WP5 - Estimated Time of Arrival: verification of the F-Man approach and identification of effects on fleet management Athens -Greece 23-25 Sept 2004 National

WP5 - Estimated Time of Arrival: verification of the F-Man approach and

identification of effects on fleet management

Athens -Greece 23-25 Sept 2004

National Technical University of Athens

SCOPE OF THE CURRENT WORK


To investigate the aspects of ETA implementation in the fleet management, focusing on:

• the identification of the ETA effects in fleet management (favorable operating conditions, order of magnitude of benefits achieved through ETA implementation.

Predicting delay at final destination


Frequent real-world processA B

F-MAN approach

2:30

11:0013:30

7:50

2:30 = “ETA”

10:20

1

2

detection of a delay 2:30 at point C“Added” delay (7:50 + 2:30) = “ETA”

1

2

2:30

11:0013:30 7:50

3:05

10:55

1

3

3 calculation of arrival delay = 3:05 = ETA

based on statistics of train delays

C

Frequent real-world processA B

F-MAN approach

2:30

11:0013:30

7:50

2:30 = “ETA”

10:20

1

2

detection of a delay 2:30 at point C“Added” delay (7:50 + 2:30) = “ETA”

1

2

2:30

11:0013:30 7:50

3:05

10:55

1

3

3 calculation of arrival delay = 3:05 = ETA

based on statistics of train delays

C

ETA: Overall approach


Delay at current station

Max delay that FM can afford

ETA P 100% Take decision

Nevertheless, this does not mean that no delay will occur

< 100%

Wait to have better information

Take the risk

Order more than one wagons to reduce the risk

i d

The F-MAN approach to ETA calculation


Time of departure from the station of origin

H2=18 h

Cum

ulat

ive

prob

abili

ty th

at th

e w

agon

wil

l ar

rive

at t

he d

esti

natio

n te

rmin

al a

t the

req

uire

d tim

e

83%

H1=11 h

17% Probability of non-shown for Fleet Manager B

42 % Probability of non-shown for Fleet Manager A

58%

A C D

+2:

00

+8:

00

+16

:00

+36

:00

22/5

/200

3

E

+22

:00

(Xa,

Ya)

(Xb,

Yb)

(Xc,

Yc)

(X

d, Y

d)

(Xe,

Ye)

(Xac

t,Y

act)

Act

ual p

ositi

on

at +

8:00

450km B

Cumulative probability for station B

Time

Fleet Manager A requires his wagon within 11 hours.

Order 1 (delayed)

H1= 11 hours

Order 2

Fleet Manager B requires his wagon within 18 hours

Order 1 (delayed)

H2= 18 hours

Time

Order 2

Figure 3: Upper part

Figure 3: Lower part

Understanding ETA effects: Simplified examples

Various examples, where the usefulness of ETA calculation, to Fleet Managers is identified, are presented below.

Example #1: It outlines a case where ETA information has no effect to total system’s output

In this example the operation period is assumed 4 days. In each day 100 wagon requests exist (constant demand) and 80 wagons are offered (e.g. return from missions and be idle/available). The term idle-wagons stands for these wagons. As demand exceeds wagon offer only 80 wagon requests (customers) can be served each day, thus totally 320 wagon requests are served during the four days period.

Understanding ETA effects: Example#1

A fleet manager is informed (through ETA information) that 10 wagons are running towards the station and these wagons will arrive in appropriate time for dispatching (these wagons are named ETA-wagons).

Following the dispatching strategy DS1, the fleet manager makes use of these 10 additional wagons, thus 90 wagon requests are served in Day1. Nevertheless, these 10 wagons are missing the next day, thus only 70 idle wagons exist. By replicating the procedure, fleet manager can swift 10 ETA-wagons from Day3 to Day2 and the following day from Day 4 to Day3 but in Day4 only 70 idle wagons exist.

Totally, 320 wagon requests are served during the four days period, the same number as without ETA information

ETA effects on the wagon system when demand is exceeds offer (Example #1)

Understanding ETA effects: Example #2

Example #2, presents the case where the wagon demand fluctuates between 70 and 130 wagons, having an average of 100 wagons per day (as in Example #1), thus occasionally wagon demand becomes lower than wagon offer (which is assumed to be 80 wagons per day). In such operating conditions, the wagon swifts (thanks to ETA information) between consecutive days allow more wagon demand to be served.


Understanding ETA effects: Example #3

Example #3, has the same operating conditions as Example #1 (constant demand of 100 wagons per day and constant offer of 80 idle wagons per day, option for 10 ETA-wagons, 4 days period) but a different dispatching strategy DS2 is applied: only 80 wagon request are served out of the 90 available (80 idle + 10 ETA-wagons). Totally, 320 wagon requests (as in Example #1 with and without ETA information) are served during the four days period. The advantage is that by selecting 80 out of 90 wagons, the fleet manager has more options thus he selects the more convenient (e.g. less distant from the station) alternatives. That means savings in total wagon-Km.


ETA effects on the wagon system when demand is lower (or equal to) than offer (Examples 4&5)

Conclusions of examples

(a) benefits concern total wagon-Km due to more wagon selection alternatives and

(b) Benefits arise when wagon offer fluctuation occasionally goes below demand

ETA effects taking into account real word operating environment

In order to have a wider picture of the ETA effects, two aspects (concerning the real-world operating environment) should be taken into account:

1. When a wagon request is not satisfied, it usually waits to be served in the following day. Nevertheless, a percentage of customers (Slovenian railways estimate that this percentage can be up to 20%) choose another transport mode, mainly a truck. For the railways, this means a direct loss (the payment is vanished) and an indirect loss (bad reputation, customers are discouraged to use the rail mode).

Therefore, when in wagon offer or demand fluctuation, the ETA information result in serving more wagon orders, such benefits should also taken into account.

ETA effects taking into account real word operating environment

2. When ETA predictions failed, direct (payment vanish) and indirect losses (bad reputation, customers discourage) are occurred which are more severe than above, as the wagon availability is initially confirmed and then cancelled. To significantly reduce such “unpleasant” cases, the fleet manager should not allocate all ETA-wagons to customers. By using the non-shown wagon probabilities, provided from the ETA method, the fleet manager can calculate the number of wagons that should be excluded from dispatching in order to reduce the above “initially confirmed and then cancelled” case in negligible limits

Quantifying wagon-Km savings due to ETA implementation

As already mentioned, one of the main benefit from ETA implementation concern total wagon-Km savings that arisen as more wagons are available during the dispatching process.

(a) The analysis required the use of a suitable simulation tool, thus on-purpose software was developed by NTUA (in VBA for Microsoft Excel, incorporating the Microsoft Solver utility). The software include wagon supply and demand generators, automatic allocation of wagon orders to demand processing and statistical elaboration, including graphic presentations, of the results. It also accommodates an ''area'' that allows for manual processing of wagon order allocations as an alternative to the above automatic processing.

(b) The technical approach and the relevant outcomes are analytically presented below.

Operating Conditions, Assumptions & User-defined variables (I)

a. The “geographical area” of the application is considered squared (e.g. 2000 Km X 2000 Km).

b. Within the geographical area, 4 stations asking for wagons (wagon requests) exist. The term “demand point” stands for these stations. The coordinates of these stations are set by the program user. Two options had investigated (a) Station properly allocated in order to equally share the geographical area. (b) deviation (see Figure 22)

c. Within the geographical area, 25 stations with idle wagons (wagon offers) exist. The X and Y coordinates of these stations are generated automatically through random numbers (following uniform distributions), thus these stations are randomly dispersed. The term “supply point” stands for these stations.

Operating Conditions, Assumptions & User-defined variables (II)

d. The mileage between supply and demand points is assumed to be the Euclidean distance between the associated stations.

e. The total number of wagon requests is set by the program user. These wagon requests are equally allocated to the demand points.

f. The total number of wagon offers is set by the program user. The wagon offers are allocated to the supply points according to the following rule: A Pi percentage is equally distributed

among supply points, while the remaining (1- Pi) percentage is

allocated randomly.

g. Each wagon request generates a transport activity (if adequate supply points exist). Wagon requests that are satisfied by wagon offers in the same station are not considered.

Example of wagon-offer and wagon-request disperses

0

500

1000

1500

2000

0 500 1000 1500 2000

C1

C3

C2

C4

0

500

1000

1500

2000

0 500 1000 1500 2000

C1

C3

C2

C4

Station were available wagons exist

Cx Stations were wagon-requests exist

Wagon orders processing: Real-world and simulation aspects

Terminal E is connected to terminal B but no wagon is idle. Idle wagons exist in terminal C but their

transport to terminal B is not assured as: Case 1: A timetable from C to B exists but no train actually runs between the stations as occasionally train

trips are not implemented due to rolling stock breakdown or due to lack of cargo. Case 2: A timetable from C to B exists does not exist but the transport between C and B is implemented by

combining the legs C to F and F to B of other timetables Case 3: A timetable from C to B does not exist, the Fleet Manager can contact the Infrastructure Manager and

the Train Operating Company in order to arrange for a train to run on purpose.

Terminal A

Terminal B requests 6 wagons for a B to C transport task

E Terminal C 15 wagons idle

D

F Existing timetable Existing timetable

Example indicating the uncertainty for wagon transport between terminals

Simulation approach (I)

For the shake of the present analysis an automatic procedure was adopted following the typical Operations Research “transportation problem”. The cost function takes into account the transport distance (maintenance cost is indirectly included as it is strongly associated to the wagon mileage). When transport distance exceeds a certain limit (e.g. the maxim distance that a wagon can cover within one day), additional cost/penalties can be applied to exclude these wagons or to compensate for the delayed customer service. In addition, and in order to consider cases where not such an advanced wagon distribution system is used, a number of cases were also solved manually following the Nearest-Neighbourhood service discipline (see below).

Simulation approach (II)

Two major remarks must be made for the above order processing mechanism:

a. The effects of the erroneous ETA predictions are not considered. A simplified way to include this element in the cost calculation is by defining a percentage of error and by applying a ''frictional'' penalty in the associated wagon non-shown cases. It must be noted that the above penalization is performed for the shake of comparison of the alternative systems and is not implemented in the Railway sector, although it is an established practice (compensation of customers) in the air sector.

Simulation approach (III)

b. The real dimensions of the imbalance between wagon demand and supply are not addressed. The introduction of ‘dummy demand ’or ‘dummy supply nodes’ solves the mathematical part of the problem, but in practice that means that either some transport requirements will be not satisfied, or that some wagons will not be used and therefore the associated owners will not be paid. These delicate issues can be solved only by fairly structured business rules. Business rules are also required for balancing F-man pool requirements versus wagon owners interests (e.g. minimum wagon dwell time in the pool), for protecting the “devoted” users versus users that are occasionally using the system (without eliminating the flexibility for occasional participation), for the fair treatment of similar users inside the pool, for rewarding “reliable” users against users causing frequent wagon delivery delays, for data confidentiality towards statistical data exploitation issues etc

Total wagon-km savings in relation to the percentage of additional wagons offered to the system due to ETA information

Comparative results of ETA effectiveness for two alternatives of the demand-stations spatial allocation

Total wagon-km covered within the 30 days simulation period for specific input variable combinations (group 1)






Conclusions (I)

The knowledge of the estimated time of arrival is valuable information for fleet managers as it allows (a) a better balance between the daily fluctuating wagon demand and offer and (b) a better wagon dispatching as more wagons are available, thus the more “convenient” wagons can be selected.

The effectiveness of dispatching is strongly related to the percentage of additional wagons that can be utilised thanks to ETA information, in relation to the wagon demand and the number of idle wagons already in the stations as well as to the qualification of dispatchers to properly utilise these additional wagons.

Conclusions (II)

Within the F-MAN project a new ETA method was developed. The verification of this method was performed by 2 ways:

1. A verification check based solely on statistical methods using data from the Portuguese railways. The data sample was split in two parts. The first part was used for the calibration of the ETA function while the second one was used for the verification test.

2. A verification check based on data/information collected through the F-MAN pilot runs where the ETA functions were calibrated through statistical information from the corridors where pilot runs take place.

Conclusions (III)

The conclusion of the first verification check was that the success rate of ETA forecast was quite high. Nevertheless, a more careful look to the results indicated that ETA systematically underestimates the subset of “Reject” cases. This is due to the fact that train delays in 2004, are significantly lower than those of 2003. This probably occurs because the operating conditions on the specific corridor favorably evolve.

The conclusion of the second verification check XXX

This part will be completed

as soon as data from the pilot runs are available.

NTUA assumes that

till the end of Sept’04, at least a small number

of data will be produced.

Documents

WP5 - Estimated Time of Arrival: verification of the F-Man approach and identification of effects on fleet management Athens -Greece 23-25 Sept 2004 National