigates the traffic from the South as it has
massive patronage, and it experiences overcrowded trains, congestions, and delays. New
Canada Station is the interconnection for traffic in these following routes,
Vereeniging/Oberholzer- New Canada – Johannesburg- George Goch (Red Route), Naledi –
New Canada – Johannesburg (Yellow Route) and Naledi – New Canada – George Goch
(Blue Route). The red route experiences heavy delays and overcrowding, due to several
factors like the distance between Johannesburg and Vereeniging, minimum headway of
approximately 20 mins and the overcrowding which is a consequence of new townships
developing around the railway lines.
Three plans or scenarios were implemented on the SOWETO corridor traffic evaluation. The
first plan uses the timeslots from the operator’s timetable, which had varying headways. The
second plan evaluates the traffic when the headway has equal intervals, and the last plan
assesses the introduction of route(s) given the results from the first and second plan. Dwell
time, vehicle type are variables used to analyse the train traffic on the SOWETO corridors, a
simulation using SUMO in conjunction with Python was implemented. The older train set
specifications (5M2A called TYPE A) and the newer train set specifications (Xtrapolis called
TYPE B) were used in the simulation.
The headway influences the manner passengers arrive at the stations. Passengers arrive in
large amounts when the headways are longer as most passengers turn to be reliant on the
timetable. Varying headways experienced more delays than equal interval headways. TYPE
A vehicles have longer travel time in comparison to TYPE B vehicles, the travel time is
longer by (1-2) minutes. TYPE B vehicles have lesser dwell time due to the arrangement of
their doors. Routing, assignment of vehicles to routes, design of a vehicle, passenger arrival
rate and the headway are essential parts of a well performing network, as they influence the
dwell time, delays, and congestion.
iv
v
TABLE OF CONTENT
1 . INTRODUCTION ............................................................................................................ 1
1.1 Background ................................................................................................................. 1
1.2 Problem Statement ...................................................................................................... 3
1.3 Aim .............................................................................................................................. 3
1.4 Delimitation ................................................................................................................. 4
1.5 STRUCTURE OF THE REPORT .............................................................................. 6
2 . Area of Study .................................................................................................................... 9
2.1 South African Railways .............................................................................................. 9
2.1.1 Signalling System (Train Authorisation) ........................................................... 12
2.2 Rolling stock ............................................................................................................. 14
2.2.1 Class 5M2A ....................................................................................................... 14
2.2.2 Xtrapolis ............................................................................................................. 14
2.2.3 Gauteng South Railway Network ...................................................................... 15
3 . LITERATURE REVIEW ............................................................................................... 19
3.1 Capacity ..................................................................................................................... 19
3.2 Simulation application............................................................................................... 20
3.3 Land use and spatial planning ................................................................................... 22
4 . METHODOLOGY ......................................................................................................... 23
4.1 Data Collection .......................................................................................................... 23
4.1.1 Concept Development of the research ............................................................... 23
4.2 Preparation and execution for model development ................................................... 23
4.2.1 Gathering of information ................................................................................... 23
4.2.2 Summary of the Questionnaire for the operator ................................................ 23
4.2.3 Timetable ........................................................................................................... 24
4.3 Conceptualisation of the Model ................................................................................ 25
4.3.1 Input parameters to the model............................................................................ 25
4.3.2 Calibration and Validation of the model ............................................................ 30
5 . SIMULATION OF URBAN MOBILITY (SUMO) SOFTWARE PACKAGE ............ 32
5.1 Generating the Network ............................................................................................ 32
5.2 Generation of Routes and Vehicles ........................................................................... 34
5.3 Simulation ................................................................................................................. 35
5.4 Assessment of different Railway Simulation Tools .................................................. 36
5.4.1 OpenTrack.......................................................................................................... 37
vi
5.4.2 RailSys ............................................................................................................... 38
5.5 Comparison of the simulation tools .......................................................................... 39
6 . RESULTS AND ANALYSIS ........................................................................................ 41
6.1 Input parameters ........................................................................................................ 44
6.2 Calibration and Validation ........................................................................................ 47
6.3 Simulation results ...................................................................................................... 49
6.3.1 Varying headways timetable- Plan 1 ................................................................. 50
6.3.2 The impact of equal interval shorter headways-Plan 2 ...................................... 62
6.3.3 Adding route(s) with same headway- PLAN 3 .................................................. 66
7 . CLOSING REMARKS................................................................................................... 73
7.1 DISCUSSION ........................................................................................................... 73
7.1.1 The Infrastructure............................................................................................... 73
7.1.2 Rolling Stock ..................................................................................................... 74
7.1.3 Operation characteristics .................................................................................... 74
7.1.4 Simulation .......................................................................................................... 75
7.2 CONCLUSION ......................................................................................................... 77
7.3 SIDE NOTES (Recommendations) ........................................................................... 78
7.3.1 Passenger surveys .............................................................................................. 78
7.3.2 Use of Open Source programmes ...................................................................... 78
7.3.3 Passenger railway research and studies ............................................................. 78
8 REFERENCES ................................................................. Error! Bookmark not defined.
9 APPENDICES .................................................................................................................. 82
9.1 Appendix B: Tables from results for Varying Headways – Plan 1 ........................... 82
9.2 Appendix C: Tables from results for Same Headway- Plan 2 .................................. 90
vii
LIST OF FIGURES
Figure 1: Spatial distribution between Johannesburg and Vereeniging. Source: (Mubiwa &
Annegarn, 2013) ........................................................................................................................ 2
Figure 2: Organogram of the report ........................................................................................... 6
Figure 3: South African Railway Map showing active and non-active railway lines. Source:
overendstudio.co.za.................................................................................................................... 9
Figure 4:Multi-aspect Colour Signal. Source (JICA,2013) ..................................................... 12
Figure 5:Single aspects on the approach to a station. Source (v. d Merwe, 2018) .................. 13
Figure 6: Flashing aspects on approach to a switch and station. Source (v. d Merwe, 2018) . 13
Figure 7: An in-transit 5M2 train . Source: www.wikipeadia.com ......................................... 14
Figure 8: Xtrapolis train at one of the stations. Source: https://www.railway-technology.com/
.................................................................................................................................................. 14
Figure 9:Gauteng Metrorail rail map. Source: www.wikiwand.com ....................................... 15
Figure 10: Gauteng Railway Network. Source: de.wikipedia.org ........................................... 17
Figure 11: Map showing New Canada (OpenRailMap,2020) ................................................. 18
Figure 12: Aerial view of New Canada Station. Source: www.maps.google.com .................. 18
Figure 13:Model Conceptualisation framework. Source (Robinson,2004) ............................. 25
Figure 14: Forces exacted on a moving train Source: (Seimbille, 2014)................................. 26
Figure 15: Subprocess of train dwell time. Source: (Gysin, 2020) .......................................... 29
Figure 16: Generating of network in Sumo. Source (Daniel, et al., 2012) .............................. 33
Figure 17: Network for the SOWETO corridor, surrounding areas of New Canada Station.
Generated using net-convert .................................................................................................... 33
Figure 18: Main Elements of OpenTrack. Source: (Nash & Huerlimann, 2004) .................... 37
Figure 19: The process used for multiple timetable analysis with simulation of nominal and
operational timetables Source: (Sipilä, 2015) .......................................................................... 38
Figure 20: Flow chart of the methodology for the analysis ..................................................... 41
Figure 21:Average vehicle flow at the statins for both random and fixed dwell times ........... 52
Figure 22:Average vehicle occupancy at the statins for both random and fixed dwell times . 53
Figure 23:Average vehicle speed at the stations for both random and fixed dwell times ....... 54
Figure 24: Visualisation of Table 16 of the Flow of vehicles for TYPE A, TYPE B and
Combination (A and B) ............................................................................................................ 59
Figure 25: Visualisation of Table 16 of the occupancy of vehicles for TYPE A, TYPE B and
Combination (A and B) ............................................................................................................ 60
viii
Figure 26: Visualisation of Table 16 of the occupancy of vehicles for TYPE A, TYPE B and
Combination (A and B) ............................................................................................................ 61
Figure 27: Average vehicle flow at the stations for both Plan 1 and Plan 2 ............................ 63
Figure 28: Occupancy at the stations for both Plan 1 and Plan 2 ............................................ 64
Figure 29: Average Speed at the stations for both Plan 1 and Plan 2 ...................................... 65
Figure 30: Frame 1, Frame 2 showing routes the area where waiting time is longer .............. 67
Figure 31: Frame 3 showing introduction of the Magenta route ............................................. 68
Figure 32: Changes made to the network to minimise congestion .......................................... 68
Figure 33: Average vehicle flow at the stations for both Alt1, Alt2 and Alt3 ......................... 70
Figure 34: Vehicle occupancy at the stations for both Alt1, Alt2 and Alt3............................. 71
Figure 35: Average vehicle speed at the stations for both Alt1, Alt2 and Alt3 ....................... 72
ix
LIST OF TABLES
Table 1: Population in the years 2001 and 2011. Source: census2001.adrianfrith.com/place,
census2011.adrianfrith.com/place.............................................................................................. 3
Table 2:Infrastructure and operation specification for the PRASA network (JICA, 2013) ..... 11
Table 3: Infrastructure and operation specification for the TRANSNET network (JICA, 2013)
.................................................................................................................................................. 11
Table 4: Infrastructure and operation specification for the GAUTRAIN network. Source:
(JICA, 2013) ............................................................................................................................ 12
Table 5: Equations to estimate dwell time at the Stations. Source: (Gysin, 2020) .................. 28
Table 6: Initial routes from the Metrorail timetable for the Gauteng South corridors ............ 35
Table 7: Comparison characteristics of the SUMO, RailSys, OpenTrack. Source:
(OpenTrack.ltd, n.d.), (Daniel, et al., 2012), (Lautala & Pouryousef, 2013)........................... 40
Table 8: Type A vehicle specification used in the simulation ................................................. 42
Table 9: Type B vehicle specification used in the simulation ................................................. 43
Table 10: Extended routes from the Metrorail timetable for the Gauteng South corridors and
additional route analysed ......................................................................................................... 44
Table 11: Estimated number of passengers arriving at each station on the Naledi-New Canada
line. Source www.statssa.gov.za .............................................................................................. 45
Table 12: Estimated number of passengers arriving at each station in the Ver-New Canada
line. Source: www.statssa.gov.za ............................................................................................. 46
Table 13:Values retrieved before and after calibration from the observed data ...................... 47
Table 14: Values retrieved for validation of the model ........................................................... 48
Table 15: times when the train are at the New Canada Station ............................................... 50
Table 16: Results for flow, track capacity, and the average speed at each platform for
considered Stations for TYPE A vehicle ................................................................................. 51
Table 17: Results for flow, capacity, and the speed at each platform for considered Stations
for TYPE B vehicle .................................................................................................................. 55
Table 18:Departure times for TYPE A and TYPE B vehicles for random dwell times .......... 56
Table 19: flow, capacity, and the speed at each platform for considered Stations for ALL
vehicle types............................................................................................................................. 58
Table 20: Flow, capacity, and the speed at each platform for PLAN 1 and PLAN 2 .............. 62
Table 21: Average Flow, Occupancy, and speed on each platform in the concerned Stations69
x
Table 22: Departure time for TYPE A vehicle with fixed dwell and random times for the
Yellow Route ........................................................................................................................... 82
Table 23: Departure time for TYPE B vehicle with fixed dwell and random times for the
Yellow Route ........................................................................................................................... 83
Table 24: Departure time for TYPE A vehicle with fixed and random dwell times for the Blue
Route ........................................................................................................................................ 84
Table 25: Departure time for TYPE B vehicle with fixed and random dwell times for the Blue
Route ........................................................................................................................................ 85
Table 26:Departure time for TYPE A vehicle with fixed and random dwell times for the Red
Route ........................................................................................................................................ 86
Table 27: Departure time for TYPE B vehicle with fixed and random dwell times for the Red
Route ........................................................................................................................................ 87
Table 28: Departure time for TYPE A and TYPE B combination with removed train(s) on the
Yellow Route. Only Random dwell times teleported vehicles removed ................................. 87
Table 29: Departure time for TYPE A and TYPE B combination with removed train(s) on the
Blue Route. Only Random dwell times ................................................................................... 88
Table 30: Departure time for TYPE A and TYPE B combination with removed train(s) on the
Red Route. Only Random dwell times .................................................................................... 89
Table 31: Departure times Yellow route when MLA-JHB is the red route ............................. 90
Table 32: Departure times red route when MLA-JHB is the red route ................................... 91
Table 33: Departure times red route when MLA-JHB when the dwell time is random .......... 92
Table 34: Departure times Blue route when MLA-JHB is the red route ................................. 93
Table 35: Departure times Blue route when MLA-JHB is the magenta route ......................... 94
Table 36: Departure times Yellow route when MLA-JHB is the magenta route .................... 95
xi
ABBREVIATIONS
ACRONYM FULL MEANING
ALOI At Least One Inequality
CTC Centralised Train Control
DBSA Development Bank of Southern Africa
JHB Johannesburg
JICA Japan International Cooperation Agency
MLA Mlamlankunzi
NAL Naledi
OHTE OverHead Traction and Electrification
PRASA Passenger Rail of South Africa
SOWETO South Western Township
StatsSA Statistics South Africa
SUMO Simulation of Urban MObility
UIC International Union of Railways
VER Vereeniging
WITS Witwatersrand
xii
1
1 . INTRODUCTION
1.1 Background
There are two main reasons for the need of an analysis in the railway sector, the first one is
that the railway is not functioning to satisfactory standards, and the second is to conduct an
improvement analysis for the railway sector. The former is the greatest motivation for this
thesis.
Capacity in railway varies in terms of the technique and objectives of the specific study
(Pouryousef, et al., 2015). Hansen and Pachl, (2008b), Sameni et al (2011b) have defined it
as a measure of ability to move a certain amount of traffic on a corridor depending on the
level of service and Krueger (1999), Transportation Research Board (2003) defined it as
number of trains per normal day designed for that track (Sameni, 2012). International Union
of Railways (UIC CODE 406, 2013) says there is no particular manner to define capacity in
the railway sector but conclude that capacity is affected by the relationship and dependency
between four major factors, the average speed, stability of operation, amount of trains and
heterogeneity. Definitions depends on the goal of infrastructure managers, timetable, and
operation planners. Some authors in capacity studies have identified characteristics that have
influence in capacity in the railways, these factors include infrastructure specifications,
rolling stock specifications and operational specifications. Some of these factors are
evaluated in this thesis.
The greater Johannesburg metropolitan has seen growth in number of residents, which
consequently led to the provincial and local governments to increase residential land-use.
Unfortunately, the settlements were built in peripheral areas of Johannesburg. This meant that
residents would have to commute from their suburbs to the city, where most workplaces are
located. During these years 1991-2001 there was a transition from mining areas to more
urban areas around the proximity of Johannesburg (Mubiwa & Annegarn, 2013), this saw an
increase in the occupation of open spaces on grasslands and bare lands. There was a strong
urban sprawl along transport corridors especially the railway line as Figure 1 shows.
2
Figure 1: Spatial distribution between Johannesburg and Vereeniging. Source: (Mubiwa & Annegarn,
2013)
In Figure 1 (a) the red part is the difference between spatial arrangement in 2001 and 1991
this figure demonstrates the beginning of the shift in the spatial arrangement, where there was
a spike in the size of the land occupied when compared to Figure 1 (b) where the expansion
of land between 2001 and 2009 was lower. Table 1 shows the increase in the population of
the areas represented in Figure 1. The numbers are from the census conducted every 10 years
by Statistics South Africa, (StatsSA). Table 1 shows the population in 2001 and 2011 and the
increase in percentages of the population from 2001 to 2011, each area represented in Figure
1 shows significant growth in the population. Figure 1 (a) shows that Oakmere was barely
occupied in 2001 and 2009, but later experienced an increase in land occupation as seen in
Figure 1 (b) this is concurrent with the data in Table 1. Though, the population in Oakmere is
small in comparison to others in 2011, it had population growth that was three times more
than its population in 2001. In 1991 the urban built-up area in Johannesburg Metropolitan
was 42%, this increased by 8,5% in 2001. In the years 2001 and 2009 the urban built-up area
increased by 6,4% for the Johannesburg Metropolitan (Mubiwa & Annegarn, 2013).
Therefore, the urban built-up area in the Johannesburg Metropolitan increased from 42% in
1991 to 57% in 2009
3
AREA POPULATION
Increase in
Population
2001 2011 %
Stretford 43 569 64 141 47.21
Orange Farm 50 137 76 767 53.11
Lakeside 14 832 23 503 58.46
Drieziek 16 613 35 622 114.42
Oakmere 1 978 6 200 213.44
Ennerdale 39 504 71 815 81.79
Lenasia South 26 182 37 110 41.73
Lawley 18 095 33 136 83.12
Table 1: Population in the years 2001 and 2011. Source: census2001.adrianfrith.com/place,
census2011.adrianfrith.com/place
According to (Mubiwa & Annegarn, 2013) the urban sprawl tended to gravitate towards
places where there is established transport infrastructure. Mubiwa & Annegarn (2013)
continues to mention that train stations tended to attract and promoted the expansion of
informal settlements, whilst major road corridors attract retail, industrial and office park.
1.2 Problem Statement
The rapid urban sprawl has put the railway sector under severe pressure, as there are more
people to be transported, whist the infrastructure remained the same. Mubiwa & Annegarn
(2013) stated that most people tended to settle in areas closest to the Stations as travelling by
railway is the cheapest and safest mode of transport. Due to this phenomenon, the railways in
SOWETO corridor experienced an increase in train delays, cancellation of trains and
overcrowded trains. Analysing the causes of overcrowding and the delays is the first step to
solving the problem that the railway is experiencing in SOWETO corridor.
1.3 Aim
An increase in population created an environment where there is a need for an increased
supply of rail traffic. According to the PRASA’s technical framework, theoretically, PRASA
commuter rail corridors have capabilities to carry approximately 60 000 – 80 000 passengers
an hour during peak periods exclusive of region.
4
To date, SOWETO corridor also called the Gauteng South corridor is serviced by the older
train sets in comparison to the other Gauteng corridors, which is called the 5M2A. The 5M2A
fleet is still functional and some trains are refurbished to what is called the M-type family that
is 10M2, 10M3 and 10M4. PRASA intends also to introduce the Xtrapolis, that is designed
by Alstom which is the newer version and slightly different from the home designed M-type
family. The introduction of the Xtrapolis is to combat the overcrowding and congestion
experienced on this corridor. Therefore, it is imperative to understand how these two types of
trains will function on the SOWETO corridor, as the rate of introduction of the newer train
sets will be in stages.
New Canada Station is in South West of Johannesburg and it is identified as the Station that
experiences congestion due to traffic from the Southern routes. New Canada Station is the
intermediate Station for traffic coming from the South of Johannesburg headed to the North
and East of Johannesburg. From 2014 the traffic from the South included these corridors:
1. Vereeniging Station - New Canada - Johannesburg Park Station- George Gogh
2. Oberholzer Station - New Canada - Johannesburg Park Station,
3. Naledi Station- New Canada - Johannesburg Park Station
4. Naledi Station – New Canada - George Goch Station occasional via Booysen,
Faraday and Westgate
The purpose of the thesis is to determine or explore ways that will benefit the Metrorail
operators by finding ways to improve the overcrowding and congestion on the SOWETO
corridor by using some of the techniques from capacity studies and the use of the SUMO
simulation tool. Dwell time, vehicle type in relation to passenger arrival rate are evaluated to
assist in the understanding of train traffic at the New Canada Station.
1.4 Delimitation
-Total Network not considered
The Gauteng South network is sparsely distributed as it is a commuter train. For example, the
distance between Vereeniging and Johannesburg is approximately 90km. Therefore,
uploading the total network using openstreetmap.com for the whole SOWETO corridor was
too large for the Application to handle. A single peak time was used instead of the evaluation
5
of the whole day. The morning peak time was the only peak time selected as it had more
traffic to evaluate.
-Full Empirical data
Passenger data is not well collected in the SOWETO corridor. Therefore, the passenger data
used in this thesis is an estimation using the population in 2km radius. Full train traffic data
was not readily available also. The data given was found to be scattered and too few to have a
conclusive prognosis of the train traffic in the SOWETO corridor
-Simulation Package
SUMO simulation package is not widely used in the railway sector. It is mostly used as a
road simulation package. Nonetheless, it can be used in the railway’s simulation, but it has
limitations. It is a continuous simulation tool. Therefore, it does need effort from the user, it
is not a user-friendly simulation tool. There are some glitches when it comes to moving from
road simulation to railway simulation which needs a vigilant eye.
6
1.5 STRUCTURE OF THE REPORT
Figure 2: Organogram of the report
7
Section 2: AREA OF STUDY
This section describes the type of infrastructure considered in the analysis both static (track,
station, and signalling) and moving (rolling stock). This is done to give the reader some
background on the decision that may be taken in the implementation, evaluation and the
analysis done in the thesis.
Section 3: LITERATURE REVIEW
This section gives a description of the selected literature used in the report. The literature
mentioned inspired the approach to the thesis. The variety of topics studied range from older
methods for analysing capacity on the railways, newer methods of analysing capacity on the
railways, optimising, analytical and simulation methods.
Section 4: METHODOLOGY
The procedures taken in the thesis from the inception to the final product. This section is a
step by step description of the decision taken in the evaluation and analysis. It encompasses
the ideas formulated from Section 2: LITERATURE REVIEW.
Section 5: SIMULATION OF URBAN MOBILITY (SUMO) SOFTWARE PACKAGE
SUMO is not a commonly used simulation tool in the railway sector. Therefore, Section 5
discusses the differences and similarities between SUMO and the commonly used simulation
tools like OpenTrack, and RailSys. The exploration of the differences and similarities for the
simulation tools, is not of a performance study but looks at the form and nature that each
simulation tool has from various literatures. SUMO is a continuous simulation tool which
means it involves writing languages to be compiled by a programme, making it slightly not
user-friendly.
Section 6: RESULTS and ANALYSIS
This section deals with the outputs from the methods taken in the study. The resulting output
are analysed to determine the type of results found, whether these results can answer the
8
problem statement and aim or whether they do not. A slight description of the procedure used
is also mentioned in this Section.
Section 7: CLOSING REMARKS
This is the part of the report where the results and the analysis are discussed in depth.
There is a discussion section where the results and analysis are rigorously interrogated by the
author. Whether the solutions found are what is expected, or they have yielded a different
answer and have counter arguments to the analysis if there are any. The conclusion is a
summary of the discussion giving final position and thoughts of the author according to the
results found. There are side notes which are some of the things that the author has noticed
and evaluated. These side notes include improvements in accordance with the results, some
possible future endeavours are discussed in this section that the operator can use for
improving the status quo. Improvement to the railway sector in general are also given in this
section.
9
2 . Area of Study
2.1 South African Railways
The South African railway network comprises of approximately 22000 of route-km. The core
network has 19 000 of route-km which is owned Transnet freight rail, 2 200 of route-km is
owned by Passenger Rail of South Africa (PRASA), and round about 80 route-km is
managed by Gautrain (DBSA, 2013). Metrorail a subsidiary of PRASA, which is responsible
for operation and maintenance of commuter railway infrastructure.
Figure 3: South African Railway Map showing active and non-active railway lines. Source:
overendstudio.co.za
Figure 3 shows the total network of the railway in South Africa. The core network comprises
of freight and passenger rail. The core lines as shown in Figure 3 are normally shared
between long distance passenger rail and freight rail. The core lines that are not within the
major Metropolitan areas are diesel operated, whilst those that are in the Metropolitan areas
and surrounding areas are electrified. Metrorail, which is a PRASA subsidiary is responsible
10
for commuter trains, operating in KwaZulu Natal Province (eThekwin Metro), Eastern Cape
Province (Bhayi, Tinarha, Monti and Berling), Western Cape Province (Kapa and Worcester)
and Gauteng Province (Sedibeng, Johannesburg, and Tshwane)
The South African railway uses the 1067mm gauge called Cape gauge for all PRASA and
TRANSNET railway lines. The Cape gauge is mostly used in Southern African countries like
South Africa, Botswana, Zambia, Zimbabwe and so forth. The Gautrain uses 1435mm
standard gauge with 25 kV AC overhead traction and electrification (OHTE). PRASA and
TRANSNET use different electrification type for current and voltage. PRASA uses the
standard 3000v DC OHTE for most of their lines. Whilst TRANSNET varies according to
load, traffic, and area, for example the Iron ore line between Saldanha Bay and Sishen uses
50kV AC OHTE , most of the general freight railway transportation either use diesel traction,
or they use 25kV OHTE or 3kV OHTE depending on the purpose. In passenger rail, PRASA
uses rail size of 48kg/m and Gautrain uses 56kg/m. The Saldanha – Sishen Iron ore line is
extremely specific on the rails as it uses the heaviest rails 60kg/m, whilst the general freight
uses either the 56kg/m or 48kg/m. Other specifications can be viewed in Table 2, Table 3 and
Table 4 which show the infrastructure specification of the railway system in South Africa
according to the infrastructure owner and operator. Table 4, GAUTRAIN has different
specifications compared to TRANSNET and PRASA this is due to that GAUTRAIN is a new
railway network and only operates in Gauteng Province.
11
PRASA TRANSNET
Table 2:Infrastructure and operation specification
for the PRASA network (JICA, 2013)
Table 3: Infrastructure and operation specification for the
TRANSNET network (JICA, 2013)
12
GAUTRAIN
Table 4: Infrastructure and operation specification for
the GAUTRAIN network. Source: (JICA, 2013)
2.1.1 Signalling System (Train Authorisation)
The South African railway signalling system guidelines and norms are not well documented
(v. d Merwe, 2018). The Metrorail signalling system uses a combination of colour light
signalling and fixed block operation, the radio, telegraph, token signalling is also in use
(JICA, 2013). The centralised train control (CTC) and station control are the main methods
used for train authorisation. Metrorail colour lighting signalling uses three colour aspect and
the multiple colour aspect signalling system see Figure 4.
The multiple-colour signalling includes
the so called Red-Blue light, which is an
emergency signalling light. Red-Blue
light works in a manner that even if the
red light is turned on, if the blue light is
also switched on, the train can proceed
with caution after confirming with the
train dispatcher. When the red lights start
Figure 4:Multi-aspect Colour Signal. Source
(JICA,2013)
13
flashing it informs the driver to stop and when the blue lights flash it means the driver can
move with extra caution (JICA, 2013). There is also the yellow-flash aspect, that are used in
as precaution to the driver.
Figure 5:Single aspects on the approach to a station. Source (v. d Merwe, 2018)
Fixed block signalling is a basic requirement of providing a timely warning to train drivers
(van der Merwe, 2018). Figure 5 shows the signalling sequence when the train is
approaching the platform or Station. The red signal means STOP. The green signal means
that the train driver can safely proceed with speed designated to the route. The yellow signal
informs the driver that must proceed with caution and may need to halt on the next signal.
The signalling system differs when the train approach turnouts compared to when train
approaches the Stations. There is an extra element that is added to the signalling system
which is the flashing lights Figure 6 shows the position of the flashing lights. The flashing
yellow is indicating that the driver may proceed and be prepared to switch tracks. Figure 5
and Figure 6 show a typical signalling system used in South African railway system.
Figure 6: Flashing aspects on approach to a switch and station. Source (v. d Merwe, 2018)
14
2.2 Rolling stock
2.2.1 Class 5M2A
The Class 5M2As see
Figure 7 operates on the
1,067 mm Cape gauge track.
It uses 3kV DC OHTE, 3kV
OHTE is standard voltage
that is used in all Metrorail
network. 5M2A’s have a
power output of 925
kilowatts and produce
approximately 160 kN of
tractive effort. The
maximum speed of a 5M2A trainset is 100 km/h. The driver coach can carry 56 seated and
110 standing passengers. A trailer coach can carry 52 seated and 149 standing passengers.
2.2.2 Xtrapolis
Xtrapolis shown in Figure 8 is a
high capacity suburban or
regional train. It runs at 120km/h
but can be easily converted to run
at maximum speed of 160km/h
(Gibela, n.d.). It can handle
approximately 30000 passenger
per hour in each direction and is
equipped with Automatic Train
Operation. It consumes less
energy 31% lesser than its
counterpart as it is lighter and has
regenerative braking system. A
train set normally has six coaches/cars, length of 131.42 metres, width of 2.75 metres floor
height of 1.1 metres, platform height (0.8 – 1,07) metres, gangway width 1.35 metres and two
Figure 7: An in-transit 5M2 train . Source: www.wikipeadia.com
Figure 8: Xtrapolis train at one of the stations. Source:
https://www.railway-technology.com/
15
bogies. The train’s modularity allows it to carry 18 car body modules (Railway Technology,
n.d.). It is designed to have wider doors to enable optimal passenger flow during rush hour.
Each vehicle can carry up to 1218 standing capacity and 380 sitting capacity.
2.2.3 Gauteng South Railway Network
Gauteng Province is one of South Africa’s nine provinces, it is the economic hub of South
Africa. It is the smallest province in area with total area of 18,176 km2 and the biggest
province in population approximately 15,176,115 people reside in Gauteng, it very densely
populated having 685 occupants per kilometre squared. It is situated in the North-Eastern part
of South Africa. There are two major cities in Gauteng Pretoria and Johannesburg.
Johannesburg is the capital city of Gauteng, though Pretoria is the capital city of South
Africa. Geographically, most of Gauteng is Highveld, a South African term for inland plateau
which has an altitude between 1500 m and 2100 m.
Gauteng South Metrorail network also known as Metrorail Wits has approximately 154
functional stations. Ten rail passenger services are operated in the Gauteng South region.
Seven of the passenger services passes through the greater Johannesburg metropolitan area,
the lines are shown in Figure 9 the Gauteng South railway network and the
communities/areas they service (JDA, 2010):
Figure 9:Gauteng Metrorail rail map. Source: www.wikiwand.com
16
Germiston–Kwesine: services Germiston and Katlehong. Light blue line
Germiston–Kliprivier–Vereeniging services Germiston, Katlehong, Meyerton and
Vereeniging. Turquoise blue line
Germiston–New Canada: services Germiston and the Reef south of central Johannesburg.
Blue line
Johannesburg–New Canada–Vereeniging services Johannesburg, Orlando, Lenasia,
Sebokeng and Vereeniging. Mustard yellow line
Johannesburg–Oberholzer: services Johannesburg, Orlando, Westonaria, and Carletonville.
Dijon yellow line
George Goch–Johannesburg–Naledi: serves Johannesburg and Soweto. Mustard yellow
line
Johannesburg–Randfontein services Johannesburg, Roodepoort, Krugersdorp, and
Randfontein. Lime green line
Johannesburg–Dunswart–Daveyton: services Johannesburg, Germiston, Boksburg and
Daveyton. Green line
Johannesburg– Springs services Johannesburg, Germiston, Boksburg, Benoni, Brakpan and
Springs. Bottle/Dark Green line
Johannesburg–Leralla/Pretoria services Johannesburg, Germiston, Kempton Park,
Tembisa, Centurion and Pretoria. Red line. The Johannesburg line is the line the separate the
Gauteng railway into Gauteng South and Gauteng North railway network.
17
Figure 10: Gauteng Railway Network. Source: de.wikipedia.org
Most of the railway lines and traffic are concentrated in Gauteng South Region (Wits
Metrorail) the circled area Figure 10. According to PRASA Gauteng South Region amounts
to 73% of the total network of the Gauteng Metrorail. Johannesburg, New Canada, and
Germiston are the busiest stations in Gauteng. The daily passengers on the ‘core’ network
between New Canada and Germiston is approaching 200,000 passengers per day
approximately 35% of the Gauteng South volume. (JDA, 2010).
2.2.3.1 New Canada Station
Figure 11 shows the locality of New Canada Station, it is in SOWETO South-West of
Johannesburg. The Station is flanked by Mlamlankunzi, Mzimhlophe, Longdale and Crown
Stations. It is a recipient of traffic from the Vereeniging to Johannesburg route, Naledi to
Johannesburg route, Naledi to George Goch route and Oberholzer to Johannesburg route.
18
Figure 11: Map showing New Canada (OpenRailMap,2020)
The New Canada Station services sixty thousand people a day where most of the passenger
are from SOWETO. There 8 platforms, 4 dedicated to the North bound traffic and the other 4
dedicated to the South bound traffic see Figure 12 at the station and a single entrance located
on the East side of the station and it is an interchange station and had a siding located on the
West side of the platforms. It is also one of the busiest train Station on the Gauteng Region.
Figure 12: Aerial view of New Canada Station. Source: www.maps.google.com
19
3 . LITERATURE REVIEW
3.1 Capacity
Ding, et al (2016) developed methodologies to measure carrying capacity for the combined
urban railway traffic that is the express and slow mode. They assessed a suburban area and
the inner city where the commuter train services the suburb and the rapid transit rail services
the inner city. When comparing the two railway services the commuter train is found to be
longer, the passenger flow is uneven due to the spatial and temporal distribution of the
suburban areas. Seven scenarios were evaluated ranging from lines where overtaking is not
permissible and more than one overtaking is permissible, the ratio of the number of rapid rail
train to commuter train taken into consideration.
Zhong, et al (2018) evaluates the capacity by utilising the blocking time theory to manage
train runs. The capacity analysis focuses on the infrastructure capacity evaluation in terms of
the time that is consumed or by the number of trains that can be operated based on real
timetables. One direction double track infrastructure whose operation is based on blocking
time theory would be the operational output. A train operating on the infrastructure is always
defined in a section, the information for analysing the utilisation of infrastructure from the
timetable is denoted as the operational inputs. The operational inputs are time for signal set
up, time for signal confirmation, approach time, running time, time for clearance, time for
release, scheduled stop, operational sequence, and overtaking arrangement. These are the
inputs that Zhong et al (2018) used to determine the capacity analysis for the train and the
infrastructure capacity.
Lindfeldt, A. (2015) analyses the underlying behaviour of congestion on railways. According
to Lindfeldt (2015) the maximum capacity is reached when the marginal gain of operating an
extra train is lower than the costs in terms of longer travel times and increased sensitivity to
delays. Lindfeldt utilised two approaches in the analysis. The first approach used train
operation and delays of actual data from the Swedish rail network, then he analysed how
different factors influence available capacity and train delays. The second approach, utilised a
simulation method, scenarios conducted to analyse the influence of traffic density, traffic
heterogeneity, primary delays, and inter-station distance on secondary delays, used timetable
allowance and capacity
20
Salido, et al (2012) determined a robust solution necessary to absorb short disruption in the
train operation system. They have identified some parameters directly related to robustness.
The parameters are considered in the analytical methods to give a measure of robustness.
The parameters are then compared to the simulation method to verify the measurement
obtained.
3.2 Simulation application
Daniel, et al (2012) give a description of how the SUMO simulation tool functions. They
explain how to conduct a road or rail simulation in SUMO. They cover the basic concept that
a simulation application tool should have for an example, attaining a network. In SUMO one
can create a network manually using netgenerate or it can be extracted from the website
OpenStreetMap.com using netconvert. A guide to route and vehicle design is also explained
as they are inter-related, a description of how the output functions works is also provided.
Lautala & Pouryousef, (2013) did a comparison study of two simulation package tools
RailSys a timetable based European simulation tool and RTC a non-timetable based North
American simulation tool. In the comparison study they note that in North America the non-
timetable simulation software is preferred as it has capabilities to improvise. The
improvisation comes at a cost as the software may encounter a problem in assigning all trains
and may need manual manoeuvring from the user to resolve the issue by adjusting the train
data. The timetable-based simulation software tool also has its limitation when there is a
conflict in the schedule, the user must change the timetable until it is workable. In their
analysis they found that though both packages are powerful rail simulation tools, RTC has an
easier operation rules and dispatching system. RailSys struggles to adjust to the American
signalling and rolling stock. According to them, the adjustment and calibration of the
parameter for the rolling stock and signalling is time consuming. They also determined that
RailSys has a better optimisation scheduling tool than RTC because RailSys provides more
capacity levels for a given scenario
Nash & Huerlimann, (2004) discuss the computer aided tool OpenTrack, a simulation system
that was developed by the Institute for Transport Planning and System, in Switzerland.
OpenTrack is a discrete and continuous event simulation tool, it can calculate continuous
21
solution of train movement and the discrete process of a signal. One of the benefits of
OpenTrack simulation tool that Nash & Huerlimann, (2004) mentioned is its versatility as it
can be incorporated with object-oriented languages that have similar data interface structure
and can be run in different computer platforms.
Yeung & Marinov, (2017), reviewed literature on five different simulation to understand
which would be suitable in the baggage transfer in Newcastle central Station. They reviewed
RailSys, OpenTrack, SIMUL8, XpressMP and Arena. RailSys and OpenTrack are simulation
tools designed for the railways whilst the others can be used in any industry. They determined
that four of the reviewed simulation tools except for XpressMP are a discrete event
simulation tool. Discrete event simulation has an advantage of being user friendly.
Continuous event simulation tools are difficult to use in that they involve coding languages to
be compiled by a programme. Yeung & Marinov, (2017) discuss the option to use for
simulating baggage transfer by comparing the strengths and weaknesses of each simulation
tool.
Markewicz, (2013), analyses a method of calibrations in the railway industry. As with many
other studies, Markewicz, (2013) states that calibration is done to obtain an accurate
operational representation of the actual rail network and the model derived could be used for
further studies. The author argues that the shortage of a methodology for rail model
calibration has led to a lack of standardisation, ad-hoc analysis, and no base to consider
calibration concerns. A fourteen (14) step-plan is designed to have complete calibration for a
model. The fourteen-step process is based on the following, calibration set-up, calibration-
process, and finalisation. The fourteen-step process was used in a case study in the
Melbourne, Australia was conducted using RailSys simulation software.
Sipilä, (2015), proposes methods for using simulation in a more effective manner and in a
wider context. The methods deal with modelling delays that can be used in the calibration
process. The models use timetable changes with respect to allowances and buffer times. He
investigated these parameters that is the allowance and buffer time on the Western line in
Sweden to see how punctuality is affected for the high-speed rail. He determined that when
the buffer time and the allowances are increased there is a decrease in the train interaction
probability. By evaluating the buffer time and allowance Sipilä, (2015) aimed to assess the
robustness of timetables.
22
3.3 Land use and spatial planning
Mubiwa & Annegarn, (2013), discus the spatial arrangement of the Greater Johannesburg.
They start by explaining the reasoning behind the spatial arrangement of the Witwatersrand
area. As they have described, Witwatersrand was a mining town therefore all the spatial
arrangement of the city was based on the set up of mines. Later industrialisation of the city
changed the manner the city is arranged, they continue to say that the arrangement of firms
and residential areas had an influence in the manner transport system especially the railway
was designed.
23
4 . METHODOLOGY
The manner of which traffic flows in railways influences the capacity, railway capacity is a
complex phenomenon which depends on many attributes of the railway. For example, the
infrastructure, train size, heterogeneity, frequency of trains and so forth. A holistic approach
is used in the analysis of the traffic flow in the SOWETO corridor. A summarised procedure
taken for the analysis of the corridor is presented in the below text.
4.1 Data Collection
4.1.1 Concept Development of the research
The research methodology for this study is qualitative and quantitative. The quantitative data
used in the analysis was collected through interviews from stakeholders. The collected data
was used to gain insight in the operation of the train service in the SOWETO corridor. This
data in turn helped in the simulation.
4.2 Preparation and execution for model development
4.2.1 Gathering of information
This sub-section describes the methods for the collection of data on the SOWETO corridor
with New Canada Station as a place of interest. To understand fully the operation of the
SOWETO corridor, the author of the report conducted interviews by means of questionnaires.
Emails were sent to the operator Metrorail Gauteng South requesting technical information
about the nature of train operations on the SOWETO Corridors. The emails informed the
operator on the nature of the study and the reasons behind the study. The author interviewed
former employees, researchers working in transport fraternity, private companies, and other
stakeholders. Most of the interviews were done by means of digital platforms Skype,
WhatsApp, E-mails and so forth. Those that were interviewed were informed about the nature
of the study.
4.2.2 Summary of the Questionnaire for the operator
The questionnaire was designed to assist the author of the report in understanding the
operation and the infrastructure of the SOWETO corridor. The following are the parameters
24
requested by the author to the operator, which were useful in the analysis of the traffic flow
on the SOWETO corridor.
The parameters are as follows:
The time needed to set up a signal on the block section;
The time needed for the train driver to confirm signal to approach in a block;
The time needed for a train to clear a block;
The time needed for a train cover the whole length of a block;
The time needed for a railway operation system to release the signal;
The duration of a scheduled stop
The departing sequence of train from block to block
Overtaking arrangements in the train operation.
4.2.3 Timetable
The Metrorail Gauteng South designed timetable was the starting point in the analysis of rail
traffic on the corridor. New Canada Station is an intermediate station therefore it experiences
major congestion. The train traffic at the New Canada station was then isolated as it is the
station of interest for the study. Timeslots pertaining to the arrival and departures at New
Canada station were extracted from the timetable. The extracted time slots are from the
Vereeniging/Oberholzer to Johannesburg route, Naledi to Johannesburg routes and Naledi to
George Goch routes. Estimated travel times and other operational aspect were measured from
the adjacent stations, that is the Mlamlankunzi Station and Mzimhlophe Station which are
located on the South of New Canada and Longdale Station and Crown Station which are
located on the North side of New Canada Station using the main timetable in Appendix A.
25
4.3 Conceptualisation of the Model
Figure 13:Model Conceptualisation framework. Source (Robinson,2004)
This section follows the concept model as per Robinson (2004) concept framework shown in
Figure 13. The following sub-Sections describes how the inputs used in the simulation were
determined. For example, getting the main input parameters, acceleration, and deceleration of
the vehicles in the simulation. The outputs are later discussed in the Results and Analysis
Section.
4.3.1 Input parameters to the model
4.3.1.1 Acceleration and Deceleration of the trains
Newton’s equations of motion were used to determine the acceleration of the vehicles. The
equations were used because they give a simplistic method to determine the acceleration and
deceleration of the vehicles. The motion of the vehicles is modelled as follows:
(4.3.1)
(4.3.2)
Where m is the gross mass of the train vehicle and a is the acceleration of the train.
26
is the combination of the internal and external forces acting on the train as shown in
Figure 14. is the traction force of the vehicles which for the trains in the study is
determined to be between 200kN – 300kN as per the specifications of the operator. is
determined by finding the force acting against the traction force, therefore ,Equation 4.3.3
is the sum of the train resistance , gravitational force and the resistance force due to
the curves .
𝐹𝑒𝑥 = 𝐹𝑟 + 𝐹𝑔𝑟 + 𝐹𝑐 (4.3.3)
Figure 14: Forces exacted on a moving train Source: (Seimbille, 2014)
The Davis formula, Equation (4.3.4), is a widely used formula in railways for determining the
train resistance forces. There are various methods used to derive A, B, C coefficients of the
Davis formula, the derivations used in the study are from (Seimbille, 2014)
𝐹𝑟 = 𝐴 + 𝐵𝑣 + 𝐶𝑣2 (4.3.4)
A = (1.3 + 29
𝑊) is the related axle load of the vehicle.
B = 0.0045 is the state and stability of the track coefficient.
C = 0.00034𝐴𝑠
𝑊𝑛 is the aerodynamics coefficient which depends on the As the surface area of
the vehicle, W the axle load of the train and the n the number of axles on the train.
27
𝐹𝑔𝑟 = 0.001𝑚𝑔𝑖
(4.3.5)
The gravitational force is the extra force needed to lift the train up an incline or push the train
down the incline. It is defined by length of the inclined length in metres (m) divided by the
height of the incline in millimetres (mm), that is ⅈ=
𝑚
𝑚𝑚=‰
𝐹𝑐 = 0.001𝑚𝑔(𝑘
𝑟)
(4.3.6)
The force due the curvature depends on the radius and gauge of the track. The rolling
resistance is due to the friction between the wheel and the rail. k is the coefficient which
depends on the track gauge and for the 1067mm gauge is derived to 541
𝐹𝑟𝑒𝑠 = 0.09𝑊 (4.3.7)
W is the combined axle loads of the train.
Fres is the resistance force needed for the vehicle to come to a halt. This force is the braking
force which is the normal force multiplied by the friction coefficient of the wheel and the rail.
The braking Force was used to determine the deceleration of the vehicles.
4.3.1.2 Stopping and Dwell times
Dwell times are a direct consequence of the number of boarding and alighting of passengers
on the train. More people at the platforms and inside the train will increase the dwell time in
each station. According to Simpson, et al (2009), 40% of travellers from Johannesburg
Metropolitan use the train to travel to Germiston and conversely from Germiston to
Johannesburg for work travel purposes. The Vereeniging/Oberholzer to Johannesburg route
to date, has approximately 25 active Stations and the Naledi to Johannesburg route has about
14 train Station. The Naledi to George Goch route has 14 train Station and the
Vereeniging/Oberholzer to George Gogh corridor has 23 active stations. Dwell times for each
station is derived from the rate of arrival of the passengers to the stations according to the
28
travel patterns as surveyed by Simpson, et al (2009). Luethi, et al (2006) divides passengers
into two categories namely:
a) Passengers that are timetable dependent; these are daily commuters who know which
vehicle they must take for their trip. The main purpose of their trip is to go to work or
school. They normally arrive at the station close to the arrival of the vehicle. These
passengers also sometimes must connect to other transport modes before reaching
their destination.
b) Passengers that are timetable independent; these are passengers that arrive at the
stations randomly.
The study takes into consideration the passengers that are timetable dependant according to
Luethi, et al (2006), these passengers have uniform distribution when arriving at stations,
meaning that stations experience a higher rate of arrival from these passengers. Determining
the dwell time, the Wirasinghe and Szplett model developed in 1984, Table 5, is used to
understand the possible dwell times for each station on each corridor. This is done to assign
the type of vehicle to the appropriate route, meaning the vehicle with the bigger internal
capacity is designated to the route that has more passengers.
Equation
number Group description
Prediction equation
𝒕 = 𝒍+ 𝝀𝒂 + 𝜸𝒃
1 Exclusive or dominant alighting
β ≤ 0.33 𝑡 = 𝑙 + 1𝑎 + 2.4𝑏
2 Alighting and boarding
0.33 ≤ β ≤ 0.67 𝑡 = 𝑙 + 0.4𝑎 + 1.4𝑏
3 Exclusive or dominant boarding
β ≤ 0.67 𝑡 = 𝑙 + 1.4𝑎 + 1.4𝑏
β = fraction of boarders l = lost time [s] 𝜆 = time per person to alight [s/P] 𝜇 =
time per person to board [s/P] a = number of alighters [P] b = number of boarders
[P]
Table 5: Equations to estimate dwell time at the Stations. Source: (Gysin, 2020)
The Wirasinghe and Szplett is a simplistic model, as it does not encapsulate other factors that
influence the dwell time. The equations in Table 5 are used to estimate the passengers
29
boarding and alighting on the vehicles. The estimated passenger number is then used in the
Weston model Equation (4.3.8).
To be able to include other factors, the Weston model as shown in Equation (4.3.8) is used as
it can capture subtleties of alighting and boarding of trains. The model assumes that the doors
takes approximately 15 seconds to open and to close though the opening and closing time
depends on the size of the door and type of rolling stock. According to Thoreau, et al (2017)
the wider the width of the door, the longer it takes to open and to close, increasing the dwell
time by few seconds in comparison to doors with smaller widths. The widths of the doors are
used to determine the F in Equation (4.3.8), which is the peak or average door factor, and is
measured by the number of passengers (pax) passing through the door of certain width.
Thoreau, et al (2017), used three widths 1.6m ,1.7m ,1.8m and found the F factor to be
0.73,0.85 and 0.76, respectively. The 5M2A has door widths of 1.1m and the Xtrapolis has
door widths of 1.4m to determine the F factor of the vehicles from the study, extrapolation
was used, using the results from Thoreau, et al (2017).
Figure 15: Subprocess of train dwell time. Source: (Gysin, 2020)
Figure 15 is a visualisation of equation 4.3.8 showing the process that applies during dwelling
of train at Stations. The other variables used in the equation are described below.
(4.3.8)
Where:
dt = dwell time (s) at the station
A = number of alighting passengers per train
B = number of boarding passengers per train
D = number of doors
F = peak door/average door factor
S = number of seats
T = number of through passengers
30
4.3.2 Calibration and Validation of the model
Calibration
There is a shortage of model calibration method that has resulted in the lack of
standardisation, ad-hoc analysis to calibration concerns in the railway traffic planning
(Markewicz, 2013). According to Dowling, et al., (2004) to calibrate a parameter of a model,
the parameter must be categorised as:
i) Parameters that the analyst is certain about and does not wish to adjust.
ii) Parameters that the analyst is less certain about and willing to adjust.
Travel time is the parameter that is calibrated in the model, to calibrate the parameter the
mean square error (MSE) is used and should be minimised (Dowling, et al., 2004). MSE is
recommended for calibration because it is most sensitive to large volume errors. MSE is the
sum of the squared errors averaged for run repetitions with different random variable
seed,Equation 4.3.9.
𝑀𝑆𝐸 =1
𝑁 (𝑀𝑖 − 𝐹𝑖 )
2
𝑛
𝑖=1
(4.3.9)
MSE equation is a quadratic equation therefore, to convert to linear form, it is squared to give
RMSE the relative mean square error, Equation 4.3.10.
𝑅𝑀𝑆𝐸 = 1
𝑁 (𝑀𝑖 − 𝐹𝑖 )
2𝑛
𝑖=1
(4.3.10)
Where:
MSE = mean square error
RMSE = relative mean square error
F = field measurement
M = model output
N = number of data points
i = data points
Validation
A simple definition of validation is that it is a method to compare scenarios, that is, real life
observation and the model that is created. It is to validate that the model can represent the
31
situation in the field. To conduct validation a statical method of hypothesis testing is used.
The hypothesis test will evaluate the means of the scenarios, to determine whether there are
any significant differences between the means, a t-test is carried out the formulae are as
follows:
𝑇𝑡 𝐴 =1
𝑛 𝑇𝑡𝐴𝑛𝐴𝑖=1 𝑇𝑡 𝐵 =
1
𝑛 𝑇𝑡𝐵𝑛𝑇𝑡𝐵𝑖=1
(4.3.11)
𝑆𝐴2 =
1
𝑛 (𝑇𝑡𝐴 − 𝑇𝑡 𝐴 )
2
𝑛𝑇𝑡𝐴
𝑖=1
𝑆𝐵2 =
1
𝑛 (𝑇𝑡𝐵 − 𝑇𝑡 𝐵 )
2
𝑛𝑇𝑡𝐵
𝑖=1
(4.3.12)
𝐻0: µ𝐴 = µ𝐵
𝐻𝐴: µ𝐴 ≠ µ𝐵
Where:
A : is the data from the field represented as scenario A
B : is the data from the model represented as scenario B
: is the sample mean of the travel time for scenario A
: is the sample mean of the travel time for scenario B
: is the variance of the travel time for scenario A
: is the variance of the travel time for scenario B
: is the population mean of travel time for scenario A
: is the population mean of the travel time for scenario B
Ho : the nul hypothesis, that the population mean of the travel time in scenario A is equal to
the population mean of the travel time in scenario B.
HA : the alternative hypothesis, that is the population mean of the travel time for scenario A is
not equals to the population mean of the travel time for scenario B.
The rejection critetion is that the p-value of the test must not be lesser that p-crtitcal = .05.
This means the set significance level is α = 0.05. The t-Stat must be greater than t-critical,
that is < tstat.
32
5 . SIMULATION OF URBAN MOBILITY (SUMO) SOFTWARE
PACKAGE
SUMO developed by the Institute of Transportation systems at the German Aerospace
Centre.
SUMO is an open source microscopic, inter and multi modal, space-continuous and time
discrete traffic flow simulation package which has capabilities to simulate micro or macro
networks. It has traffic modelling utilities like the rail and road importer which can read
different source formats (Daniel, et al., 2012).
5.1 Generating the Network
The network provided in Sumo are real World networks generated from application and can
be generated manually in the case of new designs. The rail network nodes which are the main
feature of the network have capabilities of forming edges by joining nodes from the origin
and destination. The edges are representative of the real-World tracks, nodes can be
converted to junctions, where some junctions can be converted to traffic signalling lights
(Daniel, et al., 2012). Edges are normally unidirectional but can be formatted, each edge has
its designated maximum speed and its own width. Networks are uploaded either by a network
generator called net-generate or by a network importer called net-convert. The net-convert
can read digital spatial formats like the OpenStreetMap and an original sumo-specific XML
input file. These input files are divided into node, edge, type, connection, and traffic light
logic XML files. Figure 16 shows network preparation procedure in net-convert and net-
generate.
33
Figure 16: Generating of network in Sumo. Source (Daniel, et al., 2012)
Application to the study
Figure 17: Network for the SOWETO corridor, surrounding areas of New Canada Station. Generated
using net-convert
Figure 17 shows the uploaded section of the SOWETO corridor, with New Canada Station at
the centre of the network. The network was uploaded using OpenStreetMap.com and net-
convert. The railway lines are highlighted in the pink colour and the Station are shown by the
34
red arrows. In the simulation the shape files and polygons are removed as they made it
difficult to view the simulations when it was running. The shape files and polygons made the
network to be difficult to work with as other edges, nodes, and type, from roads, streets,
housing etc. form part of the XML-files. Net-convert was used to remove these files, it is
possible to use other methods, but they are time consuming. If the network is edited and
saved using Netedit, which comes with SUMO, the network must be reloaded again using
net-convert to avoid errors.
5.2 Generation of Routes and Vehicles
Routes are designed by combining consecutive or non-consecutive edges. For non-
consecutive edges, there must be a connection, for an example in the cases of a junction
where there are turning capabilities, in other words when a vehicle needs to change directions
Daniel, et al., 2012). Vehicles are given individual identities, departure time, and designated
routes. Each vehicle can have a different type assigned to it. The assigned type is the
characteristic of the vehicle like colour, speed, type of mode, type of infrastructure and so on.
The additional file also can create routes especially if there is a need for stopping, like in
public transport. Route files do not accommodate stopping well therefore to simulate public
transport for vehicle production and stopping areas additional file is the better route.
Application to the study
The following were inserted and edited on the network:
a) Vehicles
The first vehicle used in the simulation is the 5M2A (TYPE A) which is the vehicle
that is currently used in the SOWETO corridor. The properties of the vehicle are used
to create the simulation vehicles. The second vehicle used is the Xtrapolis which is
not currently used in the SOWETO corridor, its properties are also used in the
simulation.
b) Routes
Routes are designed as per the schedule of Vereeniging – Johannesburg – George
Goch line, Naledi – George Goch line and the Naledi – Johannesburg line. Table 6
shows the colour scheme representing the routes for the designed network. These are
35
the initial routes, they are all one-directional from South to North, South being Naledi
and Vereeniging, North being George Goch and Johannesburg.
ROUTES STATION PLATFORM
Yellow 1 Mzimhlophe1→ New Canada 3 →Longdale2
Red Mamlankunzi2→ New Canada 2 → longdale1
Blue Mzimhlophe2 → New Canada 4 → Crown 2
Table 6: Initial routes from the Metrorail timetable for the Gauteng South corridors
5.3 Simulation
SUMO has a default time step of one second, but the time step can be minimised to one
millisecond, it has maximum time bound of 49 days (Daniel, et al., 2012). The simulation is
space-continuous, the vehicle position is by the track is moving on and the distance from
beginning of the track. The speed of the system is determined by the car-following model,
there are variation of car-following model. Railway simulation has a dedicated car-following
model called rail, the car-following model determines the speed and distance of the leading
vehicle to adjust the speed of the following vehicles (Daniel, et al., 2012). SUMO has
capabilities to interact with external application by means of the socket connections. For
online interaction SUMO must be started with additional option which obtains the port
number to communicate. SUMO reads the port for upcoming connection and the triggers the
start and the end of the simulation. The user can access values from the artifacts of the
simulation which is allowed by complex interaction like the online synchronisation of traffic
lights.
Application to the Study
Started with a base scenario which follows the used timetable. The timetable used only
considers a five and half hour period from 04:00am to 9:30 am as set in the timetable. The
simulation starts few hundred meters from Mlamlankunzi station and Naledi Station. The
vehicles will stop in Mlamlankunzi and Naledi Station to factor in the dwell times in these
36
particular stations, they will then proceed to New Canada where they will stop before
proceeding to Longdale and Crown station then the vehicles will exit the simulation a couple
of meters after departing from Longdale and Crown Stations. Detector loops are placed in
strategic place on the line like the stations and few kilometres before turnouts. These detector
loops will determine the harmonic speeds, occupation, average speeds, and the number of
vehicles that have entered the loop.
5.4 Assessment of different Railway Simulation Tools
Computer aided tools are an important tool for evaluating different improvement strategies in
the railway sector. Some simulation tools allow both macroscopic and microscopic
simulations like OpenTrack and some allow just one, either micro simulation an example of
this is RailSys or macroscopic like NEMO. The commercial railway simulation software
package is normally based on two main components (Pouryousef & Lautala, 2015).
• Train movement simulation
• Train dispatching simulation
Train movement simulation calculates the train speed along the track by using the train
resistance formula and train traction power (Pouryousef & Lautala, 2015). The dispatching
simulation imitates the behaviour of the dispatcher. Pouryousef &Lautala, (2015) further
describe simulation softwares to be timetable and non-timetable based where timetable based
simualtion softwares are applied in railways that operate based on the improvised operation
pattern without initial timetable. Non timetable based simualtion softwares are based on the
initial timetable of trains and uses software tools to improve the timetable as much as
possible (Pouryousef & Lautala, 2015).
Simulation tools also offer a cheaper method of evaluating the planning of the railways.
Understanding capacity as it is not intuitively obvious for an example a railway line that have
minimum train service can have capacity problems (Nash & Huerlimann, 2004). The setup of
the infrastructure is one of the factors that influence the capacity of the railway network, the
technology of the infrastructure also plays a tremendous role in the railway traffic planning.
Railway simulation tools also have their limitations. Example of this would be validation of
programmes or simulation are area specific and so on.
37
5.4.1 OpenTrack
Figure 18: Main Elements of OpenTrack. Source: (Nash & Huerlimann, 2004)
OpenTrack can be run on different computer platform and incorporates the benefit of object-
oriented programming language with a common interface. The latest railway simulation
programs are object oriented and XML based (Nash & Huerlimann, 2004). The objected
oriented programming is created by combining modules of a standard code with application
specific algorithm. OpenTrack works in conjunction with RailML an open source XML
based language. RailML is simple to use and can be used to transfer data between
programmes.
Data is put in the user-friendly graphical interface see Figure 18, then it will be process to
RailML creating XML files for the rolling stock and infrastructure. OpenTrack will then be
modified to enable rolling stock and infrastructure data to be directly imported from RailML
data files to the output.
38
5.4.2 RailSys
Figure 19: The process used for multiple timetable analysis with simulation of nominal and operational
timetables Source: (Sipilä, 2015)
RailSys is on operation management software system which integrates timetable construction
and infrastructure management with microscopic simulation. It is one of the most common
timetabled-based simulation software in Europe (Pouryousef, et al., 2015), (Sipilä, 2015),
(Yeung & Marinov, 2017). The routing algorithm installed on RailSys is heuristic-based and
not optimization based and there are thus certain limitations to the effectiveness of the
dispatching measures (Sipilä, 2015). Therefore, routing is not designed to optimize a
perturbed scheduled timetable. This applies in cases where many lines running in single-track
or bidirectional operation are located within the area under review (RailSys, 2014). Sipilä
(2015), gave a heuristic process of RailSys this can be seen in Figure 19. The process starts
with the generation of the infrastructure, vehicles then assigning the vehicles to the routes
generated, this will be followed by a simulation process for the nominal timetable which then
passes to post processing and assigned to statistical analysis.
39
5.5 Comparison of the simulation tools
The attributes of each simulation tool are summarised in Table 7, which shows the
comparison that the author made for SUMO, OpenTrack and RailSys.
SIMULATION
TOOL
SUMO
OpenTrack RailSys
DEVELOPER
Institute of
Transportation systems
at the German
Aerospace Centre
(Germany)
Institute for Transport
Planning and System,
(Switzerland)
Rail Management
Consultants GmbH
(RMCon) (Germany)
CAPABILITIES
- railway network’s
infrastructure
- Analysing the
capacity of lines
- Calculation of
minimum headway
- Rolling stock
- Running time
- designing various
signalling systems
- Extraction of real
networks
- railway network’s
infrastructure
- Analysing the
capacity of lines and
stations
- Calculation of
minimum headway
- Rolling stock
- Running time
- Timetable
Construction
- designing various
signalling systems
- Analysing the effects
of system failures
- Calculation of power
and energy
consumption of train
services
-Infrastructure manager
-Timetable construction
-Capacity Management
- Track Possession
planning
-Simulation Manager
-Rolling stock circulation
planning
- Graphical Timetable
-Platform and track
occupation diagrams
- Graphical network
interface
-Delay statistics
40
SIMULATION
CRITERIA Non-timetable based
Timetable based
simulation software
Timetable based
simulation software
OUTPUT
CAPACITY
Statistics,
Occupation
Statistics, occupation
time, timetable
Train graphs
Delay statistics,
infrastructure occupation
time, optimized timetable
TYPE OF
SIMULATION
Microsimulation /
Macrosimulation
Microsimulation /
Macrosimulation Microsimulation
HANDLE Continuous (not user-
friendly)
Discrete event (user
friendly)
Discrete event (user
friendly)
Table 7: Comparison characteristics of the SUMO, RailSys, OpenTrack. Source: (OpenTrack.ltd, n.d.),
(Daniel, et al., 2012), (Lautala & Pouryousef, 2013)
41
6 . RESULTS AND ANALYSIS
Two plans of train traffic planning are evaluated for the SOWETO corridor. The first plan
uses the timetable as designed by the operator for the train operation of the SOWETO
corridor which has varying headways, the second plan evaluates capacity when the headway
has similar time interval for all routes. The impact of an additional route(s) is also assessed,
see Figure 20.
There are many factors that influences the congestion experienced on the railway network,
for example the dwell time as it highly depends on the number of passengers boarding and
alighting the train. The dwell time delay can have a ripple effect on the network, especially
when the block length/distance system is used. Another cause of delay in the train network
would be the malfunction of the infrastructure for example the signalling equipment or the
rolling stock, these cause unpredictable delays, which is not considered in this report.
Figure 20: Flow chart of the methodology for the analysis
42
TYPE A (5M2A) and TYPE B (Xtrapolis) vehicles are individually assessed in the first plan,
then they are assessed when both are operating in the network. Similarly, with the second
plan an assessment of TYPE A vehicles is firstly done, followed by TYPE B vehicles then
when both vehicles are in the network. The third plan is only considered after the results of
the previous two plans.
The specifications see Table 8 and Table 9 of each vehicles are used in the simulation. These
specifications are found using methods as described in Section 4 of the report. The
specifications include:
a) door size, lateral and vertical gaps between door and platform number of passengers
alighting and boarding. These specifications influence the dwell time on stations
b) Acceleration, deceleration, and speed. These specifications influence the runtime a
vehicle runs on a block
Each vehicle type has a dedicated line or route to which it does not share with any other rail
traffic.
Detector loops are placed in several strategic positions in several blocks along each track and
at the stations to collect information on the vehicles moving on the route at a specific time.
The detector loops are used gather information about the vehicle passing through the detector
loop. The information retrieved from the detector loops is used to do the analysis of all the
scenarios like the occupancy, flow, speed, and number of vehicles passing through the
detector loop of the train in a section.
Vehicles Specification Value
TYPE A → 5M2A
acceleration 0.2m/s2
deceleration 0.88m/s2
length 350 m
Max Speed 25 m/s
Door width 1.1m
Number of Doors 32
Minimum Gap (overlapping length) 110m
Table 8: Type A vehicle specification used in the simulation
43
Table 8 and Table 9 show the specifications that were used as vehicle variables in the
simulation. The acceleration and deceleration are the results from using the formulae in sub-
Section 4.3.1 they depend on the type of vehicle, track geometry, tractions forces, retarding
forces, and the weight of the vehicle. The length is the longest length of vehicle that could fit
to the platform of the station. The max speed is the maximum running speed designed for
each type of vehicle that is the 5M2A and the Xtrapolis and the width of the door is the total
opening of the door allowing passengers to embark and disembark.
Vehicles Specification Value
TYPE B →Xtrapolis
acceleration 0.36 m/s2
deceleration 0.882 m/s2
length 350 m
Max Speed 34 m/s
Door width 1.4m
Number of Doors 54
Minimum Gap (overlapping length 110m
Table 9: Type B vehicle specification used in the simulation
The overlapping length is the set distance that is allowed for a vehicle to pass a stopping
signal, 110m is set by Metrorail. In the simulation the overlapping length is designated as the
minimum following gap. The minimum gap is the distance that the rear vehicle should keep
to avoid colliding with the front vehicle. It was set at 110m to adhere to the standard
overlapping length. Door width are the standardised width for each vehicle type, TYPE A has
1.1m width and TYPE B has 1.4m width and the number of doors also differ with vehicle
type, TYPE A has 32 doors whilst TYPE B has 54 doors.
44
Number ROUTES STATION PLATFORM
1 Yellow 1 Mzimhlophe1→ New Canada 3 →Longdale2
2 Red Mamlankunzi2→ New Canada 2 → longdale1
3 Blue Mzimhlophe2 → New Canada 4 → Crown 2
4 Magenta Mlamlankunzi1 → New Canada 1 → Longdale1
5 Green Mlamlankunzi2 → New Canada 3 → Crown 1
6 Yellow2 Mzimhlophe1 → New Canada 2 → Longdale1
Table 10: Extended routes from the Metrorail timetable for the Gauteng South corridors and additional
route analysed
Table 10 shows the added routes to the initial routes as explained in Table 6 of Section 4. The
routes added are Magenta, Green and Yellow2 routes. The green and magenta routes are
added to increase capacity in the Vereeniging – Johannesburg route to mitigate the
overcrowding and delays experienced on the route as it will be described in the below sub-
Sections. The Yellow 2 route is in the same direction as Yellow1 because of conflict between
Yellow1 route and the Green route. Yellow2 was designed to operate on a different track
hence Yellow 2 stops at platform 2 in New Canada Station as shown in the Station Platform
column in row 6 in Table 10. The magenta also has the same direction as the red route, due to
the red route experiencing conflict with the Yellow 1 and/or Yellow 2 route, the red route
vehicles were removed and replaced with the magenta route vehicles but the magenta
vehicles will move on different tracks with lesser conflict. Therefore, in cases where the red
route is operational, the magenta route does not operate and in cases where the Yellow 1
route is operational Yellow 2 does not operate. At most 4 routes can operate simultaneously
with no or minimum conflict.
6.1 Input parameters
The spatial arrangement in Johannesburg Metropolitan for the railway network, is arranged in
a manner that residential areas are centralised on the South and firms centralised on the North
and on the East side of Johannesburg. This leads to fewer people alighting and more boarding
the vehicle at Stations before New Canada during morning peak periods and conversely
during the evening peak period. Equation number 3 from Table 5 in sub-Section 4.3.1 of the
45
report was used to determine the dwell time for stations before New Canada. This equation
assisted in the estimation of the dwell times when more people board than alight the train
and conversely Equation 1 from Table 5 in sub-Section 4.3.1 was used in estimating dwell
times for stations after New Canada as there were more passengers alighting than boarding
Table 11 and Table 12 show the estimated number of passengers that would arrive at the
stations per during the morning peak.
To estimate the possible number of passengers the population around the stations is
considered. According to JDA, (2010) and Simpson, et al (2009) approximately 80 percent of
the population in Gauteng South uses public transport and off the 80 percent, 40 percent
choose rail as a mode of transport. Column 2 and column 3 in Table 11 and Table 12 are the
80% of the population surrounding the respective Station and the number of daily passengers
which are 40% of the population. The number of passengers is then divided to the possible
number of passengers during peak time of 5 hours which the results are seen in column 4.
15min 5min 15min 5min 15min 5min
STATION 80% POPULATIONPASSENGERS PASS/HOUR PASS/15MIN PASS/5MIN
Naledi 48612 19445 3889 972 324 292 97 681 227 1363 456
Merafe 6939 2776 555 139 46 42 14 97 32 196 67
Inhlazane 71592 28637 5727 1432 477 430 143 1002 334 2007 670
Ikhwezi 95789 38316 7663 1916 639 575 192 1341 447 2684 896
Dube 41742 16697 3339 835 278 250 83 584 195 1171 392
Phefeni 35856 14342 2868 717 239 215 72 502 167 1006 337
Phomolong 19615 7846 1569 392 131 118 39 275 92 551 185
Mzimhlophe 35856 14342 2868 717 239 215 72 502 167 1006 337
NEW CANADA 35760 14304 2861 715 238 215 72 501 167 1003 336
Total New Canada 489702 156705 31341 7835 2612 2351 784 5485 1828 10971 3658
headway-->
a--> Alighting b--> Boarding dwell time
Table 11: Estimated number of passengers arriving at each station on the Naledi-New Canada line.
Source www.statssa.gov.za
Column 5 and column 6 are the estimated arrival rate of passenger in 5 minutes and 15
minutes, respectively, which are the possible headways for the corridor. The Alighting and
Boarding columns are the distribution of passengers in relation to the pattern of patronage in
46
SOWETO corridor. As aforementioned, during peak periods the SOWETO corridor
experiences more passengers boarding than alighting. Therefore, passengers are distributed to
have 70% boarding and 30% alighting. The last column are estimated number of passengers
alighting and boarding at 5 minutes and 15 minutes headways using the Equation 1 from
Table 5 in sub-Section 4.3.1
15min 5min 15min 5min 15min 5min
STATION 80% POPULATIONPASSENGERSPASS/HOUR PASS/15MIN PASS/5MIN
Vereeniging 79830 31932 6386 1597 532 479 160 1118 373 2237 747
Houtheuwel 20062 8025 1605 401 134 120 40 281 94 564 189
Kwaggastroom 44404 17762 3552 888 296 266 89 622 207 1245 416
Eatonside 49131 19652 3930 983 328 295 98 688 229 1378 461
Residensia 18802 7521 1504 376 125 113 38 263 88 528 177
Stretford 61414 24565 4913 1228 409 368 123 860 287 1722 575
Grasmere 38301 15320 3064 766 255 230 77 536 179 1074 359
Midannadale 57452 22981 4596 1149 383 345 115 804 268 1611 538
Anglers 29688 11875 2375 594 198 178 59 416 139 833 279
Lawley 26509 10604 2121 530 177 159 53 371 124 744 249
Lenz 71771 28708 5742 1435 478 431 144 1005 335 2012 672
Midway 26530 10612 2122 531 177 159 53 371 124 745 250
Tshiawelo 40428 16171 3234 809 270 243 81 566 189 1134 379
Kliptown 26907 10763 2153 538 179 161 54 377 126 755 253
Nancefield 42195 16878 3376 844 281 253 84 591 197 1183 396
Orlando 54568 21827 4365 1091 364 327 109 764 255 1530 511
Mlamlankunzi 9692 3877 775 194 65 58 19 136 45 273 92
NEW CANADA 35760 14304 2861 715 238 215 72 501 167 1003 336
Total New
Canada916805 293377.6 58676 14669 4890 4401 1467 10268 3423 20538 6847
Headway-->
a--> Alighting b--> Boarding dwell time
Table 12: Estimated number of passengers arriving at each station in the Ver-New Canada line. Source:
www.statssa.gov.za
47
6.2 Calibration and Validation
The train type currently used by the Metrorail is the TYPE A, which is the 5M2A. Therefore,
for calibration and validation purposes TYPE A was also used in the simulation. Three routes
were used in the simulations Red route, Yellow 1 route and the Blue routes, these routes
represent the Vereeniging-Johannesburg line, Naledi – Johannesburg line and the Naledi –
George Goch line, respectively. The calibration and validation methodologies are discussed
in sub-Section 4.3.2. The parameter selected to be calibrated is the travel time from Station to
Station, this is because the observed data from the field concentrated on the travel time of the
vehicles. 2% of the field data in the Red route, 1.5% field data of the Yellow route and 1.5%
field data of the Blue route were not included in the evaluation, these were data points that
were higher than 30 minutes. These longer travel times were the cause of the malfunction of
either the train or the wayside track equipment, as stated before, these delays do not form part
of the scope.
1 2 3 4 5 6 7 8 9 10 11
2 MEAN S.D MSE RMSE MEAN S.D MEAN S.D MSE RMSE
Yellow 1== Naledi-
Johanneburg3 231,4 25,3 178901 21,1 321,1 416,0 315,5 52,0 6627 4,1
Red == Vereeniging
- Johannesburg4 230,9 23,2 24278 155,8 378,8 745,3 358,8 119,3 9569 97,8
Blue == Naledi -
George Goch5 231,2 22,3 128572 12,0 302,5 323,3 363,2 46,3 132694 12,2
Blue == Naledi -
George Goch(2)6 231,2 22,3 128572 12,0 302,5 323,3 356,9 41,2 129045 12,1
Pre-calibration (s) Observations (s) Post-Calibration (s)
CALIBRATION
Table 13:Values retrieved before and after calibration from the observed data
Table 13 are the calibration results, the dwell time was the parameter that was calibrated in
the model as it is the has the most impact on the travel time. In the first case, pre-calibration
the dwell time was set to 40s, which is the stopping time at the stations that is used in
preparation of the timetables. The dwell time for post-calibration is found using Equation
4.3.8 in sub-Section 4.3.1, this dwell time was an improvement from the 40s prescribed dwell
time. In the post-calibrated dwell time by the arrival rate of the passengers, which was
randomised. The pre-calibration results yielded a bigger RMSE in the Red and the Yellow
48
routes 155,8s and 21,1s, respectively. In the Post-calibration model, the RMSE was
minimised to 98s for the Red route and 4.1s for the Yellow route, the blue remained constant
in both models. Because there is no standardised manner for calibration in the railways lines a
validation analysis of the models was conducted.
Pre-Calibration Post-Calibration
1 2 3 4 5 6 7
2 t-test p-value t-test p-value t-Stat > t-Stat-critical
p-value < p-value critical
t-Stat > t-Stat-critical
p-value < p-value critical
critical values 3 1,96 0,05 1,96 0,05
Yellow 1 Naledi-
Johanneburg4 4,3 2,0E-05 0,3 0,8 Reject Fail to Reject
Red == Vereeniging -
Johannesburg5 8,8 3,3E-18 1,2 0,2 Reject Fail to Reject
Blue == Naledi -
George Goch6 5,9 4,2E-09 5,0 5,9E-07 Reject Reject
Blue == Naledi -
George Goch(2)7 5,9 4,2E-09 1,7 0,08 Reject Fail to Reject
VALIDATION
Pre-calibration (s) Post-Calibration (s)
Rejection Criterion
Table 14: Values retrieved for validation of the model
Though the Blue route has constant RMSE in both models it did not do well in the validation
of the models. Therefore, the blue route was re-calibrated, which resulted in the second blue
row(7) in Table 14, the travel times in the simulation were higher than that of the field data.
To mitigate the problem the dwell time was adjusted by rearranging the passenger for both
Yellow and Blue route from 50% each, to 60% passenger for the Yellow route and 40% for
the Blue route. In Table 14 the p-value of the first Blue route simulation try was small, 5.09E-
07 (row 6, coulumn5) and the t-Stat was greater than t-critical. In hypothesis testing there is a
possibility of two type of errors occurring, that is:
i) TYPE I errors, these errors occur when the null hypothesis is rejected when it should
not have been rejected, model builder’s risk.
ii) TYPE II errors, these errors occur when the null hypothesis is not rejected when it
should be rejected, model user’s risk (Sargent, 2010).
The determination of which statistical error is undesirable depends on the experimental
design before data collection. Though, TYPE II errors are deemed to be better than TYPE I
and the probability of making TYPE I errors is set to be between 1% and 10%. Hence the
Blue route was re-calibrated. Calibration was done in Plan 1 and used on the rest of the Plans.
49
6.3 Simulation results
In the varying headways plan, Plan 1, the red route, yellow1 route and the blue route were the
routes used as they are the initial routes that represent the real route choice. The plan is
initiated by evaluating the TYPE A vehicles individually first, followed by the evaluation of
TYPE B vehicle then the utilisation of both TYPE A and TYPE B vehicles in the network.
The first step had TYPE A vehicle used in the simulation for all three routes and
implementing different the dwell times. Dwell times are separated into two categories:
i) The fixed dwell time, this dwell time is the prescribed dwell time that operator uses
when designing the timetables. This dwell time is normally between 30s to 40s. 40s
dwell time was considered in the simulations.
ii) The random dwell time is passenger dependent stopping time interval. This dwell
time is determined by using the methods described in Section 4 of the report. It
considers factors like the amount of people at the platforms at a given period and the
mechanics of embarking and disembarking the train.
On a technical perspective TYPE A vehicles seem to be the inferior vehicle than TYPE B as
TYPE A vehicles have lower acceleration and speed abilities in comparison to TYPE B. The
second step had TYPE B vehicles used in the simulation with the same routes used by TYPE
A vehicles and the dwell time categorised similarly as with TYPE A vehicles, that is random
dwell times and fixed dwell times.
The last step uses both TYPE A and TYPE B vehicles simultaneously in the railway
network. The distribution of vehicle types according to the routes was dependent on the
results of the first step and the second step. The route that experiences delays is the one that
is give priority. In the simulation the delays are seen by the number of vehicles teleported,
this means that the vehicle had to wait for a long time more than 300s, then it is moved to
another edge or is taken out of the simulation. Table 31 and Table 32 in Appendix C, are
examples of vehicles teleported.
50
6.3.1 Varying headways timetable- Plan 1
Table 15 shows the timeslots of vehicles that are at the New Canada Station, in a 5 hour
period there are 20 trains that pass through New Canada from Naledi Station en-route to
Johannesburg, 15 trains from Vereeniging/Oberholzer to Johannesburg and 15 trains from
Naledi heading towards George Goch. There are three routes utilised in this section the red
route which goes from Mlamlankuzi Station to Johannesburg Park Station, the yellow route
which goes from Naledi Station to Johannesburg Parks Station and the blue route which goes
from Naledi Station to George Goch Station. The slots that are highlighted in green in Table
15 are for trains that do not stop at every stations and slots that are red trains that start at
Stations that are in Lenz Station or Midway Station on the Vereeniging - New Canada –
Johannesburg corridor. The position of Lenz and Midway Stations can be seen in Table 12
NAL-JHB 04:20 04:25 04:50 04:58 05:15 05:32
Ver-JHB 04:17 04:57 05:17 05:17 05:47 05:57
NAL- GG 04:33 04:47 05:04 05:27 05:39 05:44
NAL-JHB 06:00 06:05 06:20 06:35 06:35 06:40 07:00 07:20
Ver-JHB 06:17 06:17 06:42 06:57 07:12 07:13
NAL- GG 06:14 06:24 06:44 06:49 07:22
NAL-JHB 07:30 07:50 07:55 08:10 08:30 08:54 09:00
Ver-JHB 07:29 07:47 08:07
NAL- GG 07:24 07:39 08:24 08:30
DURATION 04:00 - 05:57
DURATION 06:00 - 07:22
DURATION 07:24 - 09:00
Table 15: times when the train are at the New Canada Station
For the first Plan the dwell time was set to 40s and the travel time is 15 mins from Stations
before and after New Canada Station which is the time used by timetable planners for
commuter rail at Metrorail. The routes used are the ones currently utilised in the SOWETO
corridor, however, it possible to create more than as this section of SOWETO corridor has
quadruple tracks but also has many switches which may hinder capacity and the free flow of
rail traffic.
51
6.3.1.1 TYPE A only on the network
Random dwellT fixed dwellT Random dwellT fixed dwellT Random dwellT fixed dwellT
mzimhlophe1 3.84 3.73 53.21 10.47 1.49 2.82
mzimhlophe2 2.67 2.67 26.94 7.49 0.51 2.03
mlamlankunzi1 0.00 0.00 0.00 0.00 0.00 0.00
mlamlankunzi2 1.55 2.35 54.00 6.86 0.09 1.68
newcanada1 0.00 0.00 0.00 0.00 0.01 0.00
newcanada2 2.13 2.35 73.91 6.59 0.14 1.89
newcanada3 3.57 3.84 53.29 12.89 0.56 2.55
newcanada4 2.77 2.67 28.16 7.83 0.58 1.68
longdale1 1.23 2.45 44.47 6.75 0.12 2.00
longdale2 3.68 3.84 51.98 10.70 0.55 2.28
crown1 0.00 0.00 0.00 0.00 0.00 0.00
crown2 0.00 0.00 27.91 7.89 0.03 0.00
SPEED(m/s)
STATION
FLOW(veh/h) OCCUPANCY(%)
Table 16: Results for flow, track capacity, and the average speed at each platform for considered Stations
for TYPE A vehicle
Table 16 shows the results found when TYPE A vehicles were inserted into the simulated
network. The results in Table 16 are result outputs retrieved from the induction loops. The
induction loops were placed in each Station on the track adjacent to each platform in the
direction of the North bound traffic. The first column in Table 16 is the platforms in each
Station, Mzimhlophe Station, Mlamlankunzi Station, Longdale Station and Crown Station
each have two platforms dedicated for the North bound traffic for example Mzimhlophe1 is
platform 1 in Mzimhlophe Station. New Canada Station has four platforms dedicated to the
North and East bound traffic.
In Plan 1 as the other plans the flow, occupancy and speed were evaluated. These variables
describe interaction of the vehicles in the network. They give an understanding of how fast or
slow the movement in the simulated network is. The flow which characterised by the number
of cars that able to pass the loop in an hour, it shows whether the system is free flowing or if
there is some stalling. The speed which is the average speed of the vehicles operating on the
network, this variable shows how fast the vehicles are moving. The Speed shown in the Table
16 is the average speed of the vehicles approaching the stations, it is not indicative of the
average speed of the whole network. The occupancy at the Stations is the percentage vehicles
TRACK OCCUPANCY (%) AVERAGE SPEED (m/s)
52
of occupying the area during the period of detection by the induction loop. The occupancy is
interpreted in conjunction with the flow and the average speed vehicle.
Figure 21:Average vehicle flow at the statins for both random and fixed dwell times
Figure 21 is the visual interpretation of the flow in Table 16 for each platform at every
Station in the simulation network. Where the blue bars represent the random dwell time at
each Station and the orange bars represent the fixed dwell time. Since there are three routes
evaluated in Plan 1 some platforms at some Stations will be left empty like platform 1 in
Mlamlankunzi, platform 1 in New Canada Station and platform 1 at Crown Station. There is
no traffic flowing on these platforms for this plan.
For fix dwell times the flow in all concerned station the is above 2 per hour on average.
Mzimhlophe1, New Canada3 and Longdale 2 are platforms located in the Yellow route have
the highest flow of vehicles both in fixed and random dwell times. The blue route also which
includes Mzimhlophe2, New Canada 4 and Crown2 platform shows a similar pattern like the
Yellow route in both the fixed and random dwell times. There are some differences in the
Red route which follows the Mlamlankunzi2, New Canada2 and Longdale1, in fixed dwell
time there is high number of vehicles detected in comparison to random dwell times. This
difference in the flow in the Red route is attributed to many factors like the headway of the
vehicles, the number of passengers arriving at the platform. The Yellow and the Blue routes
have an advantage of sharing passengers, shorter headways and small amount of stations
FLOW ON TRACKS (veh/h)
53
which means on average the total dwell times fixed or random are shorter compared to the
Red route.
Figure 22:Average vehicle occupancy at the statins for both random and fixed dwell times
Figure 22 describes the amount of time the platforms in each Station have been occupied. For
fix dwell time the platforms are barely occupied in the simulation. The New Canada 3
platform has the highest percentage of occupancy of 12% this means that the vehicles during
the simulation did not experience longer dwell time, which is not surprising as the dwell time
was fixed. For the random dwell time New Canada2 experienced the highest occupancy
approximately 74% of the time platform 2 in New Canada had vehicles. Mzimhlophe1, New
Canada3, Longdale2 and Mlamlankunzi2 all had occupancy of above 50%. The random
dwell time scenario experienced more occupancy than the fixed dwell time, this means that
vehicles were stopping for an exceedingly long time on the platforms during random dwell
times.
OCCUPANCY ON TRACKS (%)
54
Figure 23:Average vehicle speed at the stations for both random and fixed dwell times
Mzimhlophe1, New Canada3 and Longdale2 are more consistence which shows that there are
lesser dwelling times on these platforms this is shown by consistent numbers between the
flow, occupancy, and the speed. Figure 23 shows that the average speed in the three platforms
mentioned, are higher than the most during random dwelling at these platforms. The average
speed at Mlamlankunzi1, New Canada2 and Longdale1 in Figure 23is at the lowest during
random dwelling times this is consistent with findings of the flow in Figure 19 but a
contradictory to the occupancy. This contradiction implies that the 74% of occupancy in New
Canada2 and the above 50% occupancy in Mlamlankunzi1 and Longdale1 is from none
moving or very slow-moving vehicles.
55
6.3.1.2 TYPE B only on the network
In the beginning of this section, Section 6.2 it was mentioned that the second step of the
analysis is to also evaluate TYPE B vehicles individually. When the TYPE B vehicles were
evaluated, it was found TYPE B vehicles exhibits a similar pattern to that of Table 16 when
the flow, occupancy and the speed were evaluated.
fixed dwellT Random dwellT fixed dwellT Random dwellT fixed dwellT Random dwellT
mzimhlophe1 3.73 3.73 8.94 32.71 3.14 1.00
mzimhlophe2 2.67 2.67 6.39 19.21 2.22 0.74
mlamlankunzi1 0.00 0.00 0.00 0.00 0.00 0.00
mlamlankunzi2 2.35 2.29 5.76 45.51 2.01 0.28
newcanada1 0.00 0.00 0.00 0.00 0.00 0.00
newcanada2 2.35 2.45 5.65 50.63 2.21 0.28
newcanada3 3.73 3.84 10.26 34.07 3.26 0.77
newcanada4 2.67 2.77 6.66 19.59 2.13 0.78
longdale1 2.35 2.13 5.67 44.29 2.20 0.20
longdale2 3.84 3.57 9.24 32.51 3.45 0.90
crown1 0.00 0.00 0.00 0.00 0.00 0.00
crown2 2.67 2.77 6.56 20.01 2.00 0.78
FLOW(veh/h) OCCUPANCY(%) SPEED(m/s)
STATION
Table 17: Results for flow, capacity, and the speed at each platform for considered Stations for TYPE B
vehicle
What was found to be different are the quantity of the flow, occupancy, and the speed. If the
Red route is considered as it had high occupancy with low Flow and Speed in all platforms
that is the Mlamlankunzi2, New Canada2 and Longdale2 there is a slight improvement in
comparison to TYPE A vehicles. The occupancy in the TYPE B vehicles at New Canada2 is
at 50.63% down from 73,91%, Mlamlankunzi2 down to 45.51% from 54%. The speed at
New Canada2 increased from 0.14 to 0.28 (m/s) and at Mlamlankuzi2 the average speed
increased from 0.09m/s to 0.28 m/s and the flow increased from 2.13 veh/h to 2.35 veh/h.
The pattern is similar for all active platforms in the simulation there is slight decrease in the
occupancy, but the flow and speed show an increase in their values. This slight change is
attributed to the specification of the TYPE B vehicle as it is designed to have higher
acceleration and speed. TYPE B also has more doors than TYPE A, 54 doors in comparison
TRACK OCCUPANCY (%) AVERAGE SPEED (m/s)
56
to 32 doors. The doors of TYPE B vehicle are also wider by 0.3m than that of TYPE A, the
total internal capacity TYPE B is also greater than that of TYPE A. All these factors
contributed to the major differences seen between Table 16 and Table 17.
Table 18:Departure times for TYPE A and TYPE B vehicles for random dwell times
There is also a significant difference in the number of vehicles that entered the network and
departed from each platform on the Red route. Table 18 shows the differences between TYPE
A vehicles and TYPE B vehicles when random dwell time was applied. If a train is tracked
by its number, it is visible that most of the TYPE A trains are teleported to the next Station as
they must wait for a long time to enter the route or to enter the Station. For example,
TRAIN9028, TRAIN9032 in TYPE A is only seen in New Canada2 and not in other stations
and in New Canada2 they are not placed correctly, example is TRAIN9032 came before
TRAIN9031 which is sequentially incorrect. For TYPE B vehicles the trains can follow the
57
set sequence except for when the simulation period is at TRAIN9032 this mean TRAIN9031
stops in Mlamlankunzi Station for an exceedingly long time. This means there were
passengers on the platform that are more than the capacity of both TYPE A and TYPE B
vehicles in Mlamlankunzi2 according to the simulation results.
6.3.1.3 Combination of TYPE A and TYPE B vehicles on the network
The random dwelling time is used in this section as it demonstrated that it causes delays
during the separate evaluations TYPE A and TYPE B vehicles. Evaluating TYPE A and
TYPE B vehicles separately showed that the Red route experienced heavy delays and
congestion. Overcrowding is also an issue as seen in TRAIN9032 teleported in both TYPES
when it was supposed to enter Mlamlankunzi station due to TRAIN9031 stopping for a long
time on the Platform. The positive that comes from the evaluations is that it showed that
TYPE B vehicles are better suited to handle the passengers from the Red route. Though
TYPE B experienced delays, it was not as bad as that of TYPE A vehicles on the Red route.
TYPE A vehicles seem to be sufficient for the Blue route and the Yellow route as the route
did not experience any delays, the results are shown in Appendix B. Therefore, it is
beneficial to use TYPE B vehicles on the Red route and the TYPE A vehicle remain with the
Yellow route and the Blue route with random dwell time. Table 19 below shows the results
from the combination of TYPE A and TYPE B vehicles inserted to the network. The table
also has previous results from TYPE A and TYPE B when they were evaluated individually.
58
TYPE A TYPE B Combination TYPE A TYPE B Combination TYPE A TYPE B Combination
mzimhlophe1 3.84 3.73 3.52 53.21 32.71 44.09 1.49 1.00 0.58
mzimhlophe2 2.67 2.67 2.51 26.94 19.21 25.15 0.51 0.74 0.55
mlamlankunzi1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
mlamlankunzi2 1.55 2.29 2.29 54.00 45.51 45.05 0.09 0.28 0.28
newcanada1 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00
newcanada2 2.13 2.45 2.45 73.91 50.63 50.27 0.14 0.28 0.26
newcanada3 3.57 3.84 3.57 53.29 34.07 48.60 0.56 0.77 0.67
newcanada4 2.77 2.77 2.77 28.16 19.59 28.49 0.58 0.78 0.59
longdale1 1.23 2.13 2.19 44.47 44.29 47.10 0.12 0.20 0.25
longdale2 3.68 3.57 3.57 51.98 32.51 46.17 0.55 0.90 0.57
crown1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
crown2 0.00 2.77 0.00 27.91 20.01 28.25 0.03 0.78 0.00
F
p-value
F-crit
α÷3=
TYPE A
TYPE A
TYPE B
0.20
0.003
TYPE B
P(T<=t) two tail
P(T<=t) two tail
0.10
FALSE
TRUE
FALSE
Combination
1.68
0.19
3.14
SPEED(m/s)
stats analysis α = 0.05 Ho : µa = µb = µ c Ha : ALOI
0.0167
Pos hoc
Analysis with
Bonferroni
correction
Combination
4.70
0.01
3.14
2.22
0.12
3.14
FLOW(veh/h) OCCUPANCY(%)
STATION
Table 19: flow, capacity, and the speed at each platform for considered Stations for ALL vehicle types
So far there is an understanding that TYPE B vehicles perform better than TYPE A vehicles
especially in the Red route which has more passengers. Table 19 shows that the average
network speed in TYPE B vehicles is larger than both combination and TYPE A. When doing
an ANOVA statistical analysis of the total data received, there were no significant differences
between the average flows and the average speeds for TYPE A vehicles, TYPE B or when
the combination of both vehicle types when inserted to the network individually. The
occupancy shows that there are some differences in TYPE A, TYPE B and the combination
of both vehicles. This is seen by the p-value (0.01) of the occupancy is lesser than the
significant p-value (0.05) and the F critical (3.14) smaller than the F value (4.70). Therefore,
there is significant difference in how the vehicle types in the system occupy the tracks on the
TRACK OCCUPANCY (%) AVERAGE SPEED (m/s)
59
corridor for TYPE A, TYPE B and the combined vehicles. A post hoc analysis Student (t-
test) was implemented with Bonferroni correction. Bonferroni correction is a simple
correction of the analysis, it takes the significant p-value and divides it by the number of data
groups evaluated. The test yielded results that showed that the significant difference in
occupancy is between TYPE A and TYPE B vehicles as shown in the last rows of Table 19.
This means that the manner TYPE B occupies the routes is different from the manner TYPE
A occupies the routes. It could be due to the that TYPE B vehicle are able to load the
passengers much faster than the TYPE A vehicle due to TYPE B vehicle’s specifications.
Figure 24: Visualisation of Table 16 of the Flow of vehicles for TYPE A, TYPE B and Combination (A
and B)
Figure 24 is the visualisation of the average flow for each station during the evaluation of the
vehicle types. The Yellow route which has Mzimhlophe1, New Canada3 and Longdale2 has
higher flows with all vehicle types as it was seen in previous evaluations of the vehicle types.
There is some visible flow from the TYPE B vehicle at Crown2 platform but there are no
visible flows for TYPE A and combination of the two types. TYPE B and the combination of
vehicles have a similar flow for all the platforms Stations, in some Stations the TYPE B
vehicles had significant higher flows as we see in Figure 24, that is at Mzimhlophe Station
FLOW ON TRACKS (veh/h)
60
for both platforms and at New Canada3 platform. This is due to the internal capacity of the
TYPE B vehicles in comparison to the number of passengers at the platforms.
Figure 25: Visualisation of Table 16 of the occupancy of vehicles for TYPE A, TYPE B and Combination
(A and B)
TYPE A vehicles have the highest occupancy in comparison to TYPE B and the combination
as seen in Figure 25 . TYPE A vehicles have a lower internal carrying capacity than TYPE B
vehicles. This leads to TYPE A vehicles to have longer dwell time at the Stations, which also
means that TYPE A vehicles are moving with overloaded vehicles. The slowness of the
TYPE A vehicle is also shown in Figure 26 where the average speed at the stations is lower
for TYPE A vehicles. TYPE B vehicles demonstrate higher average speeds than TYPE A
vehicles and the combination of vehicles.
OCCUPANCY ON TRACKS (%)
61
Figure 26: Visualisation of Table 16 of the occupancy of vehicles for TYPE A, TYPE B and Combination
(A and B)
TYPE B vehicles have higher design speed, accelerate faster than TYPE A vehicles, and they
also have bigger internal capacity these are all the factors that put TYPE B vehicles at an
advantage. Due to these above-mentioned factors TYPE B vehicles exhibit shorter dwelling
times at the stations as seen in Figure 25. The occupancy in Figure 25 for TYPE B vehicles is
lower than most and in Figure 24 the flow is higher for most of the stations.
AVERAGE SPEED ON TRACKS (m/s)
62
6.3.2 The impact of equal interval shorter headways-Plan 2
To analyse equal interval headway, the vehicles were set to enter the network at the rate of
0.25 that is approximated to be every 5minutes. The entrance time was the same for all routes
designed. The routes were the same as the routes as described in the above sub-Sections that
is the Yellow route, Red route, and the Blue route.
In Plan 2 the combination of vehicle type that is vehicle TYPE B will be designated to the
Red route and the TYPE A vehicles will be designated to the Yellow and Blue routes. The
passenger dependent dwell time (random dwell time) was used for the dwelling times at
stations. The dwell time for Plan 2 is different from Plan 1 as the headways are different for
both plans. The dwell times for this section are found in Table 11 and Table 12 under the
5mins columns. A 5-hour simulation time was also implemented in this Plan as it was in Plan
1, as the first plan also had a five-hour simulation. There will be a comparison between Plan
1 and Plan 2 using similar concept as in sub-Section 6.2.1. The vehicle entrance is continuous
up until the simulation terminate which is contrary to the flow of sub-Section 6.2.1 where the
flow of vehicles terminates at the last time slot. The following diagrams and table show the
results as retrieved from the loop detectors.
Plan 1 Plan 2 Plan 1 Plan 2 Plan 1 Plan 2
mzimhlophe1 3.52 3.13 44.09 21.62 0.58 0.49
mzimhlophe2 2.51 11.48 25.15 66.37 0.55 1.69
mlamlankunzi1 0.00 0.00 0.00 0.00 0.00 0.00
mlamlankunzi2 2.29 5.22 45.05 49.95 0.28 0.92
newcanada1 0.00 0.00 0.00 0.00 0.00 0.00
newcanada2 2.45 7.04 50.27 91.38 0.26 0.83
newcanada3 3.57 7.91 48.60 77.65 0.67 1.14
newcanada4 2.77 11.30 28.49 65.31 0.59 1.69
longdale1 2.19 3.48 47.10 33.93 0.25 0.52
longdale2 3.57 8.26 46.17 57.80 0.57 1.34
crown1 0.00 0.00 0.00 0.00 0.00 0.00
crown2 0.00 0.00 28.25 63.14 0.00 0.00
P(T<=t) two tail
t-crit two tail 2.02
SPEED(m/s)
stats analysis α = 0.05 Ho : µ1 = µ2 Ha : µ1 ≠ µ2
1.88E-14 1.10E-05 2.27E-16
STATION
FLOW(veh/h) OCCUPANCY(%)
2.02 2.02 Table 20: Flow, capacity, and the speed at each platform for PLAN 1 and PLAN 2
TRACK OCCUPANCY (%) AVERAGE SPEED (m/s)
63
A statistical analysis of the data for the combination vehicles in Plan 1 and combination of
vehicles in Plan 2 showed that the population mean of the Plans are significantly different
(Table 20). The p-values for the flow, occupancy and the average speed are all below alpha.
Therefore, there is differences in the character of the traffic for the corridors at the stations.
This shows the impact the headway might have on the network.
Figure 27: Average vehicle flow at the stations for both Plan 1 and Plan 2
Figure 27 shows the differences of the flow for Plan 1 and Plan 2, the figure shows that there
are differences in the flow as the differences between the bars is significantly large. Plan 2
has more vehicles inserted to the network but there was a lot of teleporting that occurred in
Plan 2 Table 20 in Appendix C demonstrates this. This means there is too much pressure on
the network when Plan 2 with random dwell times is implemented. When the fixed dwell
time was tried the network experienced lesser congestion this fits well in theory, but it does
not in reality. The number of vehicles is higher, but this does not mean the network is
healthy. Mzimhlophe2 has the highest flow, this is because the Blue route does not have any
infringement towards the journey to George Goch, it also experiences lesser delays. This is
FLOW ON TRACKS (veh/h)
64
also due to the higher average speed on the route Figure 29 demonstrates this and so does
Figure 29 Figure 28 with higher occupancy on the route.
Figure 28: Occupancy at the stations for both Plan 1 and Plan 2
The occupancy of the Plans also differs, in Figure 28, New Canada2 has the highest
occupancy during the second plan. New Canada2 is the platform the accommodates traffic
from Mlamlankunzi2 on the Red route. The reason there is this large occupancy on the New
Canada2 platform is because most of the vehicles that waited longer to enter Mlamlankunzi2
were teleported to New Canada2 platform. New Canada4 and Mzimhlophe2 are platforms
accommodating the traffic from the Blue route, their occupancy is approximately on the same
level which shows there is lesser congestion on the route. Mzimhlophe1 has lesser occupancy
but the consecutive platform on the Yellow route, New Canada3, has a higher occupancy in
comparison to Mzimholphe1 which also suggest delays at the New Canada3. From Figure 26
it is visible that at New Canada Station there is heavy traffic and stalling of trains.
OCCUPANCY ON TRACKS (%)
65
Figure 29: Average Speed at the stations for both Plan 1 and Plan 2
As in the previous figures, Figure 27, and Figure 28, Mzimhlophe2 platform has higher
average speed, which also translates to New Canada4 having a higher speed. The Red route
that is Mmlamlankunzi2, New Canada2 and Longdale2 shows some inconsistency in the
average speeds. Mlamlankuzi2 has the speed approximately in the same level as New
Canada2 but the speed decreases significantly at Longdale1. The occupancy is extremely
high at New Canada2 as demonstrated in Figure 28 and the flow is a lower in New Canada2
which leads to the speed at Longadale1 to be slow as Longadale1 one depends on the New
Canada2. There should be vehicles that are teleported from New Canada2 to Longdale1 but
from the observation it seems like the traffic at New Canada2 is heavily congested such that
teleporting to the next edge is not possible. When this occurs the vehicle normally gets
removed from the network. Therefore, the original routes Yellow, Blue and Red are not
suitable for Plan 2. Section 6.2.3 evaluates possible solution to this issue for Plan 2.
AVERAGE SPEED ON TRACKS (m/s)
66
6.3.3 Adding route(s) with same headway- PLAN 3
This sub-Section evaluates a scenario when a route is added into the rail network. Three
routes were tried, as to find the best suitable route for the network to operate with zero or
minimum congestion. There are already three existing routes on the network that is the Red
route, Yellow1 route and the blue routes. The newer route evaluated are the Green route,
Magenta route and the Yellow 2 route see Table 10 for description. The routes were
introduced in the network one at a time. Started with the green route as the first alternative,
the Magenta route as the second alternative and the Yellow 2 route as the third alternative.
All the alternatives were introduced to increase the capacity on the Vereeniging –
Johannesburg line as it seen in the above sub-Section this corridor is experiencing heavy
delays due to trains dwelling longer at the Stations
6.3.3.1 Alternative 1
In this alternative the green line was added to the already existing routes. This mean that
during this alternative the Red route, Blue route, Yellow1 route and the Green route were
operating routes on the network. The Green route was introduced to add more trains to the
system that will enable the passenger to use an alternative route to reach Johannesburg. This
route is also aimed for the passengers going to the North East side of Johannesburg and those
en-route to Germiston Station. These are passengers who would normally have to change
trains at New Canada or Johannesburg Park Station. The Green route follows the same path
as the red line, this was done to have the Green route alternate with the Red route at New
Canada Station as to not disturb the Yellow route but the this created conflict in two places
see Figure 30.
67
Figure 30: Frame 1, Frame 2 showing routes the area where waiting time is longer
The first conflict was between the Green route and the Yellow 1 route which occurs at the
entrance of the New Canada Station. The conflict was caused by fact that the Green route
operates on the outer left side of the Yellow1 route but share the stopping platform 3. The
track at platform 3 can switch to either to the Johannesburg path or to the George Gogh path
and that is the reason Platform 3 was chosen to be the also the stopping for the Green route.
The stalling occurs in the blue circled area in the first frame as shown in Figure 30. The
second conflict occurs when the Red route and the Yellow1 route leave the New Canada
Station see frame 2 of Figure 31. In this instance the Yellow1 route must stop even though it
is ready to depart, so that the Red route vehicle can clear the block. This means that vehicles
in the Yellow1 route must wait extra minutes and the Green line also delayed as the Yellow1
vehicle is still on the block
6.3.3.2 Alternative 2
In the first alternative the network experienced conflict in two places at the New Canada
Station. Therefore, the Red route was replaced by the Magenta route see frame 3 of Figure
31. The introduction of the Magenta route eliminated the congestion as seen in frame 2 but
did not mitigate the stalling that occurred between the Green route and the Yellow1 route.
This was due to that the Green traffic increased as it no longer alternates with the red traffic.
68
The delays in this alternative started faster than in Alternative 1. Therefore, the second
alternative did not perform as envisioned.
Figure 31: Frame 3 showing introduction of the Magenta route
6.3.3.3 Alternative 3
Due to the conflict of the Yellow1 traffic and the Green traffic described in the second
Alternative, the Yellow 1 route was replaced by the Yellow 2 route. Yellow 2 routes still
serve the Naledi – Johannesburg corridor. Frame 2 in Figure 32 shows the introduction of the
Yellow 2 route. The Green route and the Yellow 2 route criss-crosses and alternates at the
area pointed by the blue arrow. The area pointed by the black arrow is the area where the
conflict between the Green traffic and the Yellow 1 traffic occurred. To determine which new
route should be considered by either the Yellow traffic or Green traffic the stopping platform
was the indicator, meaning which platform will be suitable for either traffic that will create
less conflict.
Figure 32: Changes made to the network to minimise congestion
There are three platforms in New Canada Station that can accommodate traffic towards
Longdale Station which forms part of the Naledi-Johannesburg corridor and the Vereeniging
– Johannesburg corridor, these are platform1, platform2 and platform3. Platform 3 could not
be used as it is the platform already in use for the Yellow 1 traffic and it is part of the already
existing conflict. Platform 1 could not be used as it is not in the path of the Naledi –
1
2
3
69
Johannesburg and the Vereeniging – Johannesburg corridors in other words, there are no
possible train paths that could be created that could allow the Green traffic nor the Yellow
traffic to stop at Platform 1. Platform 2 was the last platform that could be used but the Green
route could not use this platform as it does not allow traffic to Crown Station, the Station that
follows New Canada Station for the Vereeniging/Naledi – George Goch corridors. Therefore,
the Yellow route was changed to form Yellow 2 (see frame 1 of Figure 32) from Yellow 1
(see Figure 30). Alternative 3 showed to have lesser conflicts and delays also better
performance hence it was the last alternative considered. In this alternative the vehicle type
for the Yellow2 route was changed from TYPE A to TYPE B because when TYPE A was
used there was still some congestion. It was when the vehicle type changed that the route did
not experience congestion.
Alt 1 Alt 2 Alt 3 Alt 1 Alt 2 Alt 3 Alt 1 Alt 2 Alt 3
mzimhlophe1 4 10 13 41.56 66.92 59.52 3.16 4.38 4.39
mzimhlophe2 2 12 10 24.51 63.59 67.88 1.13 4.59 4.08
mlamlankunzi1 0 4 11 0.00 63.51 79.11 0.00 1.69 4.54
mlamlankunzi2 2 0 13 41.82 0.00 76.61 3.51 0.00 4.66
newcanada1 0 7 11 0.00 90.37 74.28 0.00 4.63 4.37
newcanada2 3 0 12 47.20 0.00 55.70 3.36 0.00 4.33
newcanada3 4 9 13 48.16 66.23 68.79 1.06 4.30 4.63
newcanada4 3 11 10 26.81 62.53 66.82 2.26 4.52 3.93
longdale1 2 2 11 43.98 31.13 74.76 3.36 0.32 4.25
longdale2 4 9 12 44.76 62.57 54.50 1.97 4.05 4.66
crown1 0 0 13 0.00 0.00 67.17 0.00 0.00 4.51
crown2 0 0 0 26.25 61.71 64.10 2.00 2.57 2.84
FLOW(veh/h) OCCUPANCY(%) SPEED(m/s)STATION
Table 21: Average Flow, Occupancy, and speed on each platform in the concerned Stations
The results of the flow, occupancy, and average speed for Plan 3 are shown in Table 21 the
visualisation of the Table is demonstrated by Figure 33, Figure 34, and Figure 35. These
figures and table show how the alternatives as described above produced. From Table 21,
Alternative 1 is experiencing a lot of stalling, this is demonstrated by the occupancy column
when viewed with the flow and the speed. There is no movement in alternative 1, the speed
column shows that the average speeds at the stations is generally low and the through put of
the vehicles is also low shown by the flow. In general sense the first alternative is not suitable
TRACK OCCUPANCY (%) AVERAGE SPEED (m/s)
70
to be considered for the network as it is slow and lacks capacity. Alternative 3 is a better
performer in the general sense higher speeds, higher flows and high occupancy and lesser
congestion.
Figure 33: Average vehicle flow at the stations for both Alt1, Alt2 and Alt3
Figure 33 shows the flow of the alternatives for all stations. Some alternatives did not include
all the platforms hence in some platform stations some alternatives are zero. For example,
Mlamlankunzi1 and Mlamlankuzi2 in the first alternative the Red route is used which means
that Mlamlankunzi2 will be utilised hence the green bar. But there is no blue bar which is for
the second alternative and is seen in Mlamlankunzi1 this is because for the second alternative
the Magenta route was used to replace the red route, where in this case Mlamlankunzi1 was
the platform utilised. This goes also for the routes that alternated like the Yellow route in
New Canada Station.
The flow for the first alternative is extremely low compared to the two alternatives. It seems
that the double congestion points that the first alternative experienced had a negative impact
on the performance of the first alternative. Alternative three has the highest flow in
FLOW ON TRACKS (veh/h)
71
comparison to the other alternatives, this alternative had many changes therefore the original
route was severely change.
Figure 34: Vehicle occupancy at the stations for both Alt1, Alt2 and Alt3
The Occupancy between alternative 2 and alternative3 is well balanced. These alternatives
also have higher flows and the speeds are also relatively good. The major difference between
alternative 2 and alternative 3 is the time the simulation started to get congested. In
alternative 2 the simulation got congested at the middle of the simulation but it able to correct
itself. Therefore, the number of vehicles that were waiting for exceedingly long periods were
exceedingly small. The third alternative did not have issues with vehicles that needed to have
operational braking or need to wait for a long time. Figure 34 shows a higher occupancy for
the second and third alternative, in the previous investigation of this report when the
occupancy is normally high it would mean there is a great chance that the route is
experiencing congestion. Normally this coincides with the slowness of the average speed and
low number of vehicles on the network. Which in the third alternative all three were at high
levels
OCCUPANCY ON TRACKS (%)
72
Figure 35: Average vehicle speed at the stations for both Alt1, Alt2 and Alt3
The average speed for the third alternative in Figure 35 is the most interesting as it has
uniform speed throughout the journey of the vehicles. In all the evaluations that have been
done in this report uniform speed is not a phenomenon that readily occurs. This means that in
the third alternative the arrangement of routes can buffer some delays creating a free-flowing
rail traffic. This also means that there was no unnecessary operational breaking as
experienced before which is beneficial to the rail infrastructure. The second alternative also
did not perform badly on the average speed, of course the speed in the second alternative is
not as uniform as the third alternative, but it is better than the first alternative. As seen in
Figure 35 the first alternative has see-saw average speeds this means that the vehicles had to
apply breaks all through their journey.
AVERAGE SPEED ON TRACKS (m/s)
73
7 . CLOSING REMARKS
7.1 DISCUSSION
Capacity analysis is to scrutinise the bottlenecks in the network and planning efficient
timetable with high punctuality. New Canada Station is the intermediate Station in the South
Gauteng corridor which is an area that experiences bottleneck. Finding solutions to the
bottleneck would minimise the congestion that is experienced by the corridor. New Canada
Station is located at centre of South West part of the Gauteng network. When traffic slows
down on the North side of Gauteng, traffic will slow down on the South side of Gauteng
causing delays, overcrowded trains, and lesser number of trains able to enter the network.
Several factors influence the performance of the corridors that is:
1. The infrastructure - which include the performance of the signalling infrastructure, the
length of the blocks and the design of the track (radii of curves and gradients).
2. Rolling Stock - which include the design features of the vehicle like the size of the
door, surface area of the vehicle, speed, braking and acceleration capabilities of the
vehicle.
3. Operation characteristics- which include timetable design, traffic flow, dwell and
buffer times, passenger inflow to the stations and selection of train paths.
7.1.1 The Infrastructure
The relationship between the length of the block and the speed of the vehicle influences
performance of the corridor. Longer blocks where slower vehicles move tend to have longer
headway due to longer travel times in comparison to shorter blocks with slower moving
vehicle. Longer blocks with faster vehicles tend to perform better in comparison, of course
this is due to that a vehicle must clear the block before the next enters. In the area of the study
the Vereeniging – New Canada – Johannesburg – George Goch route has longer block
lengths than the Naledi – Johannesburg – George Goch corridor(s). Therefore, route design
specific to the track design is quite essential in the capacity of the network.
74
7.1.2 Rolling Stock
The specification of the vehicle is important factor in the ability for the vehicle to move faster
from the station. The vehicle type does have an impact on the dwell time especially in
corridors with high volume of passengers. The ability for passengers to alight and board rely
on how accessible the vehicle is. If the naledi-Johannesburg corridor were operating by itself
without the operation of Naledi – George Goch this corridor would also go through heavy
delays and overcrowding as the Vereeniging – Johannesburg corridor. The ability of the train
to have faster acceleration and high speed is essential as they make the train move a little
quicker as we have seen in the comparison between TYPE A vehicle and TYPE B vehicles
when they were operating individually on the network. TYPE B vehicles managed to be
approximately 2 mins faster than TYPE A vehicles. TYPE B vehicles unfortunately also
experienced delays on the Red route although it was not severe as TYPE A. This was since
TYPE B vehicles have more entrances and exits to TYPE A vehicles and TYPE B vehicles
also has doors that are wider than TYPE A by 0.3m. In total TYPE B vehicles had
approximately 40m of width more than TYPE A vehicles that allowed passenger to have
faster circulation.
7.1.3 Operation characteristics
The timetable is an important integral of passenger rail traffic as it informs the passenger of
the sequence of trains. The headway informs the passenger on how to plan their journey.
When the headway is greater than 7 mins (some authors have it 10 mins) passengers must
pre-plan their arrival at the stations. Most people start work or school round about the same
time, which means a huge group of people come into the station at same time. The longer
headways create a situation where passengers arriving are uniformly distributed, which
influences the dwelling time of the vehicle as passengers need to alight and board the vehicle.
The number of passengers on platforms influences the dwell time. If the platforms are
crowded the dwell time will increase, which will cause stalling for the vehicle. When the
vehicle is stalled at a station it prevents other vehicles following it from moving which will
eventually cause the network to experience delays.
The more the vehicles stalls at stations the delay time accumulates which will affect the flow
of the traffic especially on networks that do not allow vehicle to change tracks that is tracks
where overtaking is possible.
75
7.1.4 Simulation
Plan 1 was the initial stage of starting the evaluation of the traffic from the Gauteng South
corridors. The first step in this Plan was to use the Timetable from the operator and use the
allocated timeslots in the simulation. The headways varied between 5 mins to 25 mins
depending on the corridor and the prescribed stopping time of 40 seconds utilised by the
operator was used, this stopping time is called fixed dwell time. When the fixed dwell time
was used all the trains were on time they fit perfectly with the timetable, the fixed dwell time
was accommodated in the headways. All evaluations of the train type performed without
delays when the dwell time was fixed. The TYPE B vehicles had faster travel times, but this
is due to it having higher max speed and average speed. The average departure time
differences between TYPE A vehicles and TYPE B vehicles is between 1.5mins -3 when the
dwell time is fixed and approximately 2mins to 5 mins when random dwell time is applied
When the dwell time which is dependent to the passengers at the platform which is called
random dwell time in the report, the network experienced delays. The Red route experiences
delays especially when the TYPE A were evaluates. The waiting time for vehicles waiting to
enter either Mlamlankunzi or New Canada stations were so long that majority of vehicles
needed to teleport to another following edge. The set teleporting time is 300s (5 mins)
simulation time, this created confusion as some vehicles appeared at following Stations
before the designated vehicle, overtaking is not allowed in the simulation.
The combination of TYPE A and TYPE B inserted to the Network seemed to work similarly
to the TYPE B vehicles. In the combination the TYPE B vehicles were designated to the Red
route as TYPE B vehicles responded well to the passenger traffic on the Red route which
minimised the delays on the route. There were statistical differences between TYPE A and
TYPE B when it comes to occupancy. The dwell time and the speed could be the factors of
this difference, but these factors were not investigated in this study whether they do have an
impact it is a postulate from the author. In Plan 1 it is concluded that the using combination
of TYPE A and TYPE B vehicles has s similar effect as using TYPE B.
Plan 2 uses the random dwell times with TYPE A and TYPE B combination. The
combination of vehicles proved to be effective as TYPE B vehicles. In theory TYPE B
vehicles would be the suited candidate as it outperformed TYPE A vehicle and TYPE A –
TYPE B combination but reality dictates that the combination of the vehicle types be used.
This is because there is still functioning fleet that is available and some of the fleet is
refurbished. Therefore, in this Plan the combination is evaluated. The shorter interval caused
76
major congestion on the Red route this was because the dwell time for an example were
sometimes longer than the departing interval of the vehicles. This imbalance of the dwell
time longer than the pre-planned headway created the worst congestion in all tries. This led to
the creation of the option finding other routes that can work.
When different routes were tried, Plan 3, they led to various results some closer to each other
some much further from each other. Three alternatives were tried in Plan 3 the method in
finding the best suitable route was through trial and error. The Green route was introduced to
minimise the delays from the Red route which were the consequence of longer dwell times
due to overcrowding on the platform. The concept was to follow the Yellow route and Blue
route operation, that is one line to Johannesburg Park Station and the other to George Goch
Station. The Green route created more congestion on the line worse than before therefore it
was unsuitable. Observations made from the Green route like determining the positions
where the network experiences congestion, the congestions were found out that they occur in
two places on the network which involved the Green, Red and Yellow route.
To solve the problem, the Red route was removed, and the Magenta route was added to the
network. The Magenta route had the same destination as the Red route it just utilised different
tracks. This second alternative worked better than the first alternative though the conflict
between the Yellow route and the Green route continued. The conflict was made worse
because there was frequent Green traffic than there was in the first alternative. In the first
alternative the Green and the Red traffic shared the same tracks up until New Canada Station
so that there will be less disturbance for the Yellow route. The Red route was meant to serve
as a calming effect.
Because of the conflict between the Yellow route and the Green route a different route from
Naledi to Johannesburg was created. Route was named Yellow2 route and the original
Yellow route was called Yellow1 route to avoid confusion. The addition of Yellow2 route
was successful as alternative three did not experience congestion for the 5-hour period of
simulation. Because how well it worked no other alternatives were tried. The third alternative
is the best solution to combat capacity problem on the Gauteng South corridor.
77
7.2 CONCLUSION
Headways, rolling stock and routing were found to be important factors in finding best
solutions to traffic flows on the network. The rolling stock attributes, routing technique and
the headways had a big influence on the dwell times. For corridors with heavy passenger
traffic rolling stock with the specification like Xtrapolis vehicles could be beneficial in
combating overcrowding. Uniform shorter time interval headways decrease the congestion in
the network even when the arrival rate of passengers is uniformly distributed.
The knowledge of optimising the railway infrastructure plays a critical role. The first plan
had the Red route and did not experience any major issues even when the random dwelling
time was introduced it managed to relatively do alright. But when there are some
disturbances of some sort like introducing a new train line it finds it difficult to adhere to the
schedule. Unfortunately, it seems that finding the best route is challenging as it is time
consuming as you must change many variables on the infrastructure like the changing tracks,
changing the function of signals and changing vehicles.
In alternative 3 which is deemed to be the best solution had many changes made to it. From
this alternative it was visible that both the 5M2A and the Xtrapolis can operate concurrently
in the network with minimum delays. The Xtrapolis was used in the Naledi-New Canada-
Johannesburg route instead of the 5M2A as per the other alternative but the important factor
in this case was the speed and the acceleration of the Xtrapolis that made the difference and
not the capabilities of fast circulation of passengers into and out of the vehicle. Therefore, it
also possible to continue using the M-type family in the Naledi-New Canada-Johannesburg as
most of the infringements were caused by the slowness of the vehicle and but the speed of the
M-type family should be above 90 km/h. It must be noted that, if the 5M2A or the M-type
family solely operates on the SOWETO corridor it cannot eliminate the congestion and
delays experienced on the SOWETO corridor. The Xtrapolis should be included in process of
improving the traffic flow in the SOWETO corridor.
78
7.3 SIDE NOTES (Recommendations)
7.3.1 Passenger surveys
It is evident that the number of passengers on platforms influences the dwell time, which in
turn impacts the delays on corridors. Knowing the amount of passenger heading to a certain
direction is important as it will inform a planner of the demand to supply the appropriate
number of vehicles for that corridor. In most Stations the entrance is located at the station
where people can scan their tickets, mostly the information received from the scanner is
information about the number of passengers entering and sometimes leaving the station. The
scanners do not give the number of people travelling in certain direction; therefore, scanners
could be placed at the entrance of platforms. Now, this does depend on the design of the
Stations, in some Stations this could cause passenger traffic, therefore in such Stations this
method must not implemented.
7.3.2 Use of Open Source programmes
Open Source programmes like Python and SUMO are some of less expensive ways to do
experiments. Traffic planners and developers can work to develop a plan that could be
customised for that area. These programmes are also community based which makes getting
help quicker and easier. They also get frequent updates as several people can detect bugs.
So far, did not experience that the simulation tool SUMO is able to simulate dispatching of
trains per signal. When trying to load infrequent intervals for vehicles it does not work well
unless the time interval is manual. Therefore, there should be more comparative studies for
SUMO and other well-known or commonly used railway simulation tools, to know how
much uncertainty it provides to make improvements on SUMO.
7.3.3 Passenger railway research and studies
It is difficult to find studies on passenger railways in South Africa. The most studies that are
done are based on freight railway transportation. Therefore, most opportunities to improve
the passenger railway are missed. There are few major concerns in passenger rail that need
attention like understanding the causes for crowding and the pattern of crowding, in train also
on platforms. Travelling pattern study of the public transport users concentrating on railway.
So far what is noticed is that the design of operation is done similarly to that of freight
railway which does not cater for nuances of passenger rail.
79
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9 . APPENDICES
9.1 Appendix B: Tables from results for Varying Headways – Plan 1
Results for the flow, occupancy, and speed for periods of 5 mins detects from the loops
Departure time for TYPEA and TYPEB vehicle for fixed and random dwell times
Table 22: Departure time for TYPE A vehicle with fixed dwell and random times for the Yellow
Route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
3 TRAIN931 7 TRAIN931 11 TRAIN931
23 TRAIN933 27 TRAIN933 31 TRAIN933
28 TRAIN934 32 TRAIN934 36 TRAIN934
53 TRAIN937 57 TRAIN937 61 TRAIN937
61 TRAIN939 65 TRAIN939 69 TRAIN939
78 TRAIN9313 82 TRAIN9313 86 TRAIN9313
95 TRAIN9315 99 TRAIN9315 103 TRAIN9315
123 TRAIN9319 128 TRAIN9319 132 TRAIN9319
128 TRAIN9321 132 TRAIN9321 136 TRAIN9321
143 TRAIN9324 147 TRAIN9324 151 TRAIN9324
158 TRAIN9326 162 TRAIN9326 166 TRAIN9326
163 TRAIN9327 168 TRAIN9327 172 TRAIN9327
183 TRAIN9333 187 TRAIN9333 191 TRAIN9333
203 TRAIN9336 207 TRAIN9336 211 TRAIN9336
213 TRAIN9337 218 TRAIN9337 222 TRAIN9337
233 TRAIN9339 238 TRAIN9339 242 TRAIN9339
238 TRAIN9342 242 TRAIN9342 246 TRAIN9342
253 TRAIN9344 257 TRAIN9344 261 TRAIN9344
273 TRAIN9345 277 TRAIN9345 281 TRAIN9345
297 TRAIN9348 301 TRAIN9348 305 TRAIN9348
303 TRAIN9349 307 TRAIN9349 311 TRAIN9349
333 TRAIN9352 337 TRAIN9352 342 TRAIN9352
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
10 TRAIN931 21 TRAIN931 32 TRAIN931
28 TRAIN933 38 TRAIN933 47 TRAIN933
38 TRAIN934 48 TRAIN934 58 TRAIN934
59 TRAIN937 69 TRAIN937 80 TRAIN937
70 TRAIN939 81 TRAIN939 92 TRAIN939
84 TRAIN9313 95 TRAIN9313 106 TRAIN9313
102 TRAIN9315 113 TRAIN9315 124 TRAIN9315
129 TRAIN9319 139 TRAIN9319 150 TRAIN9319
139 TRAIN9321 150 TRAIN9321 160 TRAIN9321
149 TRAIN9324 159 TRAIN9324 169 TRAIN9324
164 TRAIN9326 175 TRAIN9326 186 TRAIN9326
176 TRAIN9327 188 TRAIN9327 201 TRAIN9327
190 TRAIN9333 201 TRAIN9333 212 TRAIN9333
209 TRAIN9336 219 TRAIN9336 229 TRAIN9336
219 TRAIN9337 230 TRAIN9337 241 TRAIN9337
238 TRAIN9339 247 TRAIN9339 257 TRAIN9339
250 TRAIN9342 262 TRAIN9342 275 TRAIN9342
261 TRAIN9344 274 TRAIN9344 286 TRAIN9344
282 TRAIN9345 295 TRAIN9345 308 TRAIN9345
303 TRAIN9348 314 TRAIN9348 324 TRAIN9348
313 TRAIN9349 323 TRAIN9349 334 TRAIN9349
339 TRAIN9352 349 TRAIN9352 359 TRAIN9352
Departures of TYPE A vehicle WHEN dwell time is passenger based
Departures of TYPE A vehicle WHEN dwell time is fixed prescribed to 40s
Mzimhlophe1 NewCanada3 Longdale2
Mzimhlophe1 NewCanada3 Longdale2
83
Table 23: Departure time for TYPE B vehicle with fixed dwell and random times for the Yellow
Route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
2 TRAIN931 5 TRAIN931 8 TRAIN931
22 TRAIN933 25 TRAIN933 28 TRAIN933
27 TRAIN934 30 TRAIN934 33 TRAIN934
52 TRAIN937 55 TRAIN937 58 TRAIN937
60 TRAIN939 63 TRAIN939 66 TRAIN939
77 TRAIN9313 80 TRAIN9313 83 TRAIN9313
94 TRAIN9315 97 TRAIN9315 100 TRAIN9315
122 TRAIN9319 126 TRAIN9319 129 TRAIN9319
127 TRAIN9321 130 TRAIN9321 133 TRAIN9321
142 TRAIN9324 145 TRAIN9324 148 TRAIN9324
157 TRAIN9326 160 TRAIN9326 163 TRAIN9326
162 TRAIN9327 166 TRAIN9327 169 TRAIN9327
182 TRAIN9333 185 TRAIN9333 188 TRAIN9333
202 TRAIN9336 205 TRAIN9336 208 TRAIN9336
212 TRAIN9337 216 TRAIN9337 219 TRAIN9337
232 TRAIN9339 236 TRAIN9339 239 TRAIN9339
237 TRAIN9342 240 TRAIN9342 243 TRAIN9342
252 TRAIN9344 255 TRAIN9344 258 TRAIN9344
272 TRAIN9345 275 TRAIN9345 278 TRAIN9345
296 TRAIN9348 299 TRAIN9348 302 TRAIN9348
302 TRAIN9349 305 TRAIN9349 308 TRAIN9349
332 TRAIN9352 336 TRAIN9352 339 TRAIN9352
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
6 TRAIN931 12 TRAIN931 19 TRAIN931
27 TRAIN933 35 TRAIN933 43 TRAIN933
33 TRAIN934 41 TRAIN934 49 TRAIN934
56 TRAIN937 64 TRAIN937 72 TRAIN937
64 TRAIN939 72 TRAIN939 80 TRAIN939
81 TRAIN9313 89 TRAIN9313 96 TRAIN9313
97 TRAIN9315 103 TRAIN9315 109 TRAIN9315
126 TRAIN9319 134 TRAIN9319 141 TRAIN9319
132 TRAIN9321 140 TRAIN9321 147 TRAIN9321
146 TRAIN9324 153 TRAIN9324 160 TRAIN9324
161 TRAIN9326 168 TRAIN9326 175 TRAIN9326
167 TRAIN9327 174 TRAIN9327 181 TRAIN9327
186 TRAIN9333 194 TRAIN9333 201 TRAIN9333
206 TRAIN9336 213 TRAIN9336 220 TRAIN9336
215 TRAIN9337 221 TRAIN9337 227 TRAIN9337
235 TRAIN9339 242 TRAIN9339 248 TRAIN9339
241 TRAIN9342 247 TRAIN9342 254 TRAIN9342
256 TRAIN9344 263 TRAIN9344 271 TRAIN9344
276 TRAIN9345 283 TRAIN9345 291 TRAIN9345
299 TRAIN9348 305 TRAIN9348 311 TRAIN9348
306 TRAIN9349 313 TRAIN9349 320 TRAIN9349
336 TRAIN9352 341 TRAIN9352 347 TRAIN9352
NewCanada3Mzimhlophe1 Longdale2
Departures of TYPE B vehicle WHEN dwell time is passenger based
Departures of TYPE B vehicle WHEN dwell time is fixed prescribed to 40s
Mzimhlophe1 NewCanada3 Longdale2
84
Table 24: Departure time for TYPE A vehicle with fixed and random dwell times for the Blue
Route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
17 TRAIN972 21 TRAIN972 25 TRAIN972
34 TRAIN975 38 TRAIN975 42 TRAIN975
57 TRAIN978 61 TRAIN978 65 TRAIN978
69 TRAIN9711 73 TRAIN9711 77 TRAIN9711
74 TRAIN9712 78 TRAIN9712 82 TRAIN9712
104 TRAIN9717 108 TRAIN9717 112 TRAIN9717
114 TRAIN9718 118 TRAIN9718 122 TRAIN9718
134 TRAIN9722 138 TRAIN9722 142 TRAIN9722
139 TRAIN9723 143 TRAIN9723 147 TRAIN9723
172 TRAIN9729 176 TRAIN9729 180 TRAIN9729
175 TRAIN9730 179 TRAIN9730 184 TRAIN9730
189 TRAIN9734 193 TRAIN9734 197 TRAIN9734
234 TRAIN9741 238 TRAIN9741 242 TRAIN9741
240 TRAIN9743 244 TRAIN9743 248 TRAIN9743
294 TRAIN9747 298 TRAIN9747 302 TRAIN9747
310 TRAIN9750 314 TRAIN9750 318 TRAIN9750
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
22 TRAIN972 31 TRAIN972 41 TRAIN972
38 TRAIN975 47 TRAIN975 56 TRAIN975
60 TRAIN978 68 TRAIN978 76 TRAIN978
73 TRAIN9711 81 TRAIN9711 91 TRAIN9711
81 TRAIN9712 88 TRAIN9712 97 TRAIN9712
108 TRAIN9717 117 TRAIN9717 126 TRAIN9717
118 TRAIN9718 126 TRAIN9718 135 TRAIN9718
138 TRAIN9722 145 TRAIN9722 154 TRAIN9722
146 TRAIN9723 154 TRAIN9723 163 TRAIN9723
176 TRAIN9729 184 TRAIN9729 193 TRAIN9729
184 TRAIN9730 192 TRAIN9730 201 TRAIN9730
195 TRAIN9734 205 TRAIN9734 215 TRAIN9734
238 TRAIN9741 245 TRAIN9741 254 TRAIN9741
246 TRAIN9743 256 TRAIN9743 266 TRAIN9743
297 TRAIN9747 305 TRAIN9747 313 TRAIN9747
314 TRAIN9750 322 TRAIN9750 331 TRAIN9750
Mzimhlophe2 NewCanada4 Crown2
Departures of TYPE A vehicle WHEN dwell time is fixed prescribed to 40s
Mzimhlophe2 NewCanada4 Crown2
Departures of TYPE A vehicle WHEN dwell time is passenger based
85
Table 25: Departure time for TYPE B vehicle with fixed and random dwell times for the Blue
Route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
16 TRAIN972 19 TRAIN972 22 TRAIN972
33 TRAIN975 36 TRAIN975 39 TRAIN975
56 TRAIN978 59 TRAIN978 62 TRAIN978
68 TRAIN9711 71 TRAIN9711 74 TRAIN9711
73 TRAIN9712 76 TRAIN9712 79 TRAIN9712
103 TRAIN9717 106 TRAIN9717 109 TRAIN9717
113 TRAIN9718 116 TRAIN9718 119 TRAIN9718
133 TRAIN9722 136 TRAIN9722 139 TRAIN9722
138 TRAIN9723 141 TRAIN9723 144 TRAIN9723
171 TRAIN9729 174 TRAIN9729 177 TRAIN9729
174 TRAIN9730 176 TRAIN9730 180 TRAIN9730
188 TRAIN9734 191 TRAIN9734 194 TRAIN9734
233 TRAIN9741 236 TRAIN9741 239 TRAIN9741
239 TRAIN9743 242 TRAIN9743 245 TRAIN9743
293 TRAIN9747 296 TRAIN9747 299 TRAIN9747
309 TRAIN9750 312 TRAIN9750 315 TRAIN9750
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
19 TRAIN972 26 TRAIN972 33 TRAIN972
36 TRAIN975 42 TRAIN975 48 TRAIN975
59 TRAIN978 64 TRAIN978 71 TRAIN978
71 TRAIN9711 78 TRAIN9711 85 TRAIN9711
77 TRAIN9712 83 TRAIN9712 90 TRAIN9712
107 TRAIN9717 113 TRAIN9717 120 TRAIN9717
116 TRAIN9718 122 TRAIN9718 129 TRAIN9718
135 TRAIN9722 141 TRAIN9722 147 TRAIN9722
140 TRAIN9723 146 TRAIN9723 152 TRAIN9723
174 TRAIN9729 179 TRAIN9729 186 TRAIN9729
179 TRAIN9730 184 TRAIN9730 190 TRAIN9730
191 TRAIN9734 196 TRAIN9734 203 TRAIN9734
236 TRAIN9741 242 TRAIN9741 249 TRAIN9741
242 TRAIN9743 247 TRAIN9743 253 TRAIN9743
295 TRAIN9747 300 TRAIN9747 306 TRAIN9747
312 TRAIN9750 317 TRAIN9750 324 TRAIN9750
Mzimhlophe2 NewCanada4 Crown2
Departures of TYPE B vehicle WHEN dwell time is fixed prescribed to 40s
Mzimhlophe2 NewCanada4 Crown2
Departures of TYPE B vehicle WHEN dwell time is passenger based
86
Table 26:Departure time for TYPE A vehicle with fixed and random dwell times for the Red Route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
43 TRAIN906 46 TRAIN906 51 TRAIN906
63 TRAIN9010 66 TRAIN9010 71 TRAIN9010
93 TRAIN9014 96 TRAIN9014 101 TRAIN9014
103 TRAIN9016 106 TRAIN9016 111 TRAIN9016
123 TRAIN9020 126 TRAIN9020 131 TRAIN9020
148 TRAIN9025 151 TRAIN9025 156 TRAIN9025
163 TRAIN9028 166 TRAIN9028 171 TRAIN9028
178 TRAIN9031 181 TRAIN9031 186 TRAIN9031
181 TRAIN9032 185 TRAIN9032 189 TRAIN9032
195 TRAIN9035 198 TRAIN9035 203 TRAIN9035
213 TRAIN9038 216 TRAIN9038 221 TRAIN9038
233 TRAIN9040 236 TRAIN9040 241 TRAIN9040
293 TRAIN9046 296 TRAIN9046 301 TRAIN9046
310 TRAIN9051 313 TRAIN9051 318 TRAIN9051
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
62 TRAIN906 84 TRAIN906 107 TRAIN906
82 TRAIN9010 105 TRAIN9010 128 TRAIN9010
116 TRAIN9014 132 TRAIN9016 154 TRAIN9016
137 TRAIN9020 157 TRAIN9014 185 TRAIN9014
167 TRAIN9025 174 TRAIN9020 224 TRAIN9025
201 TRAIN9031 196 TRAIN9025 289 TRAIN9031
234 TRAIN9038 213 TRAIN9028 313 TRAIN9038
255 TRAIN9040 235 TRAIN9032 366 TRAIN9046
314 TRAIN9046 261 TRAIN9031 387 TRAIN9051
284 TRAIN9038
304 TRAIN9040
340 TRAIN9046
360 TRAIN9051
Mlamlankunzi2 NewCanada2 Longdale1
Departures of TYPE A vehicle WHEN dwell time is fixed prescribed to 40s
Mlamlankunzi2 NewCanada2 Longdale1
Departures of TYPE A vehicle WHEN dwell time is passenger based
87
Table 27: Departure time for TYPE B vehicle with fixed and random dwell times for the Red Route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
42 TRAIN906 45 TRAIN906 48 TRAIN906
62 TRAIN9010 65 TRAIN9010 68 TRAIN9010
92 TRAIN9014 95 TRAIN9014 98 TRAIN9014
102 TRAIN9016 105 TRAIN9016 108 TRAIN9016
122 TRAIN9020 125 TRAIN9020 128 TRAIN9020
147 TRAIN9025 150 TRAIN9025 153 TRAIN9025
162 TRAIN9028 165 TRAIN9028 168 TRAIN9028
177 TRAIN9031 180 TRAIN9031 183 TRAIN9031
179 TRAIN9032 182 TRAIN9032 185 TRAIN9032
194 TRAIN9035 197 TRAIN9035 200 TRAIN9035
212 TRAIN9038 215 TRAIN9038 218 TRAIN9038
232 TRAIN9040 235 TRAIN9040 238 TRAIN9040
292 TRAIN9046 295 TRAIN9046 298 TRAIN9046
309 TRAIN9051 312 TRAIN9051 315 TRAIN9051
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
50 TRAIN906 62 TRAIN906 74 TRAIN906
72 TRAIN9010 84 TRAIN9010 97 TRAIN9010
103 TRAIN9014 116 TRAIN9014 130 TRAIN9014
116 TRAIN9016 129 TRAIN9016 143 TRAIN9016
134 TRAIN9020 148 TRAIN9020 164 TRAIN9020
162 TRAIN9025 180 TRAIN9025 199 TRAIN9025
175 TRAIN9028 193 TRAIN9028 211 TRAIN9028
187 TRAIN9031 205 TRAIN9031 223 TRAIN9031
205 TRAIN9035 218 TRAIN9032 238 TRAIN9032
225 TRAIN9038 231 TRAIN9035 266 TRAIN9038
242 TRAIN9040 246 TRAIN9038 332 TRAIN9046
303 TRAIN9046 258 TRAIN9040 344 TRAIN9051
318 TRAIN9051 317 TRAIN9046
331 TRAIN9051
Mlamlankunzi2 NewCanada2 Longdale1
Departures of TYPE B vehicle WHEN dwell time is fixed prescribed to 40s
Mlamlankunzi2 NewCanada2 Longdale1
Departures of TYPE B vehicle WHEN dwell time is passenger based
Table 28: Departure time for TYPE A and TYPE B combination with removed train(s) on the
Yellow Route. Only Random dwell times teleported vehicles removed
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
8 TRAIN931 18 TRAIN931 27 TRAIN931
28 TRAIN933 37 TRAIN933 46 TRAIN933
38 TRAIN934 48 TRAIN934 59 TRAIN934
58 TRAIN937 68 TRAIN937 78 TRAIN937
67 TRAIN939 77 TRAIN939 85 TRAIN939
86 TRAIN9313 97 TRAIN9313 109 TRAIN9313
102 TRAIN9315 113 TRAIN9315 125 TRAIN9315
131 TRAIN9319 143 TRAIN9319 154 TRAIN9319
149 TRAIN9324 163 TRAIN9324 173 TRAIN9324
167 TRAIN9326 179 TRAIN9326 192 TRAIN9326
189 TRAIN9333 200 TRAIN9333 212 TRAIN9333
209 TRAIN9336 219 TRAIN9336 229 TRAIN9336
219 TRAIN9337 230 TRAIN9337 241 TRAIN9337
239 TRAIN9339 249 TRAIN9339 259 TRAIN9339
247 TRAIN9342 257 TRAIN9342 266 TRAIN9342
257 TRAIN9344 265 TRAIN9344 274 TRAIN9344
277 TRAIN9345 285 TRAIN9345 294 TRAIN9345
302 TRAIN9348 311 TRAIN9348 320 TRAIN9348
311 TRAIN9349 321 TRAIN9349 331 TRAIN9349
339 TRAIN9352 349 TRAIN9352 359 TRAIN9352
Departures of combination TYPE B and TYPE A vehicle WHEN dwell time is passenger based
Mzimhlophe1 NewCanada3 Longdale2
88
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
8 TRAIN931 18 TRAIN931 27 TRAIN931
28 TRAIN933 37 TRAIN933 46 TRAIN933
38 TRAIN934 48 TRAIN934 59 TRAIN934
58 TRAIN937 68 TRAIN937 78 TRAIN937
67 TRAIN939 77 TRAIN939 85 TRAIN939
86 TRAIN9313 97 TRAIN9313 109 TRAIN9313
102 TRAIN9315 113 TRAIN9315 125 TRAIN9315
131 TRAIN9319 143 TRAIN9319 154 TRAIN9319
149 TRAIN9324 154 TRAIN9321 165 TRAIN9321
167 TRAIN9326 163 TRAIN9324 173 TRAIN9324
189 TRAIN9333 179 TRAIN9326 192 TRAIN9326
209 TRAIN9336 190 TRAIN9327 202 TRAIN9327
219 TRAIN9337 200 TRAIN9333 212 TRAIN9333
239 TRAIN9339 219 TRAIN9336 229 TRAIN9336
247 TRAIN9342 230 TRAIN9337 241 TRAIN9337
257 TRAIN9344 249 TRAIN9339 259 TRAIN9339
277 TRAIN9345 257 TRAIN9342 266 TRAIN9342
302 TRAIN9348 265 TRAIN9344 274 TRAIN9344
311 TRAIN9349 285 TRAIN9345 294 TRAIN9345
339 TRAIN9352 311 TRAIN9348 320 TRAIN9348
321 TRAIN9349 331 TRAIN9349
349 TRAIN9352 359 TRAIN9352
Departures of combination TYPE B and TYPE A vehicle WHEN dwell time is passenger based
Mzimhlophe1 NewCanada3 Longdale2
Table 29: Departure time for TYPE A and TYPE B combination with removed train(s) on the Blue
Route. Only Random dwell times
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
21 TRAIN972 30 TRAIN972 40 TRAIN972
37 TRAIN975 45 TRAIN975 54 TRAIN975
60 TRAIN978 68 TRAIN978 76 TRAIN978
73 TRAIN9711 81 TRAIN9711 90 TRAIN9711
81 TRAIN9712 89 TRAIN9712 98 TRAIN9712
107 TRAIN9717 114 TRAIN9717 123 TRAIN9717
118 TRAIN9718 127 TRAIN9718 136 TRAIN9718
139 TRAIN9722 148 TRAIN9722 158 TRAIN9722
146 TRAIN9723 155 TRAIN9723 164 TRAIN9723
176 TRAIN9729 184 TRAIN9729 194 TRAIN9729
193 TRAIN9734 201 TRAIN9734 210 TRAIN9734
239 TRAIN9741 248 TRAIN9741 259 TRAIN9741
246 TRAIN9743 255 TRAIN9743 265 TRAIN9743
299 TRAIN9747 307 TRAIN9747 317 TRAIN9747
315 TRAIN9750 324 TRAIN9750 335 TRAIN9750
Departures of combination TYPE B and TYPE A vehicle WHEN dwell time is passenger based
Mzimhlophe2 NewCanada4 Crown2
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
21 TRAIN972 30 TRAIN972 40 TRAIN972
37 TRAIN975 45 TRAIN975 54 TRAIN975
60 TRAIN978 68 TRAIN978 76 TRAIN978
73 TRAIN9711 81 TRAIN9711 90 TRAIN9711
81 TRAIN9712 89 TRAIN9712 98 TRAIN9712
107 TRAIN9717 114 TRAIN9717 123 TRAIN9717
118 TRAIN9718 127 TRAIN9718 136 TRAIN9718
139 TRAIN9722 148 TRAIN9722 158 TRAIN9722
146 TRAIN9723 155 TRAIN9723 164 TRAIN9723
176 TRAIN9729 184 TRAIN9729 194 TRAIN9729
193 TRAIN9734 193 TRAIN9730 203 TRAIN9730
239 TRAIN9741 201 TRAIN9734 210 TRAIN9734
246 TRAIN9743 248 TRAIN9741 259 TRAIN9741
299 TRAIN9747 255 TRAIN9743 265 TRAIN9743
315 TRAIN9750 307 TRAIN9747 317 TRAIN9747
412 324 TRAIN9750 335 TRAIN9750
Departures of combination TYPE B and TYPE A vehicle WHEN dwell time is passenger based
Mzimhlophe2 NewCanada4 Crown2
89
Table 30: Departure time for TYPE A and TYPE B combination with removed train(s) on the Red
Route. Only Random dwell times
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
50 TRAIN906 62 TRAIN906 74 TRAIN906
72 TRAIN9010 84 TRAIN9010 97 TRAIN9010
103 TRAIN9014 116 TRAIN9014 130 TRAIN9014
116 TRAIN9016 129 TRAIN9016 143 TRAIN9016
134 TRAIN9020 148 TRAIN9020 164 TRAIN9020
162 TRAIN9025 180 TRAIN9025 199 TRAIN9025
175 TRAIN9028 193 TRAIN9028 211 TRAIN9028
187 TRAIN9031 205 TRAIN9031 223 TRAIN9031
225 TRAIN9038 246 TRAIN9038 266 TRAIN9038
303 TRAIN9046 317 TRAIN9046 332 TRAIN9046
318 TRAIN9051 331 TRAIN9051 344 TRAIN9051
Departures of combination TYPE B and TYPE A vehicle WHEN dwell time is passenger based
Mlamlankunzi2 NewCanada2 Longdale1
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
50 TRAIN906 62 TRAIN906 74 TRAIN906
72 TRAIN9010 84 TRAIN9010 97 TRAIN9010
103 TRAIN9014 116 TRAIN9014 130 TRAIN9014
116 TRAIN9016 129 TRAIN9016 143 TRAIN9016
134 TRAIN9020 148 TRAIN9020 164 TRAIN9020
162 TRAIN9025 180 TRAIN9025 199 TRAIN9025
175 TRAIN9028 193 TRAIN9028 211 TRAIN9028
187 TRAIN9031 205 TRAIN9031 223 TRAIN9031
205 TRAIN9035 218 TRAIN9032 238 TRAIN9032
225 TRAIN9038 231 TRAIN9035 266 TRAIN9038
242 TRAIN9040 246 TRAIN9038 332 TRAIN9046
303 TRAIN9046 258 TRAIN9040 344 TRAIN9051
318 TRAIN9051 317 TRAIN9046
331 TRAIN9051
Departures of combination TYPE B and TYPE A vehicle WHEN dwell time is passenger based
Mlamlankunzi2 NewCanada2 Longdale1
90
9.2 Appendix C: Tables from results for Same Headway- Plan 2
Table 31: Departure times Yellow route when MLA-JHB is the red route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
5 TRAIN931 11 TRAIN931 17 TRAIN931
10 TRAIN934 16 TRAIN934 23 TRAIN934
16 TRAIN937 22 TRAIN937 28 TRAIN937
22 TRAIN9310 29 TRAIN9310 36 TRAIN9310
29 TRAIN9313 35 TRAIN9313 42 TRAIN9313
34 TRAIN9316 41 TRAIN9316 50 TRAIN9316
41 TRAIN9319 48 TRAIN9319 57 TRAIN9319
47 TRAIN9322 53 TRAIN9322 62 TRAIN9322
52 TRAIN9325 58 TRAIN9325 69 TRAIN9325
58 TRAIN9328 64 TRAIN9328 74 TRAIN9328
64 TRAIN9331 70 TRAIN9331 80 TRAIN9331
69 TRAIN9334 75 TRAIN9334 85 TRAIN9334
76 TRAIN9337 82 TRAIN9337 91 TRAIN9337
81 TRAIN9340 88 TRAIN9340 96 TRAIN9340
87 TRAIN9343 93 TRAIN9343 104 TRAIN9343
93 TRAIN9346 100 TRAIN9346 116 TRAIN9346
98 TRAIN9349 111 TRAIN9349 122 TRAIN9349
105 TRAIN9352 119 TRAIN9352 129 TRAIN9352
341 126 TRAIN9355 135 TRAIN9355
132 TRAIN9358 141 TRAIN9358
138 TRAIN9361 151 TRAIN9361
147 TRAIN9364 157 TRAIN9364
154 TRAIN9367 165 TRAIN9367
160 TRAIN9370 171 TRAIN9370
168 TRAIN9373 184 TRAIN9373
179 TRAIN9376 194 TRAIN9376
188 TRAIN9379 200 TRAIN9382
194 TRAIN9385 206 TRAIN9379
200 TRAIN9388 211 TRAIN9385
210 TRAIN9391 217 TRAIN9388
218 TRAIN93100 223 TRAIN9391
223 TRAIN9394 231 TRAIN93100
232 TRAIN9397 236 TRAIN9394
237 TRAIN93103 243 TRAIN9397
242 TRAIN93106 249 TRAIN93103
251 TRAIN93112 255 TRAIN93106
258 TRAIN93109 262 TRAIN93112
267 TRAIN93115 269 TRAIN93118
274 TRAIN93121 275 TRAIN93109
283 TRAIN93124 281 TRAIN93115
288 TRAIN93127 287 TRAIN93121
296 TRAIN93130 294 TRAIN93124
301 TRAIN93133 301 TRAIN93127
302 309 TRAIN93130
309 TRAIN93136 315 TRAIN93133
317 TRAIN93139 322 TRAIN93136
330 TRAIN93142 338 TRAIN93139
341 TRAIN93154
Departures of TYPE A vehicle WHEN dwell time is passenger based
Mzimhlophe1 NewCanada3 Longdale2
91
Table 32: Departure times red route when MLA-JHB is the red route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
6 TRAIN903 12 TRAIN903 19 TRAIN903
12 TRAIN906 19 TRAIN909 28 TRAIN909
19 TRAIN9012 26 TRAIN906 34 TRAIN906
28 TRAIN9018 31 TRAIN9012 41 TRAIN9012
44 TRAIN9027 37 TRAIN9015 47 TRAIN9015
51 TRAIN9030 45 TRAIN9018 55 TRAIN9024
61 TRAIN9036 51 TRAIN9021 68 TRAIN9027
90 TRAIN9051 57 TRAIN9027 74 TRAIN9030
100 TRAIN9057 65 TRAIN9030 80 TRAIN9033
108 TRAIN9063 71 TRAIN9033 88 TRAIN9036
120 TRAIN9069 78 TRAIN9036 95 TRAIN9042
127 TRAIN9075 85 TRAIN9042 103 TRAIN9039
138 TRAIN9081 91 TRAIN9039 116 TRAIN9054
150 TRAIN9087 98 TRAIN9045 130 TRAIN9057
172 TRAIN9099 105 TRAIN9051 137 TRAIN9060
184 TRAIN90105 117 TRAIN9057 215 TRAIN90102
203 TRAIN90117 124 TRAIN9060 221 TRAIN9099
215 TRAIN90123 130 TRAIN9063 228 TRAIN90111
226 TRAIN90129 137 TRAIN9072 234 TRAIN90105
232 TRAIN90132 143 TRAIN9075 300 TRAIN90156
239 TRAIN90138 152 TRAIN9078
251 TRAIN90144 159 TRAIN9084
270 TRAIN90153 165 TRAIN9081
282 TRAIN90162 172 TRAIN9087
294 TRAIN90168 183 TRAIN9093
306 TRAIN90174 196 TRAIN90102
318 TRAIN90180 205 TRAIN9099
330 TRAIN90186 212 TRAIN90105
337 TRAIN90192 227 TRAIN90108
239 TRAIN90114
245 TRAIN90123
255 TRAIN90129
264 TRAIN90132
271 TRAIN90138
278 TRAIN90144
290 TRAIN90147
305 TRAIN90153
312 TRAIN90159
324 TRAIN90168
336 TRAIN90174
Departures of TYPE B vehicle WHEN dwell time is passenger based
Mlamlankunzi2 NewCanada2 Longdale1
92
Table 33: Departure times red route when MLA-JHB when the dwell time is random
mlamla1 mlamla1 newcanada2 newcanada2 longdale1 longdale1
6 TRAIN903 12 TRAIN903 19 TRAIN903
12 TRAIN906 19 TRAIN909 28 TRAIN909
19 TRAIN9012 26 TRAIN906 34 TRAIN906
28 TRAIN9018 31 TRAIN9012 41 TRAIN9012
44 TRAIN9027 37 TRAIN9015 47 TRAIN9015
51 TRAIN9030 45 TRAIN9018 55 TRAIN9024
61 TRAIN9036 51 TRAIN9021 68 TRAIN9027
90 TRAIN9051 57 TRAIN9027 74 TRAIN9030
100 TRAIN9057 65 TRAIN9030 80 TRAIN9033
108 TRAIN9063 71 TRAIN9033 88 TRAIN9036
120 TRAIN9069 78 TRAIN9036 95 TRAIN9042
127 TRAIN9075 85 TRAIN9042 103 TRAIN9039
138 TRAIN9081 91 TRAIN9039 116 TRAIN9054
150 TRAIN9087 98 TRAIN9045 130 TRAIN9057
172 TRAIN9099 105 TRAIN9051 137 TRAIN9060
184 TRAIN90105 117 TRAIN9057 215 TRAIN90102
203 TRAIN90117 124 TRAIN9060 221 TRAIN9099
215 TRAIN90123 130 TRAIN9063 228 TRAIN90111
226 TRAIN90129 137 TRAIN9072 234 TRAIN90105
232 TRAIN90132 143 TRAIN9075 300 TRAIN90156
239 TRAIN90138 152 TRAIN9078 340
251 TRAIN90144 159 TRAIN9084
270 TRAIN90153 165 TRAIN9081
282 TRAIN90162 172 TRAIN9087
294 TRAIN90168 183 TRAIN9093
306 TRAIN90174 196 TRAIN90102
318 TRAIN90180 205 TRAIN9099
330 TRAIN90186 206
337 TRAIN90192 212 TRAIN90105
340 227 TRAIN90108
239 TRAIN90114
245 TRAIN90123
255 TRAIN90129
264 TRAIN90132
271 TRAIN90138
278 TRAIN90144
290 TRAIN90147
305 TRAIN90153
312 TRAIN90159
324 TRAIN90168
336 TRAIN90174
340 TRAIN90183
Departure times when the dwell time is passenger dependent. For
combination of vehicles
93
Table 34: Departure times Blue route when MLA-JHB is the red route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
5 TRAIN972 10 TRAIN972 17 TRAIN972
9 TRAIN975 15 TRAIN975 21 TRAIN975
14 TRAIN978 20 TRAIN978 26 TRAIN978
20 TRAIN9711 25 TRAIN9711 32 TRAIN9711
25 TRAIN9714 31 TRAIN9714 38 TRAIN9714
31 TRAIN9717 36 TRAIN9717 43 TRAIN9717
36 TRAIN9720 41 TRAIN9720 48 TRAIN9720
41 TRAIN9723 46 TRAIN9723 53 TRAIN9723
46 TRAIN9726 51 TRAIN9726 58 TRAIN9726
51 TRAIN9729 57 TRAIN9729 64 TRAIN9729
57 TRAIN9732 62 TRAIN9732 69 TRAIN9732
62 TRAIN9735 68 TRAIN9735 74 TRAIN9735
67 TRAIN9738 72 TRAIN9738 79 TRAIN9738
72 TRAIN9741 77 TRAIN9741 84 TRAIN9741
78 TRAIN9744 83 TRAIN9744 90 TRAIN9744
83 TRAIN9747 88 TRAIN9747 95 TRAIN9747
88 TRAIN9750 93 TRAIN9750 99 TRAIN9750
92 TRAIN9753 98 TRAIN9753 104 TRAIN9753
97 TRAIN9756 102 TRAIN9756 109 TRAIN9756
102 TRAIN9759 107 TRAIN9759 114 TRAIN9759
107 TRAIN9762 112 TRAIN9762 119 TRAIN9762
112 TRAIN9765 118 TRAIN9765 124 TRAIN9765
117 TRAIN9768 123 TRAIN9768 129 TRAIN9768
123 TRAIN9771 128 TRAIN9771 135 TRAIN9771
128 TRAIN9774 135 TRAIN9774 142 TRAIN9774
134 TRAIN9777 140 TRAIN9777 147 TRAIN9777
139 TRAIN9780 145 TRAIN9780 151 TRAIN9780
144 TRAIN9783 150 TRAIN9783 156 TRAIN9783
150 TRAIN9786 156 TRAIN9786 163 TRAIN9786
155 TRAIN9789 161 TRAIN9789 167 TRAIN9789
160 TRAIN9792 165 TRAIN9792 172 TRAIN9792
165 TRAIN9795 171 TRAIN9795 177 TRAIN9795
170 TRAIN9798 176 TRAIN9798 182 TRAIN9798
175 TRAIN97101 181 TRAIN97101 187 TRAIN97101
180 TRAIN97104 186 TRAIN97104 192 TRAIN97104
186 TRAIN97107 191 TRAIN97107 198 TRAIN97107
190 TRAIN97110 196 TRAIN97110 202 TRAIN97110
196 TRAIN97113 201 TRAIN97113 207 TRAIN97113
201 TRAIN97116 206 TRAIN97116 212 TRAIN97116
205 TRAIN97119 211 TRAIN97119 217 TRAIN97119
211 TRAIN97122 216 TRAIN97122 223 TRAIN97122
216 TRAIN97125 221 TRAIN97125 228 TRAIN97125
221 TRAIN97128 227 TRAIN97128 233 TRAIN97128
227 TRAIN97131 232 TRAIN97131 239 TRAIN97131
232 TRAIN97134 238 TRAIN97134 245 TRAIN97134
238 TRAIN97137 244 TRAIN97137 250 TRAIN97137
243 TRAIN97140 249 TRAIN97140 256 TRAIN97140
248 TRAIN97143 253 TRAIN97143 260 TRAIN97143
254 TRAIN97146 260 TRAIN97146 266 TRAIN97146
259 TRAIN97149 265 TRAIN97149 272 TRAIN97149
264 TRAIN97152 270 TRAIN97152 276 TRAIN97152
270 TRAIN97155 275 TRAIN97155 282 TRAIN97155
275 TRAIN97158 281 TRAIN97158 288 TRAIN97158
281 TRAIN97161 287 TRAIN97161 293 TRAIN97161
286 TRAIN97164 292 TRAIN97164 298 TRAIN97164
291 TRAIN97167 297 TRAIN97167 304 TRAIN97167
297 TRAIN97170 302 TRAIN97170 309 TRAIN97170
302 TRAIN97173 308 TRAIN97173 315 TRAIN97173
Departures of TYPE A vehicle WHEN dwell time is passenger based
Mzimhlophe2 NewCanada4 Crown2
94
Table 35: Departure times Blue route when MLA-JHB is the magenta route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
5 TRAIN972 10 TRAIN972 17 TRAIN972
9 TRAIN975 15 TRAIN975 21 TRAIN975
14 TRAIN978 20 TRAIN978 26 TRAIN978
20 TRAIN9711 25 TRAIN9711 32 TRAIN9711
25 TRAIN9714 31 TRAIN9714 38 TRAIN9714
31 TRAIN9717 36 TRAIN9717 43 TRAIN9717
36 TRAIN9720 41 TRAIN9720 48 TRAIN9720
41 TRAIN9723 46 TRAIN9723 53 TRAIN9723
46 TRAIN9726 51 TRAIN9726 58 TRAIN9726
51 TRAIN9729 57 TRAIN9729 64 TRAIN9729
57 TRAIN9732 62 TRAIN9732 69 TRAIN9732
62 TRAIN9735 68 TRAIN9735 74 TRAIN9735
67 TRAIN9738 72 TRAIN9738 79 TRAIN9738
72 TRAIN9741 77 TRAIN9741 84 TRAIN9741
78 TRAIN9744 83 TRAIN9744 90 TRAIN9744
83 TRAIN9747 88 TRAIN9747 95 TRAIN9747
88 TRAIN9750 93 TRAIN9750 99 TRAIN9750
92 TRAIN9753 98 TRAIN9753 104 TRAIN9753
97 TRAIN9756 102 TRAIN9756 109 TRAIN9756
102 TRAIN9759 107 TRAIN9759 114 TRAIN9759
107 TRAIN9762 112 TRAIN9762 119 TRAIN9762
112 TRAIN9765 118 TRAIN9765 124 TRAIN9765
117 TRAIN9768 123 TRAIN9768 129 TRAIN9768
123 TRAIN9771 128 TRAIN9771 135 TRAIN9771
128 TRAIN9774 135 TRAIN9774 142 TRAIN9774
134 TRAIN9777 140 TRAIN9777 147 TRAIN9777
139 TRAIN9780 145 TRAIN9780 151 TRAIN9780
144 TRAIN9783 150 TRAIN9783 156 TRAIN9783
150 TRAIN9786 156 TRAIN9786 163 TRAIN9786
155 TRAIN9789 161 TRAIN9789 167 TRAIN9789
160 TRAIN9792 165 TRAIN9792 172 TRAIN9792
165 TRAIN9795 171 TRAIN9795 177 TRAIN9795
170 TRAIN9798 176 TRAIN9798 182 TRAIN9798
175 TRAIN97101 181 TRAIN97101 187 TRAIN97101
180 TRAIN97104 186 TRAIN97104 192 TRAIN97104
186 TRAIN97107 191 TRAIN97107 198 TRAIN97107
190 TRAIN97110 196 TRAIN97110 202 TRAIN97110
196 TRAIN97113 201 TRAIN97113 207 TRAIN97113
201 TRAIN97116 206 TRAIN97116 212 TRAIN97116
205 TRAIN97119 211 TRAIN97119 217 TRAIN97119
211 TRAIN97122 216 TRAIN97122 223 TRAIN97122
216 TRAIN97125 221 TRAIN97125 228 TRAIN97125
221 TRAIN97128 227 TRAIN97128 233 TRAIN97128
227 TRAIN97131 232 TRAIN97131 239 TRAIN97131
232 TRAIN97134 238 TRAIN97134 245 TRAIN97134
238 TRAIN97137 244 TRAIN97137 250 TRAIN97137
243 TRAIN97140 249 TRAIN97140 256 TRAIN97140
248 TRAIN97143 253 TRAIN97143 260 TRAIN97143
254 TRAIN97146 260 TRAIN97146 266 TRAIN97146
259 TRAIN97149 265 TRAIN97149 272 TRAIN97149
264 TRAIN97152 270 TRAIN97152 276 TRAIN97152
270 TRAIN97155 275 TRAIN97155 282 TRAIN97155
275 TRAIN97158 281 TRAIN97158 288 TRAIN97158
281 TRAIN97161 287 TRAIN97161 293 TRAIN97161
286 TRAIN97164 292 TRAIN97164 298 TRAIN97164
291 TRAIN97167 297 TRAIN97167 304 TRAIN97167
297 TRAIN97170 302 TRAIN97170 309 TRAIN97170
302 TRAIN97173 308 TRAIN97173 315 TRAIN97173
307 TRAIN97176 313 TRAIN97176 319 TRAIN97176
313 TRAIN97179 318 TRAIN97179 325 TRAIN97179
318 TRAIN97182 324 TRAIN97182 331 TRAIN97182
323 TRAIN97185 328 TRAIN97185 334 TRAIN97185
328 TRAIN97188 334 TRAIN97188 340 TRAIN97188
Departures of TYPE A vehicle WHEN dwell time is passenger based
Mzimhlophe2 NewCanada4 Crown2
95
Table 36: Departure times Yellow route when MLA-JHB is the magenta route
Depart Time(min) Train number Depart Time(min) Train number Depart Time(min) Train number
5 TRAIN931 11 TRAIN931 17 TRAIN931
10 TRAIN934 16 TRAIN934 23 TRAIN934
16 TRAIN937 22 TRAIN937 28 TRAIN937
22 TRAIN9310 29 TRAIN9310 36 TRAIN9310
29 TRAIN9313 35 TRAIN9313 42 TRAIN9313
34 TRAIN9316 41 TRAIN9316 47 TRAIN9316
41 TRAIN9319 48 TRAIN9319 55 TRAIN9319
47 TRAIN9322 53 TRAIN9322 59 TRAIN9322
52 TRAIN9325 58 TRAIN9325 65 TRAIN9325
58 TRAIN9328 64 TRAIN9328 70 TRAIN9328
64 TRAIN9331 70 TRAIN9331 76 TRAIN9331
69 TRAIN9334 75 TRAIN9334 82 TRAIN9334
76 TRAIN9337 82 TRAIN9337 89 TRAIN9337
81 TRAIN9340 88 TRAIN9340 94 TRAIN9340
87 TRAIN9343 93 TRAIN9343 100 TRAIN9343
93 TRAIN9346 99 TRAIN9346 105 TRAIN9346
98 TRAIN9349 105 TRAIN9349 111 TRAIN9349
105 TRAIN9352 112 TRAIN9352 119 TRAIN9352
111 TRAIN9355 118 TRAIN9355 125 TRAIN9355
117 TRAIN9358 123 TRAIN9358 129 TRAIN9358
123 TRAIN9361 130 TRAIN9361 136 TRAIN9361
129 TRAIN9364 135 TRAIN9364 141 TRAIN9364
135 TRAIN9367 142 TRAIN9367 149 TRAIN9367
141 TRAIN9370 148 TRAIN9370 155 TRAIN9370
147 TRAIN9373 154 TRAIN9373 160 TRAIN9373
153 TRAIN9376 160 TRAIN9376 167 TRAIN9376
159 TRAIN9379 165 TRAIN9379 171 TRAIN9379
165 TRAIN9382 172 TRAIN9382 179 TRAIN9382
171 TRAIN9385 178 TRAIN9385 185 TRAIN9385
178 TRAIN9388 184 TRAIN9388 191 TRAIN9388
184 TRAIN9391 191 TRAIN9391 198 TRAIN9391
190 TRAIN9394 196 TRAIN9394 203 TRAIN9394
196 TRAIN9397 202 TRAIN9397 208 TRAIN9397
202 TRAIN93100 208 TRAIN93100 214 TRAIN93100
208 TRAIN93103 215 TRAIN93103 222 TRAIN93103
214 TRAIN93106 221 TRAIN93106 227 TRAIN93106
221 TRAIN93109 228 TRAIN93109 235 TRAIN93109
227 TRAIN93112 235 TRAIN93112 242 TRAIN93112
233 TRAIN93115 241 TRAIN93115 247 TRAIN93115
240 TRAIN93118 247 TRAIN93118 254 TRAIN93118
245 TRAIN93121 253 TRAIN93121 259 TRAIN93121
251 TRAIN93124 259 TRAIN93124 266 TRAIN93124
258 TRAIN93127 265 TRAIN93127 272 TRAIN93127
264 TRAIN93130 272 TRAIN93130 278 TRAIN93130
270 TRAIN93133 278 TRAIN93133 285 TRAIN93133
276 TRAIN93136 284 TRAIN93136 291 TRAIN93136
281 TRAIN93139 290 TRAIN93139 296 TRAIN93139
287 TRAIN93142 296 TRAIN93142 302 TRAIN93142
293 TRAIN93145 303 TRAIN93145 310 TRAIN93145
299 TRAIN93148 309 TRAIN93148 316 TRAIN93148
305 TRAIN93151 316 TRAIN93151 323 TRAIN93151
311 TRAIN93154 322 TRAIN93154 329 TRAIN93154
318 TRAIN93157 329 TRAIN93157 336 TRAIN93157
324 TRAIN93160 335 TRAIN93160 342 TRAIN93160
Departures of TYPE A vehicle WHEN dwell time is passenger based
Mzimhlophe1 NewCanada3 Longdale2
TRITA TRITA-ABE-MBT 20-670
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