simulation of heterogeneous traffic to derive capacity and service volume standards for urban roads

5/20/2018 Simulation of Heterogeneous Traffic to Derive Capacity and Service Volume Standards for Urban Roads

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Paper No. 500+

SIMULATION OF HETEROGENEOUS TRAFFIC TO DERIVE CAPACITY AND SERVICE VOLUME

STANDARDS FOR URBAN ROADS

By

DR. V. THAMIZH ARASAN* & REEBU ZACHARIAH KOSHY**

CONTENTS

Page

1. Introduction ... ... 220

2. Background ... ... 222

3. The Simulation Model ... ... 225

4. Model Validation ... ... 227

5. Model Application ... ... 2336. Conclusions ... ... 240

SYNOPSIS

Highway capacity is the maximum number of vehicles that can reasonably be

expected to pass a section of road in unit time under prevailing roadway, traffic

and control conditions; whereas, service-volume is the maximum number of vehicles

that can be accommodated at a specified Level of Service (LOS). The performanceof urban road networks depends on the practical capacity and actual volume of

traffic on each of the links that constitute the network. The heterogeneous traffic

existing on urban roads of developing countries like India is characterised by the

presence of vehicles of wide ranging static and dynamic characteristics. The unrestricted

movement of these vehicles on road space makes the lane concept and expression

of flow values, based on standard lane width, invalid. Also, when different types

of vehicles share the same road space without any physical segregation, the extent

of vehicular interactions varies widely with variation in traffic mix. To arrive at

an estimate of practical capacity of road links, it is necessary to study the influenceof roadway, traffic and other relevant features on vehicular movement using appropriate

techniques. Modelling of traffic flow is the widely accepted technique for studying

the flow characteristics over a wide range of the involved variables. Hence, there

is a need for development of models to replicate heterogeneous traffic flow; and

such models would be of significant assistance to traffic planners while making key

decisions. The design service volumes recommended for urban roads are for a LOS

of C (about 0.7 times the maximum capacity). Capacity and service volumes being

+ Written Comments on this Paper are invited and will be received upto 31stDecember 2004

* Professor,

** Research Scholar, } Transportation Engineering Division, Department of CivilEngineering, IIT Madras, Chennai - 600 036


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DR. ARASAN& KOSHYON220

expressed in Passenger Car Units (PCU), the PCU values for the different types of

vehicles are needed for quantifying traffic flow corresponding to LOS C.

Simulation, from microscopic through macroscopic, is increasingly

becoming a popular traffic-flow modelling tool for analysing traffic

operations and highway capacity. This paper deals with the development

and application of a heterogeneous traffic-flow simulation model to develop

capacity and service-volume standards for urban roads. The simulation

model was first validated and used to estimate PCU values of different

categories of vehicles, applicable to traffic flow at LOS C. These PCUvalues have then been used to convert heterogeneous traffic streams of

varying compositions to equivalent homogeneous (passenger-cars-only)

streams. The results were found to be consistent, establishing the credibility

of the PCU values derived using the model. Service volumes for 7.5 m and

11.0 m wide urban roadways were also arrived at, as an illustration of the

application of the model. Development of a general purpose traffic

simulation model to replicate the lane-less nature of heterogeneous traffic

flow for comprehensive study of the traffic flow characteristics, andapplication of the model to derive relatively accurate PCU values to

develop capacity standards are the unique features of the research study

presented here.

1. INTRODUCTION

With the increasing urbanization, improved transportation technology

and an expanding economy, additional roads and highways are built, in

an effort to balance roadway capacity and demand. At the same time,

traffic volumes and travel distances continue to increase, and the new

roadway facilities get filled up shortly after completion. Traffic congestion

and safety are serious problems, impacting on the economy, environment

and quality of life in our cities. In designing highways, traffic engineers

must anticipate the amount and type of traffic that will use the road, in

order to make the highway match its anticipated use. The capacity of ahighway is defined as the number of vehicles that can reasonably be

expected to pass a point or section of the highway during a given period

of time under prevailing roadway, traffic, and control conditions. Highway

capacity is usually expressed in terms of number of vehicles per hour.

Knowledge of capacity of a road is essential in planning, design and

operation of roads. Capacity refers to the rate of flow during a specified

period; and any change in the prevailing conditions results in a change

in the capacity of the facility. Also, capacity is assumed to be stochasticin nature because of differences in individual driver behaviour and changing

roadway and weather conditions (Minderhoud, et al. 1997).15


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SIMULATIONOFHETEROGENEOUSTRAFFICTODERIVECAPACITYANDSERVICEVOLUMESTANDARDSFORURBANROADS

221

The concept of level of service (LOS) describes operating conditions

within a traffic stream, and their perception by motorists and passengers.A level-of-service definition generally describes these conditions in terms

of such factors as speed, travel time, freedom to manoeuvre, traffic

interruptions, comfort and convenienced, and safety. The Hi gh wa y

Capacity Manual special report 209 of Transportation Research Board

(TRB), USA defines six levels of service. They are given letter designations

from A to F with A representing the best operating conditions and F

representing the worst. Very often, the flow reflects the quality of traffic

movement on a road. When traffic volumes approach the capacity of aroad, traffic becomes congested and the flow of traffic is considered

undesirable. LOS is thus one mechanism used to measure the quality of

traffic flow.

Another mechanism used to measure the quality of traffic flow is

the volume-to-capacity (V/C) ratio. It is a measurement of traffic service

or flow quality that compares the number of vehicles using or expected

to use a given road or segment of a road during a single hour with thenumber of vehicles that the facility is designed to handle safely in a

single hour. Use of V/C ratio for analysis allows the evaluation of potential

demand compared to the capacity of the facility in question. Volume can

never exceed the capacity of the facility, yet demand can. If the demand

for a facility is greater than the capacity, a break down situation occurs.

Highway capacity values and speed-flow relationships used for

planning, design and operation of highways, in most of the developedcountries, have been based on Manuals and Codes of practices, which

are valid for fairly homogeneous traffic comprising vehicles of more or

less uniform static and dynamic characteristics. Even under nearly

homogeneous traffic conditions, it is necessary to convert heavy vehicles

such as buses and trucks, which constitute a small proportion of traffic,

into equivalent number of a standard type of vehicle (usually passenger

cars) to measure the traffic flow using a single unit. The road traffic in

most developing countries such as India comprises vehicles of wide

ranging physical dimensions, weight and dynamic characteristics. Also,

the motorized and non-motorized vehicles share the same road space

without any segregation. The speeds of these vehicles vary from about

5 to over 100 km/h. Due to the highly varying physical dimensions and

speeds, it becomes difficult to make the vehicles to follow traffic lanes.

Consequently, they tend to choose any advantageous position on the

road based on space availability. Also, the extent of vehicular interactions

varies widely with variation in traffic mix. Vehicles, which are less mobile

in terms of manoeuvrability, cause significant level of friction to the

movement of other vehicles in the traffic stream. The extent of friction


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realized by the different categories of vehicles depends on their static

and dynamic characteristics. For example, at higher traffic volumes alarge proportion of motorized two-wheelers and bicycles may be able to

move with speeds closer to their free speeds because of their ability for

utilizing smaller gaps in the stream for movement, while the large-size

vehicles such as buses and trucks may be subjected to considerable

speed reduction. Traffic engineers account for the impact on capacity

from the different types of vehicles by assigning each class of vehicle

a passenger car equivalent (PCE or PCU) value. This value represents the

number of passenger cars that would consume the same percentage ofthe highways capacity as the vehicles under consideration under prevailing

roadway and traffic conditions. This study deals with development of a

simulation model to replicate heterogeneous traffic flow on urban roads

of developing countries such as India and the application of the model

to derive PCU values for the different types of vehicles in heterogeneous

traffic streams, and hence arrive at capacity standards.

2. BACKGROUND

There are different approaches to estimate the capacity of a road.

Fig. 1 shows the various methods, which are based on direct empirical

and indirect empirical approaches (Minderhoud et al. 1997)15. There are

four different methods available for capacity estimation under the direct

empirical approach. The observed headway models (e.g. Branstons

generalised queueing model, Beckleys semi-Poisson model, etc.) are based

on the theory that, at capacity level of flow on the road, all driver-vehicle

elements are constrained. These models can be applied only for a single

lane. In the case of multiple lane roads, the lanes are treated separately.

An example for capacity estimation technique based on observed traffic

CAPACITY ESTIMATION

DIRECT EMPIRICAL INDIRECT EMPIRICAL

Observedheadways

Observedvolumes

Observedvolumes

and speeds

Observedvolumes,

densities and

speeds

Guidelines Simulationmodels

Fig. 1. Methods of capacity estimation


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volumes is the observed extreme value method which estimates the capacity

of a road by using only known maximum traffic volumes observed overa certain period. The product limit method is an example for road capacity

estimation based on both traffic volume and speed data. On line procedures

for actual capacity estimation and the fundamental diagram method are

capacity estimation techniques based on traffic volumes, speeds and

densities. Fundamental diagram method is based on the relationships

between traffic flow, speed and density. It is sufficient to measure two of

the three variables to construct one diagram. Traffic must be observed at

different volumes to make a curve fitting possible. The capacity-estimationguidelines by TRB (HCM), Indian Roads Congress (IRC) and similar

agencies are based on indirect empirical methods using appropriate

theoretical techniques. Outputs from a traffic flow simulation model can

be used to construct fundamental diagrams of flow, thereby making it

possible to estimate the capacity of a facility. Though several methods,

as indicated (Fig. 1) are available for estimation of capacity, the microscopic

simulation models are now widely used as the most effective analytical

tool for studying the traffic problems and for assessing the effectiveness

of traffic management measures. This is because, these models, once

validated, can be used to study the traffic flow characteristics over a wide

range of values of the involved variables to get more acceptable results.

In the current Highway Capacity Manual (HCM 2000)20 procedures,

there is an implicit assumption that safety, an important measure of the

service a facility provides, is automatically considered when LOS is

specified. The notion is that the better the LOS, the safer a facility willbe and that the usual practice of designing for a median LOS of C or

D produces a desirable balance among cost, safety and operational

measures. The methodologies presented in HCM do not generally deal

with full hour volumes, but rather with equivalent hourly flow rates

during a peak 15 min interval within the analysis hour. The basic capacity

value of 2,000 PCU per hour per lane for freeways and multilane roads

thus refers to the maximum flow that could be accommodated in a 15 min.

period.

The IRC recommends that LOS C be adopted for design of urban

roads (IRC:106-1990)8. At this level, the volume of traffic will be around

0.7 times the maximum capacity, and this is taken as the design service

volume for urban roads. Maitra, et al. (1999)13 proposed 10 levels of

services with 9 in a stable flow zone (conventional LOS A to E region)

and one representing the unstable flow (presently LOS F), as a means of

quantifying congestion on urban roads. They assumed capacity valuesof study locations on urban roads as 3,500 and 4,500 PCU per hour for

road widths of 7.0 and 10.3 m respectively in one direction.


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There have been several attempts to derive PCU values applicable for

homogeneous and heterogeneous traffic environments (e.g. Huber 1982 ,Krammes and Crowly 1986, Cunagin and Messer 1983, Sumner, et al.1984,

Elefteriadou, et al. 1997, Chandra and Sikdar 2000, Tiwari, et al.

2000)6,10,4,19,5,2, & 21. There is general agreement among researchers that the

PCU of a vehicle type will decrease with increase in its own proportion

in the traffic stream, and that for a given road width, an increase in flow

level will result in smaller PCU value for a vehicle type. Recently (2003) 3,

Chandra and Kumar proposed capacity values for various road widths

under mixed traffic conditions. They used a new concept for estimatingPCU of various types of vehicles based on their projected areas on the

road surface. The PCU factors, for urban roads, recommended by IRC are

available in the IRC Code IRC:106-19908. The PCU values have been given

in the Code for two levels of traffic mix, namely the percentage composition

of a vehicle type being 5 per cent and 10 per cent and above.

There had been quite a few attempts to apply the techniques of

simulation to study the characteristics of mixed traffic. Ramanayya (1988)17

developed a simulation model to study the traffic flow for single lane one-

way, two-lane one-way, and two-lane two-way roads considering the lane

concept. Issac (1995)9 developed a mixed traffic flow simulation model for

estimation of urban road capacities and studied the effect of variation in

traffic composition. A simulation model of traffic operations on two-lane

highways was developed by Kumar and Rao (1996)11. Rajagopal and

Dhingra (2002)

16

investigated the usefulness of traffic simulation inassessment of traffic management strategies. Marwah and Singh (2000)14

did simulation studies of traffic flow on Kanpur urban roads using two-

lane one-way traffic simulation model. Arasan and Kashani (2003)1 studied

the platoon dispersal characteristics of heterogeneous traffic streams

using simulation technique. All these studies, however, are limited in

scope, and further the models developed are specific to certain roadway

and traffic conditions.

The review of literature has led to the following capacity related

observations. There is substantial variation in the capacity values estimated

by various researchers, by virtue of the variations in the roadway and

traffic conditions considered for the studies and the uncertainties associated

with mixed traffic and its characteristics. Computer simulation models can

be advantageously used to estimate capacity and PCU values of various

categories of vehicles expected in an urban heterogeneous traffic

environment. The formulation to establish PCU for a vehicle type on aparticular roadway should necessarily be based on the variables that

reflect the combination of factors contributing to the overall influence of


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the type of vehicle on the quality of service provided by the roadway.

Simulation logic of mixed traffic flow models takes care of the dimensionsof vehicles, their free speeds, acceleration characteristics, space gap

requirements in a traffic stream, etc. Hence, the speed reduction caused

to the reference vehicle (passenger cars) by the addition of a specified

number of vehicles of a particular type, is a satisfactory basis for estimating

the PCU value of that vehicle. For the planning and design of urban

traffic facilities, traffic-flow level at LOS C (V/C ratio about 0.7) is usually

recommended. Therefore, the PCU factors used for the conversion of

mixed traffic to obtain service volume standards for urban roads must bederived using such factors relevant to this flow level.

In this study, it was decided to develop a general purpose microscopic

heterogeneous traffic-flow simulation model, because, it facilitated

representation of all the relevant characteristics of mixed traffic, and

permitted the variation of all the parameters over a wider range, than what

might have been possible with field observed data.

3. THE SIMULATION MODEL

In the absence of program packages for simulating heterogeneous

traffic flow, a computer program was newly developed by the authors for

the purpose, and the program code was written in C language. The model

addresses the stochastic and dynamic nature of heterogeneous traffic

flow. It is a discrete-event traffic simulation model, using interval-scanning

technique with fixed-increment time advance. At higher traffic flow levels,

there is a chance of more vehicle arrivals during each scan interval one

second. To address this issue, a separate clock with precision (scan

interval) of 0.05 second is provided in the headway-generation-module to

generate inter arrival times with 0.05 second accuracy. The model is also

capable of showing the animation of simulated traffic movements over the

road stretch. For the purpose of modelling, the entire road space is

considered as single unit, without any lane separation. The vehicles arethen represented, with dimensions, as rectangular blocks, and their

longitudinal and lateral movements on the road surface are tracked using

a co-ordinate system. The traffic-simulation model was validated by

comparing the simulated and field observed data on a set of roads (Sardar

Patel road and GST road) in Chennai City. The modelling concepts are

only briefly explained here as the emphasis of this paper is on the

estimation of capacity and service-volume standards for urban roads. The

basic structure of the model is depicted in Fig. 2. The model, as indicatedin the figure, has three major modules, namely, Vehicle Generation, Vehicle

Placement, and Vehicle Movement. Inputs required for the model are:


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Inputs and Initialization

Generation of Vehicle Arrivals

Vehicle Movement

Start

Vehicle Placement

Is SimulationTime Over?

End

PrintOutputs

No

Yes

Fig. 2. Simulation framework


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traffic volume, composition, free speeds of different types of vehicles,

length of road stretch for simulation, width of roadway, overall dimensions(length and breadth) of different types of vehicles, acceleration and

deceleration characteristics, total simulation period, etc. For generation of

headways, free speed, etc., the model uses several random-number streams,

which are generated by specifying separate seed values. Any generated

vehicle is placed at the beginning of the simulation stretch, considering

the safe headway (which is based on the free speed assigned to the

entering vehicle), lateral gap, and the overall width of the vehicle with

lateral clearances. If the longitudinal gap is less than the minimum requiredsafe gap, the entering vehicle is assigned the speed of the leading

vehicle, and once again the check for safe gap is made. If the gap is still

insufficient to match the reduced speed of the entering vehicle, it is kept

as backlog, and its entry is shifted to the next scan interval. During every

scan interval, the vehicles remaining in the backlog will be admitted first,

before allowing the entry of a newly generated vehicle. A continuous

increase in the number of vehicles as backlog during the simulation run

indicates 100 per cent platoon condition (capacity flow) on the road

stretch. Placement and movement of non-motorized vehicles (Bicycles

and Tricycles) based on field observation, is restricted to the left most

part of the road only.

The simulation model uses the time-interval scanning technique to

update the state of the system, the chosen interval being one second.

During each scan interval, the positions of all vehicles in the system are

updated using the formulated movement logic. The movement logic alsoincludes the overtaking and car-following logics as applicable to

heterogeneous traffic. The model measures the speed maintained by each

vehicle when it traverses a given reference length of roadway which is

specified by the user, in addition to the various other flow characteristics

of interest. Fig. 3 displays a snapshot of animation screen while simulating

mixed traffic in one direction on a 7.5 m wide road space.

4. MODEL VALIDATION

The validation is concerned with determining whether the conceptual

simulation model is an accurate representation of the system under study

(Law and Kelton, 2000)12. It is a crucial element in assessing the models

value for making policy decisions and is aimed to produce a model that

represents true system behavior so that the model can be used as a

substitute for the physical system. Since no simulation model can be

expected to capture real behavior exactly, formulating appropriateperformance measures or evaluation functions is fundamental to the

validation process. Sacks, et al. (2002)18 suggest visual comparison of


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Fig. 3. Traffic flow animation snapshot

graphical output (animation) with field data as a validation technique for

simulation models capable of showing animation. Though this process of

visual validation is highly informal and subjective in nature, it is of great

value in assessing the capability of the model to emulate reality as well

as identifying sources of trouble.

As a measure of validation, the simulation model was used to

replicate the mixed traffic flow on a stretch of road. The total length of

road stretch for simulation was taken as 1000 m. The initial 200 m length

at the entry point was used as a warm up zone and a similar 200 m long

stretch at the exit end was also excluded from the analysis. To eliminate

the initial transient nature of traffic flow, the simulation clock was set to

start only after the first 50 vehicles reached the exit end of the road

stretch. The traffic composition considered for the purpose of simulation(field observed value) is shown in Fig. 4. The data of overall dimensions

and free speeds of the different categories of vehicles, given as input for


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Bicycles

4.5%

Motorized

two-wheelers

50.0%

Cars

25.0%

Auto-rickshaws

10.0%

Buses

5.0%

Light

Commercial

Vehicles

3.0%

Trucks

2.5%

Fig. 4. Composition of field observed traffic

model validation, are shown in Table1. The data of acceleration

characteristics of the vehicles are shown in Table 2. The model measures

the speed maintained by each vehicle when it traverses a given reference

length of roadway which is specified by the user. The output also includes

the number of each category of vehicles generated, the values of all theassociated headways generated, number of vehicles present over a given

TABLE-1. DATAOFFREESPEEDANDOVERALL DIMENSIONSOFTHEDIFFERENTTYPESOFVEHICLES

Sl. Type of Vehicle Free Speed in km/h Average Overall

No. Dimensions in m

Mean Standard Length Breadth

Deviation

(1) (2) (3) (4) (5) (6)

1. Bus 53.01 7.2 10.3 2.5

2. Truck 51.50 6.6 8.4 2.5

3. LCV 50.30 7.7 5.0 1.9

4. Car 58.90 14.3 4.2 1.7

5. Auto-rickshaw 44.90 7.7 2.6 1.4

6. M T W 45.05 12.4 1.8 0.6

7. Bi-cycle 16.00 3.0 1.9 0.5

8. Tri-cycle 15.30 3.2 2.5 1.3


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TABLE-2. DATA OF ACCELERATION CHARACTERISTICS OF THEDIFFERENT TYPES OF VEHICLES

Type of Vehicle Acceleration Value at Various Speed

Ranges (m/s2)

0 - 20 km/h 20 - 40 km/h Above 40 km/h

(1) (2) (3) (4)

Bus 0.89 0.45 0.33

Truck 0.79 0.45 0.33

Light Commercial Vehicle 0.82 0.60 0.35Car 1.50 1.30 1.00

Auto-rickshaw 1.01 0.58 0.34

Motorised Two-wheeler 1.35 1.03 0.37

Bicycle 0.10 a a

Tricycle 0.07 a a

Note: a Not applicable

length of road (concentration), number of overtaking manoeuvre made byeach vehicle, speed profile of vehicles, etc. The simulated traffic

characteristics (headway and speed) were then compared with respective

field values to assess the goodness of fit. The model was first validated

by examining the observed and simulated headways of traffic, moving in

one direction on a 7.5 m wide road space at various volume levels. The

results of the experiment, for two levels of traffic volume, are shown in

Fig. 5. It can be noted that the observed and simulated cumulative

frequency distributions match to a greater extent in both the cases,

indicating the validity of the model. To further ensure the credibility of

the models behaviour under heterogeneous traffic conditions, the model

was used to simulate one-way traffic on a 7.5 m wide road space with

different traffic volumes. The traffic speeds simulated by the model were

compared with observed speed values. It was found that the observed

and simulated speeds are matching to a greater extent in all the cases.

Fig. 6 depicts, for example, the comparison of observed and simulatedspeeds for a volume level of 2,150 vehicles/hour (v/h) for a known traffic

composition (Fig. 4). It can be seen that the simulated speeds of different

categories of vehicles match with the corresponding observed values to

a greater extent. The details of the statistical validation of the model,

based on observed and simulated speeds of the different categories of

vehicles, as example, is shown in Table 3. It can be seen that the simulated

speed values significantly replicate the field observed speeds of the

different categories of vehicles.


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231

0

20

40

60

80

100

120

0 2 4 6 8 10

Headway (s)

CumulativeFrequency

(%)

observed

simulated

0

20

40

60

80

100

120

0 1 2 3 4

Headway (s)

Cu

mulativeFrequenc

y

(%)

observed

simulated

Fig. 5. Model Validation by comparison of headways

Traffic Volume = 2608 v/h

Traffic Volume = 5880 v/h


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0

10

20

30

40

50

60

Motorized

two-

wheelers

Cars

Buses

Trucks

Light

Commercial

Vehicles

Auto-

rickshaws

Bicycles

Simulated Observed

Fig. 6. Model validation by comparison of speeds

TABLE-3. STATISTICALVALIDATIONOFTHEMODELBASEDONOBSERVEDANDSIMULATEDSPEEDS

Vehicle Type Observed Simulated Difference Squared

Average Average (Deviation) Deviation

Speed (km/h) Speed (km/h)

(1) (2) (3) (4) (5)

Motorised two-wheelers 40.00 40.01 -0.01 0.0001Cars 47.00 44.80 2.20 4.84

Buses 42.00 39.10 2.90 8.41

Trucks 40.60 38.90 1.70 2.89

Light Commercial Vehicles 40.10 38.80 1.30 1.69

Auto-rickshaws 39.00 37.74 1.26 1.59

Bicycles 13.00 12.4 0.60 0.36

SUM 9.95 19.78

dmean

= Mean of observed difference = 9.95/7 = 1.42

t statistic of observed speeds, to

= dmean

/(Sd/k),where k = Number of data sets =7

Sd2= 19.78/(k-1) = 19.78/6 = 3.30, where S

d is the Standard Deviation; S

d =1.82

Therefore, to

= 1.42/(1.82/7) = 2.06.

The critical value of t statistic for 0.05 level of significance and 6 degrees of freedom,

obtained from standard t-distribution Table, is 2.45. Thus, it can be seen that the value of

t statistic calculated based on the observed data (to) is less than the corresponding Table

value. This implies that the simulated speeds significantly represent the observed speeds.

Speed

(km/h)


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233

5. MODEL APPLICATION

The model was first used to simulate homogeneous (100 per cent

passenger cars) traffic flow, at various volume levels, in one direction, on

7.5 and 11.0 m wide road spaces, which are common on major divided

urban roads in India. Speed-flow relationships obtained by simulating

traffic-flow on these roads are presented in Figs. 7 and 8 for 7.5 m and

11.0 m wide road spaces respectively. The simulation runs were repeated

using three different-random number streams to check for the consistency

of the results. Simulation run-lengths were varied according to the inputtraffic volumes, to obtain adequate data for the calculation of average

speeds.

0

10

20

30

40

50

60

70

0 500 1000 1500 2000 2500 3000 3500

Flow (Cars per hour)

Fig. 7. Speed-flow relationship for cars-only traffic on 7.5 m wide road space

Capacity values of 7.5 m and 11.0 m wide road spaces, with trafficin one direction, were then obtained as about 3,200 and 4,500 cars per

hour respectively (refer Figs. 7 and 8). The tentative capacity values, for

mixed traffic, as per IRC:86-19837, for one-way traffic movement, on two-

lane (7.5 m) and three-lane (10.5 m) urban arterial are, respectively, 2,400

and 3,600 PCU per hour. It may be noted that the capacity values obtained

through the present study are higher than the capacity values recommended

by IRC:86. The relatively lower values given in the IRC code may be

attributed to the approximation that might have been made in assigningPCU values for the different types of vehicles on urban roads in India.

Speed

(km/h)


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0

10

20

30

40

50

60

70

0 1000 2000 3000 4000 5000

Flow (Cars per hour)

Fig. 8. Speedflow relationship for cars-only traffic on 11.0 m wide road space

Assuming LOS C for urban road design (V/C ratio=0.7), the service

volumes of 7.5 m and 11.0 m wide roads are found (by multiplying the

capacity values by 0.7) to be about 2,250 PCU/h and 3150 PCU/h

respectively.

For the conversion of heterogeneous traffic into equivalenthomogeneous traffic, PCU factors of different categories of vehicles are

required. In this study, an attempt was made to derive PCU values for the

vehicles in the mixed traffic with flow rates at LOC C, which is commonly

taken as the basis for design of urban road facilities. For this purpose,

passenger-cars-only streams were simulated at various flow levels to

obtain the speedflow relationship of homogeneous traffic. From the flow

of passenger-cars-only stream at LOC C, a specified number of cars are

removed and an equivalent number of the chosen vehicle type is added,

so that they create more or less the same effect on the traffic stream that

is equivalent to that of the cars removed from the stream. Then, the

number of cars removed divided by the number of other vehicle type

introduced will give the PCU value of the vehicle type. The procedure

was repeated by varying the composition of the chosen vehicle type over

a wide range. In general, it was found that for most of the vehicle types,

there was a decreasing trend in PCU values as their percentage compositionin the traffic stream increased. The trends of variation of PCU values, due

to variation in the percentage composition of these vehicles, are depicted

Speed

(km/h)


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235

in Figs. 9 to 14. These trend curves of PCU values of the different types

of vehicles will be very useful to select a more appropriate value of PCUfor a given vehicle type based on the percentage composition of the type

of vehicle in the traffic stream. For example, in the case of buses (Fig.10)

on a 7.5 m wide road space, the PCU will be 2.7 when the composition

is 10 per cent, and it will be 1.8 when the composition is 80 per cent.

0.00

0.50

1.00

1.50

2.00

0% 10% 20% 30% 40% 50% 60% 70% 80%

Percentage Composition of Two-wheelers

PCU

7.5 m wide road 11.0 m wide road

Fig. 9. Variation of PCU of motorised two-wheelers

0.00

1.00

2.00

3.00

4.00

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentage Composition of Buses

PCU

Fig. 10. Variation of PCU of buses

7.5 m wide 11.0 m wide road


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0.00

1.00

2.00

3.00

4.00

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentage Composition of Trucks

PCU


Fig. 11. Variation of PCU of trucks

0.00

0.50

1.00

1.50

2.00

2.50

3.00

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentage Composition of Bicycles

PCU

7.5 m wide road 11.0 m wide

road

Fig. 12. Variation of PCU of bicycles


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237

0.00

0.50

1.00

1.50

2.00

2.50

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentage Composition of Auto-rickshaws

PCU


Fig. 13. Variation of PCU of auto-rickshaws

0.00

1.00

2.00

3.00

4.00

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentage Composition of LCVs

PCU


Fig. 14. Variation of PCU of light commercial vehicles


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To assess the effect of PCU values derived in this study on the

service volumes of 7.5 and 11.0 m wide urban road spaces, the followingmethodology was adopted. As a first step, three common urban traffic

compositions were assumed. These assumed compositions are shown in

Table 4. For each of these compositions, the traffic flow was simulated

to obtain the respective speed-flow relationships. The speedflow

relationships thus obtained for one-way traffic flow on a 7.5 m wide road

space is depicted in Fig. 15. As depicted in the figure, the capacity flows

observed during simulation runs were 3,558, 4,077, and 3,568 v/h

respectively for the assumed traffic compositions 1, 2 and 3. A similarexercise for traffic flow on a 11.0 m wide road space yielded capacity

values of 5,138, 5,976, and 5,306 v/h respectively for compositions 1, 2

and 3 (Fig. 16).

TABLE-4. TRAFFICCOMPOSITIONSCONSIDEREDFORTHESTUDY

Percentage composition

Vehicle type

Compos it ion 1 Compos it ion 2 Composi ti on 3

Motorised Two-wheelers 5 0 60 4 0

Cars 2 5 15 2 5

Buses 5 3 3

Trucks 2.5 1.5 1.5

Light Commercial Vehicles 3 2 2

Auto-rickshaws 10 1 5 2 0

Bicycles 4.5 3.5 8.5

0

10

20

30

40

50

60

0 1000 2000 3000 4000 5000

Fig. 15. Speed-flow relationships on 7.5 m wide road space

Speed

(km/h)

comp.1 comp.2 comp.3

Flow (v/h)


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239

0

1 0

2 0

3 0

4 0

5 0

6 0

0 2 0 0 0 4 00 0 6 0 0 0 8 00 0

Fig. 16. Speed-flow relationships on 11.0 m wide road space

As the next step, the service volumes for these roads, in terms ofheterogeneous traffic flow, for each of the compositions, were estimated

based on a V/C ratio of 0.7. The service volumes in terms of heterogeneous

traffic were then converted to equivalent passenger cars using the PCU

factors derived in this study. The details are shown in Tables 5 and 6

respectively for 7.5 m and 11.0 m wide road spaces. It can be noted that

the results are consistent (error being 4.3 to 8.8 for the different

compositions) establishing the credibility of the PCU values derived

using the model.

TABLE-5. COMPARISONOFSERVICEVOLUMESOFCARS-ONLYANDMIXEDTRAFFIC ON

7.5 MWIDEROADSPACE

Road width=7.5 m Traffic flow

Capacity flow for cars-only 3200

traffic (cars/h)

Flow at LOS C for cars-only 2250

traffic (cars/h)

C om po si ti on C omp os it ion Co mpo si ti on

1 2 3

Capacity flow (v/h) 3538 4077 3568

Flow at LOS C (v/h) 2447 2854 2498

Flow at LOS C (PCU/h) 2381 2367 2450

Difference in flow at LOS C between 5.80% 5.20% 8.88%

cars-only and mixed traffic

c om p. 1 c om p.2 c o m p . 3

Flow (v/h)

Speed

(km/h)


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TABLE-6. COMPARISON OF SERVICE VOLUMES OF CARS-ONLY AND MIXED TRAFFIC ON

11.0 M WIDE ROAD SPACE

Road width=11.0 m Traffic flow

Capacity flow for cars-only 4500

traffic (cars/h)

Flow at LOS C for cars-only 3150

traffic (cars/h)

C om po si ti on C omp os it ion Co mpo si ti on

1 2 3

Capacity flow (v/h) 5138 5976 5306

Flow at LOS C (v/h) 3597 4183 3714

Flow at LOS C (PCU/h) 3285 3339 3381

Difference in flow at LOS C between 4.30% 6.00% 7.30%

cars-only and mixed traffic

6. CONCLUSIONS

The following are the important conclusions of the study:

1. Through the research work, a model to simulate heterogeneous

traffic flow on mid block sections of urban roads has been developed.

2. The results of validation of the model indicate that the model iscapable of replicating the mixed traffic flow on urban roads to a

highly satisfactory extent.

3. The trend lines developed to indicate the extent of variation of PCU

value will be useful to pick an appropriate PCU value for the different

types of vehicles in mixed traffic streams based on the observed

composition of the vehicles in the stream.

4. Based on the simulation study, it has been found that the servicevolumes at LOS C for one-way traffic flow on 7.5 m and 11.0 m wide

road spaces are 2,250 and 3,150 PCU per hour respectively.

5. It has been found that the effect of heterogeneity of traffic on the

variable PCU values is only marginal (the difference in service

volume values between cars-only and heterogeneous traffic streams

lies between 4.3 and 8.8 per cent) and hence the PCU trend lines can

be used to pick out PCU values for different vehicular compositionsof heterogeneous traffic streams.


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241

Limitations of the Study:

1. The application of the model has been demonstrated only for two

road widths though similar results can be obtained for any normal

widths of road space using the model.

2. The model, in its present form, can simulate only one-way traffic

movement which restricts its application for only one-way streets

and divided urban roads.

Scope for Further Research:

1. The model can be modified to replicate two-way traffic flow on

undivided roads by incorporating suitable simulation logic for

movement of vehicles in opposing streams of traffic.

2. The model can be extended to cover traffic flow through intersections

so that the flow of traffic on a corridor can be simulated which will

provide more useful outputs for traffic management.

REFERENCES

1. Arasan, V.T., and Kashani., S.H. (2003). Modeling Platoon Dispersal Pattern

of Heterogeneous Road Traffic. Proceedings of the 82nd Annual Meeting of

Transportation Research Board, Washington, D.C., USA, Jan. 2003.

2. Chandra, S., and Sikdar, P. K. (2000). Factors affecting PCU in mixed traffic

situations on urban roads. Road and Transport Research, Vol.9, No.3,40-50.

3. Chandra, S., and Kumar, U.(2003). Effect of Lane Width on Capacity under

Mixed Traffic Conditions in India. Journal of Transportation Engineering,

Vol. 129, ASCE, 155-160.

4. Cunagin, W.D., and Messer, C.J. (1983). (Passenger-car equivalents for rural

highways). Transportation Research Record 905, Transportation Research

Board, Washington, D.C., 61-68.

5 . E lef ter iadou, L . , Torbic , D . , and Webste r, N . (1997) . Deve lopment o f

passenger car equivalents for freeways, two-lane highways, and arterials.

Transportation Research Record 1572, Transportation Research Board,

Washington, D.C., 51-58.

6. Huber, M.J. (1982). Estimation of passengercar equivalents of trucks in

traffic stream. Transportation Research Record 869, Transportation Research

Board, Washington, D.C., 60-68.

7. IRC: 86-1983, Geometric Design Standards for Urban Roads in Plains, IndianRoads Congress, New Delhi.


24/24


8. IRC: 106-1990, Guidel ines for Capacity of Urban Roads in Plain Areas,

Indian Roads Congress, New Delhi.

9 . Issac, K.P. (1995) . Studies on Mixed Traff ic Flow Characteris tics Under

Varying Composition. Ph.D thesis (Unpublished), Bangalore University.

10 . Krammes, R.A., and Crowley, K.W. (1986). Passenger car equivalents for

trucks on level freeway segments. Transportation Research Record 1091,

Transportation Research Board, Washington, D.C., 10-17.

11 . Kumar, V.M., and Rao, S.K. (1996). Simulation modelling of traffic operations

on two-lane highways. Highway Research Bulletin, No. 54, Indian RoadsCongress, Highway Research Board, 211 237.

12. Law, A.M., and Kelton, W.D. (2000). Simulation modeling and analysis,

McGraw-Hill Higher Education, Singapore.

13 . Maitra, B., Sikdar, P.K., and Dhingra, S.L. (1999). Modelling Congestion on

Urban Roads and Assessing Level of Service. Journal of Transportation

Engineering, ASCE, Vol.125, No.6, 08-514.

14 . Marwah, B. R. and Singh, B. (2000). Level of service classification for urbanheterogeneous traffic: A case study of Kanpur metropolis. Transportation

Research. Circular E- C018: 4th International Symposium on Highway Capacity,

Maui, Hawaii, 271-286.

15. Minderhoud, M.M. Botma, H., and Bovy, P.H.L. (1997). Assessment of

Roadway Capacity Estimation Methods. Transportation Research Record

1572, TRB, National Research Council, Washington D.C., 59-67.

16 . Rajagopal, A. and Dhingra, S. L. (2002). Simulation-based evaluation for

traffic management. National Conference on Transportation Systems (NCTS),IIT New Delhi, 449-458.

17 . Ramanayya, T.V. (1988). Highway capacity under mixed traffic conditions.

Traffic Engineering and Control, Vol. 29 (5), 284-287, 300.

18 . Sacks, J., Rouphail, N.M., Park, B. and Thakuriah, P. (2002). Statistically-

Based Validation of Computer Simulation Models in Traffic Operations and

Management. Journal of Transportation Statistics, Vol. 5, No. 1., 1-24.

19 . Sumner, R., Hill, D., and Shapiro, S. (1984). Segment passenger car equivalent

values for cost allocation on urban arterial roads. Transportation Research-

A., Vol. 18A. No. 5/6, 399-406.

20. The Highway Capacity Manual (HCM). (2000) . Special Rep. No. 209, 4th

Ed., Transportation Research Board, National Research Council, Washington,

D.C.

21 . Tiwari, G., Fazio, J., and Pavithravas, S. (2000). Passenger car units for

heterogeneous traffic using a modified density method. Transportation Research

Circular E- C018, 4th International Symposium on Highway Capacity, Maui,

Hawaii, 246-257.


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