Flow Characteristics of Heterogeneous

Traffic with and without Adherence to Lane

Following

G. Sarishka, A. Gowri and R. Sivanandan

Centre of Excellence in Urban Transport

Transportation Engineering Division

Department of Civil Engineering

IIT Madras, Chennai

Urban Mobility India Research Symposium

Dec 5, 2012

Introduction

Homogeneous traffic

Lane-following

Disciplined movement

Heterogeneous traffic

Highly varying static and dynamic characteristics

Difficult to impose lane discipline

Vehicles occupy any lateral position on the

available road space

Growth of vehicular traffic has resulted in low

speeds, excess travel times, delays and safety-

related traffic problems

2

Motivation

Different types of vehicles moving on the same

road may enjoy different levels of service (e.g. Two

wheelers)

Complex traffic scenario poses a serious challenge

to traffic planners and engineers

Solutions can be obtained through systematic

study of relevant characteristics of mixed traffic

3

Objectives

To compare the lane following and non-lane

following characteristics of heterogeneous traffic

for different lane facility types (two-, three- and

four-lane)

To compare the capacities and speeds of different

lane facility types by varying composition, flow

levels and lane widths

4

Literature Review

Different simulation models were developed for

heterogeneous traffic conditions (Oketch (2000) , Cho &

Wu (2004), Arasan & Koshy (2005), Mallikarjuna & Rao

(2007), Dey et al. (2008) )

Limited work were done for comparing capacities of

different lane facility types - heterogeneous traffic

system follow lane discipline (Chandra & Kumar (2003),

Arasan et al. (2009), Thomas et al. (2011), Gowri et al.

(2012) )

This work is a further attempt in this direction; it

compares the capacities of lane following and non-lane

following scenarios through simulation models

5

Development of Simulation Model

On most Indian roads, vehicles move freely based

on availability of space and ignore lane discipline

Smaller vehicles often weave through gaps

between larger vehicles

These features of traffic flow and wide variations in

vehicular characteristics are incorporated in the

developed simulation model (Gowri et al., 2009;

Gowri, 2011).

This model is intended to simulate the

heterogeneous traffic flow at mid-block section (two

lanes) of an urban street

6

Logics used in Simulation Model

Logics in simulation model for non-lane following

scenario

Vehicle Generation

Vehicle Placement

Vehicle Movement

Object Oriented Programming concepts

Implemented in C++ programming language

(Gowri et al., 2009)

7

Vehicle Generation

• Vehicle enter the system based on time interval (time

gap distribution)

• Time gap distributions for different categories based

on lead-lag vehicle (e.g. TW-TW, TW-Car, etc.) are

given as input to the simulation model

• Generated vehicle is assigned a free speed as per

the normal distribution based on field data

• Type of vehicle is assigned based on traffic

composition observed in field

8

Vehicle Placement

• Vehicle placement is based on availability of

transverse and longitudinal spaces

• Vehicle looks for spaces from right edge to left

edge of the road stretch

• If spaces are sufficient, vehicle will be placed on

the road stretch

• If not, it reduces its speed to that of its leader (car

following rule)

W3

Curb

Vehicle 3

Vehicle 2

Vehicle 1 Median

L 3

L 2

L 1 W1

W2

9

Vehicle Movement

If there is no slow vehicle in

front of it, vehicle accelerates

up to its free speed.

If not, overtaking logic is invoked

Overtaking vehicle looks for availability of transverse

and longitudinal spaces on the right/left side of the

overtaken vehicle

If spacings are inadequate on both the sides, car

following logic is involved

10

Modifications to the Simulation Program

In order to simulate lane following by vehicles and

to accommodate multiple lane scenarios, program

was modified appropriately

Lane following scenario

Vehicles are placed in such a way that centre line of

the vehicle and centre line of the lane match.

Overtaking is allowed but movements between lanes

are not allowed.

Vehicles are allowed to overtake from the left as well

as right

Increase in lane widths to reflect multiple lanes

(three and four) were incorporated

11

Ashok Nagar Mid-block Section, Chennai

12

Data Collection

Road width – 8.2 m

Total volume – 4000 veh/h

Two Wheeler composition is dominant (70%)

Model Validation

Parameter used for validation – Speeds of different

types of vehicles

Speeds of vehicles for one hour peak period is

obtained from field and simulation model

Simulated values are not statistically different from

observed values, indicating the validity of the

developed model

13

Model Application

• Study of influence of lane discipline on speed-flow

relationships by varying

Compositions

Flow levels [500 veh/h to capacity (simulation

model)]

Number of lanes (two, three and four lanes)

• Different compositions used:

Composition 1 - 70% TW and 19% cars

Composition 2 - 19% TW and 70% cars

Composition 3 - 45% TW and 44% cars

14

Composition 1 (TW Dominant)

15

5

10

15

20

25

30

35

40

45

50

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000

Sp

eed

(km

ph

)

Flow (veh/h)

Two Lane Road - Without Lane Discipline Two Lane Road - With Lane Discipline

Three Lane Road - Without Lane Discipline Three Lane Road - With Lane Discipline

Four Lane Road - Without Lane Discipline Four Lane Road - With Lane Discipline

• Capacities (in veh/h) are

higher in the traffic stream for

both lane following and non-

lane following cases.

• Due to high seepage

(between other vehicles) of two

wheelers due to their smaller

size and higher

manoeuverability

Composition 2 (Car Dominant)

16

5

10

15

20

25

30

35

40

45

50

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000

Sp

eed

(km

ph

)

Flow (veh/h)

Two Lane road - Without Lane Discipline Two Lane Road - With Lane Discipline

Three Lane Road - Without Lane Discipline Three Lane Road - With Lane Discipline

Four Lane Road - Without Lane Discipline Four Lane Road - With Lane Discipline

• Capacities (veh/h) are lesser

in the traffic stream compared

to that of two wheeler dominant

composition

•Due to the presence of higher

proportion of cars vis-a-vis two

wheelers.

Composition 3 (TW and Car almost equal)

17

5

10

15

20

25

30

35

40

45

50

500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000

Sp

eed

(km

ph

)

Flow (veh/h)

Two Lane road - Without Lane Discipline Two Lane Road - With Lane Discipline

Three Lane Road - Without Lane Dicipline Three Lane Road - With Lane Discipline

Four Lane Road - Without Lane Discipline Four Lane Road - With Lane Discipline

• Capacities (in veh/h) are

generally higher than car

dominant composition

(Composition 2) and lesser

than two wheeler dominant

composition (Composition 1)

Capacity difference between Lane Following (LF) and Non-Lane

Following (NLF) for various lane widths and compositions

18

Lane

Width

Composition 1

(70% TW, 19% Cars)

Composition 2

(19% TW, 70% Cars)

Composition 3

(44% TW, 45% Cars)

Capacity PCU/h (veh/h) Capacity PCU/h(veh/h) Capacity PCU/h(veh/h)

NLF LF Diff. NLF LF Diff. NLF LF Diff.

Two- lane

road

6343

(7356)

5067

(5876)

1276

(1480)

7226

(7300)

5768

(5827)

1458

(1473)

6803

(7336)

5415

(5839)

1388

(1497)

Three-lane

road

8839

(10250)

5962

(6914)

2877

(3336)

10314

(10238)

6529

(6596)

3785

(3642)

9502

(10246)

6258

(6748)

3244

(3498)

Four- lane

road

11101

(12873)

9082

(10532)

2019

(2341)

12250

(12375)

9964

(10066)

2286

(2309)

11920

(12854)

9692

(10451)

2228

(2403)

Comparison of stream speeds (at capacities) of Lane

Following and Non-Lane Following cases for various lane

widths and compositions

19

Lane

Width

Composition 1

(70% TW, 19% Cars)

Composition 2

(19% TW, 70% Cars)

Composition 3

(44% TW, 45% Cars)

Speed km/h Speed km/h Speed km/h

NLF LF Diff. NLF LF Diff. NLF LF Diff.

Two- lane

road 31 24 7 31.4 24.5 6.9 31 24 7

Three-lane

road 32 25 7 32 23.6 8.4 32 24 8

Four- lane

road 29 24 5 32 24.8 7.2 29 24 5

Conclusions

This study focuses on comparison of capacities and

corresponding speeds under heterogeneous traffic

with and without lane adherence for various

combinations of vehicular compositions

An existing microscopic traffic simulation model was

used

Simulation runs were carried out for various

combinations of vehicular composition, volume levels,

number of lanes and lane following/non-lane following

scenarios

20

Conclusions (contd..)

Non-adherence to lanes results in higher mid-block

capacities vis-à-vis the case of lane following

Capacity decreases with the increase in the cars in

the total composition when it is measured in veh/h but

increases with increase in cars when it is measured

in PCU/h, for both lane following and non-lane

following cases, in all the road cases, i.e. two, three

and four lanes

While these findings point to higher capacities for

non-lane following cases, the issue of compromise on

safety must be kept in mind

21

Conclusions (contd..)

More case studies and further scenario analysis

need to be conducted to generalize the findings

The present work can be extended to include both

mid-block and intersection to form a linear section,

which would serve as measure of performance of

traffic flow on a road corridor

22

Acknowledgement

The data collection effort in this project was made

possible through a project supported by funds from

Ministry of Urban Development (MoUD), GoI, through

their sponsorship of Centre of Excellence in Urban

Transport at IIT Madras

23

References

Arasan, V.T. & Koshy, R.Z. (2005). Methodology for modeling highly heterogeneous traffic, Journal of

Transportation Engineering, ASCE, 131(7), 544-551.

Arasan, V.T., Vedagiri, P. & Manu, S. (2009). Development of Speed-Flow Relationship for

Heterogeneous Traffic Using Computer Simulation, Journal of Institution of Engineers (India), 89, 3-9.

Chandra, S. & Kumar, U. (2003).Effect of Lane Width on Capacity under Mixed Traffic Conditions,

Journal of Transportation Engineering, ASCE, 129,155-160.

Cho, H. & Wu, Y. (2004). Modeling and Simulation of Motorcycle Traffic Flow, Proceedings of the IEEE

International Conference on Systems, Man and Cybernetics. The Hague, Netherlands.

Dey, P. P., Chandra, S. & Gangopadhyay, S. (2008).Simulation of mixed traffic flow on two-lane roads,

Journal of Transportation Engineering, ASCE, 134, 361-369.

Gowri Asaithambi, Venkatesan Kanagaraj & Sivanandan, R. (2009). Object-oriented methodology for

intersection simulation model under heterogeneous traffic conditions, Advances in Engineering Software

(Elsevier), 40 (10), 1000-1010.

Gowri, A. (2011). Evaluation of turn lanes at signalized intersections under heterogeneous traffic

conditions using microsimulation models, Ph.D Thesis, Indian Institute of Technology Madras, Chennai,

India.

Gowri Asaithambi, Venkatesan Kanagaraj, Karthik K. Srinivasan & Sivanandan, R.(2012).Mixed

Traffic Characteristics on Urban Arterials with Significant Motorized Two-Wheeler Volumes: Role of

Composition, Intra-Class Variability, and Lack of Lane Discipline, Transportation Research Record,

Transportation Research Board, Washington D.C., USA (Article in Press).

Mallikarjuna, Ch. & Rao, K. R. (2007) Identification of suitable cellular automata model for mixed traffic,

Journal of the Eastern Asia Society for Transportation Studies, 7, 2454-2469.

Oketch, T. M. (2000). New modeling approach for mixed-traffic streams with nonmotorized vehicles,

Transportation Research Record1674, Transportation Research Board, Washington, D.C., 86-93.

Thomas, J., Karthik K. Srinivasan & Arasan,V. T.(2011). Multi-class volume delay functions for

heterogeneous traffic applications, Journal of Road Transport, 2, 40-54.

24