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

219

Paper No. 500+

“SIMULATION OF HETEROGENEOUS TRAFFIC TO DERIVE CAPACITY AND SERVICE VOLUME

STANDARDS FOR URBAN ROADS”

By

DR. V. THAMIZH ARASAN* & R EEBU ZACHARIAH KOSHY**

CONTENTSPage

1. Introduction ... ... 2202. Background ... ... 2223. The Simulation Model ... ... 2254. Model Validation ... ... 2275. Model Application ... ... 2336. Conclusions ... ... 240

SYNOPSIS

Highway capacity is the maximum number of vehicles that can reasonably beexpected to pass a section of road in unit time under prevailing roadway, trafficand control conditions; whereas, service-volume is the maximum number of vehiclesthat can be accommodated at a specified Level of Service (LOS). The performanceof urban road networks depends on the practical capacity and actual volume oftraffic on each of the links that constitute the network. The heterogeneous trafficexisting on urban roads of developing countries like India is characterised by thepresence of vehicles of wide ranging static and dynamic characteristics. The unrestrictedmovement of these vehicles on road space makes the lane concept and expressionof flow values, based on standard lane width, invalid. Also, when different typesof vehicles share the same road space without any physical segregation, the extentof vehicular interactions varies widely with variation in traffic mix. To arrive atan estimate of practical capacity of road links, it is necessary to study the influenceof roadway, traffic and other relevant features on vehicular movement using appropriatetechniques. Modelling of traffic flow is the widely accepted technique for studyingthe flow characteristics over a wide range of the involved variables. Hence, thereis a need for development of models to replicate heterogeneous traffic flow; andsuch models would be of significant assistance to traffic planners while making keydecisions. The design service volumes recommended for urban roads are for a LOSof 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

DR. ARASAN & K OSHY ON220

expressed in Passenger Car Units (PCU), the PCU values for the different types ofvehicles are needed for quantifying traffic flow corresponding to LOS C.

Simulation, from microscopic through macroscopic, is increasinglybecoming a popular traffic-flow modelling tool for analysing trafficoperations and highway capacity. This paper deals with the developmentand application of a heterogeneous traffic-flow simulation model to developcapacity and service-volume standards for urban roads. The simulationmodel was first validated and used to estimate PCU values of differentcategories of vehicles, applicable to traffic flow at LOS C. These PCUvalues have then been used to convert heterogeneous traffic streams ofvarying compositions to equivalent homogeneous (passenger-cars-only)streams. The results were found to be consistent, establishing the credibilityof the PCU values derived using the model. Service volumes for 7.5 m and11.0 m wide urban roadways were also arrived at, as an illustration of theapplication of the model. Development of a general purpose trafficsimulation model to replicate the lane-less nature of heterogeneous trafficflow for comprehensive study of the traffic flow characteristics, andapplication of the model to derive relatively accurate PCU values todevelop capacity standards are the unique features of the research studypresented here.

1. INTRODUCTION

With the increasing urbanization, improved transportation technologyand an expanding economy, additional roads and highways are built, inan effort to balance roadway capacity and demand. At the same time,traffic volumes and travel distances continue to increase, and the newroadway facilities get filled up shortly after completion. Traffic congestionand safety are serious problems, impacting on the economy, environmentand quality of life in our cities. In designing highways, traffic engineersmust anticipate the amount and type of traffic that will use the road, inorder to make the highway match its anticipated use. The capacity of ahighway is defined as the number of vehicles that can reasonably beexpected to pass a point or section of the highway during a given periodof time under prevailing roadway, traffic, and control conditions. Highwaycapacity is usually expressed in terms of number of vehicles per hour.Knowledge of capacity of a road is essential in planning, design andoperation of roads. Capacity refers to the rate of flow during a specifiedperiod; and any change in the prevailing conditions results in a changein the capacity of the facility. Also, capacity is assumed to be stochasticin nature because of differences in individual driver behaviour and changingroadway and weather conditions (Minderhoud, et al. 1997).15

SIMULATION OF HETEROGENEOUS TRAFFIC TO DERIVE CAPACITY AND

SERVICE VOLUME STANDARDS FOR URBAN ROADS221

The concept of level of service (LOS) describes operating conditionswithin a traffic stream, and their perception by motorists and passengers.A level-of-service definition generally describes these conditions in termsof such factors as speed, travel time, freedom to manoeuvre, trafficinterruptions, comfort and convenienced, and safety. The HighwayCapacity Manual special report 209 of Transportation Research Board(TRB), USA defines six levels of service. They are given letter designationsfrom A to F with A representing the best operating conditions and Frepresenting the worst. Very often, the flow reflects the quality of trafficmovement on a road. When traffic volumes approach the capacity of aroad, traffic becomes congested and the flow of traffic is consideredundesirable. LOS is thus one mechanism used to measure the quality oftraffic flow.

Another mechanism used to measure the quality of traffic flow isthe volume-to-capacity (V/C) ratio. It is a measurement of traffic serviceor flow quality that compares the number of vehicles using or expectedto 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 asingle hour. Use of V/C ratio for analysis allows the evaluation of potentialdemand compared to the capacity of the facility in question. Volume cannever exceed the capacity of the facility, yet demand can. If the demandfor a facility is greater than the capacity, a break down situation occurs.

Highway capacity values and speed-flow relationships used forplanning, design and operation of highways, in most of the developedcountries, have been based on Manuals and Codes of practices, whichare valid for fairly homogeneous traffic comprising vehicles of more orless uniform static and dynamic characteristics. Even under nearlyhomogeneous traffic conditions, it is necessary to convert heavy vehiclessuch as buses and trucks, which constitute a small proportion of traffic,into equivalent number of a standard type of vehicle (usually passengercars) to measure the traffic flow using a single unit. The road traffic inmost developing countries such as India comprises vehicles of wideranging physical dimensions, weight and dynamic characteristics. Also,the motorized and non-motorized vehicles share the same road spacewithout any segregation. The speeds of these vehicles vary from about5 to over 100 km/h. Due to the highly varying physical dimensions andspeeds, it becomes difficult to make the vehicles to follow traffic lanes.Consequently, they tend to choose any advantageous position on theroad based on space availability. Also, the extent of vehicular interactionsvaries widely with variation in traffic mix. Vehicles, which are ‘less mobile’in terms of manoeuvrability, cause significant level of friction to themovement of other vehicles in the traffic stream. The extent of friction


realized by the different categories of vehicles depends on their staticand dynamic characteristics. For example, at higher traffic volumes alarge proportion of motorized two-wheelers and bicycles may be able tomove with speeds closer to their free speeds because of their ability forutilizing smaller gaps in the stream for movement, while the large-sizevehicles such as buses and trucks may be subjected to considerablespeed reduction. Traffic engineers account for the impact on capacityfrom the different types of vehicles by assigning each class of vehiclea passenger car equivalent (PCE or PCU) value. This value represents thenumber of passenger cars that would consume the same percentage ofthe highway’s capacity as the vehicles under consideration under prevailingroadway and traffic conditions. This study deals with development of asimulation model to replicate heterogeneous traffic flow on urban roadsof developing countries such as India and the application of the modelto derive PCU values for the different types of vehicles in heterogeneoustraffic 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 empiricaland indirect empirical approaches (Minderhoud et al. 1997)15. There arefour different methods available for capacity estimation under the directempirical approach. The observed headway models (e.g. Branston’sgeneralised queueing model, Beckley’s semi-Poisson model, etc.) are basedon the theory that, at capacity level of flow on the road, all driver-vehicleelements are constrained. These models can be applied only for a singlelane. 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 andspeeds

Guidelines Simulationmodels

Fig. 1. Methods of capacity estimation



volumes is the observed extreme value method which estimates the capacityof a road by using only known maximum traffic volumes observed overa certain period. The product limit method is an example for road capacityestimation based on both traffic volume and speed data. On line proceduresfor actual capacity estimation and the fundamental diagram method arecapacity estimation techniques based on traffic volumes, speeds anddensities. Fundamental diagram method is based on the relationshipsbetween traffic flow, speed and density. It is sufficient to measure two ofthe three variables to construct one diagram. Traffic must be observed atdifferent volumes to make a curve fitting possible. The capacity-estimationguidelines by TRB (HCM), Indian Roads Congress (IRC) and similaragencies are based on indirect empirical methods using appropriatetheoretical techniques. Outputs from a traffic flow simulation model canbe used to construct fundamental diagrams of flow, thereby making itpossible to estimate the capacity of a facility. Though several methods,as indicated (Fig. 1) are available for estimation of capacity, the microscopicsimulation models are now widely used as the most effective analyticaltool for studying the traffic problems and for assessing the effectivenessof traffic management measures. This is because, these models, oncevalidated, can be used to study the traffic flow characteristics over a widerange 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 theservice a facility provides, is automatically considered when LOS isspecified. 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 orD produces a desirable balance among cost, safety and operationalmeasures. The methodologies presented in HCM do not generally dealwith full hour volumes, but rather with equivalent hourly flow ratesduring a peak 15 min interval within the analysis hour. The basic capacityvalue of 2,000 PCU per hour per lane for freeways and multilane roadsthus 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 urbanroads (IRC:106-1990)8. At this level, the volume of traffic will be around0.7 times the maximum capacity, and this is taken as the design servicevolume for urban roads. Maitra, et al. (1999)13 proposed 10 levels ofservices 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 ofquantifying congestion on urban roads. They assumed capacity valuesof study locations on urban roads as 3,500 and 4,500 PCU per hour forroad widths of 7.0 and 10.3 m respectively in one direction.


There have been several attempts to derive PCU values applicable forhomogeneous 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 thePCU of a vehicle type will decrease with increase in its own proportionin the traffic stream, and that for a given road width, an increase in flowlevel will result in smaller PCU value for a vehicle type. Recently (2003)3,Chandra and Kumar proposed capacity values for various road widthsunder mixed traffic conditions. They used a new concept for estimatingPCU of various types of vehicles based on their projected areas on theroad surface. The PCU factors, for urban roads, recommended by IRC areavailable in the IRC Code IRC:106-19908. The PCU values have been givenin the Code for two levels of traffic mix, namely the percentage compositionof a vehicle type being 5 per cent and 10 per cent and above.

There had been quite a few attempts to apply the techniques ofsimulation 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 laneconcept. Issac (1995)9 developed a mixed traffic flow simulation model forestimation of urban road capacities and studied the effect of variation intraffic composition. A simulation model of traffic operations on two-lanehighways was developed by Kumar and Rao (1996)11. Rajagopal andDhingra (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 studiedthe platoon dispersal characteristics of heterogeneous traffic streamsusing simulation technique. All these studies, however, are limited inscope, and further the models developed are specific to certain roadwayand traffic conditions.

The review of literature has led to the following capacity relatedobservations. There is substantial variation in the capacity values estimatedby various researchers, by virtue of the variations in the roadway andtraffic conditions considered for the studies and the uncertainties associatedwith mixed traffic and its characteristics. Computer simulation models canbe advantageously used to estimate capacity and PCU values of variouscategories of vehicles expected in an urban heterogeneous trafficenvironment. The formulation to establish PCU for a vehicle type on aparticular roadway should necessarily be based on the variables thatreflect the combination of factors contributing to the overall influence of



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 gaprequirements in a traffic stream, etc. Hence, the speed reduction causedto the reference vehicle (passenger cars) by the addition of a specifiednumber of vehicles of a particular type, is a satisfactory basis for estimatingthe PCU value of that vehicle. For the planning and design of urbantraffic facilities, traffic-flow level at LOS C (V/C ratio about 0.7) is usuallyrecommended. Therefore, the PCU factors used for the conversion ofmixed 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 microscopicheterogeneous traffic-flow simulation model, because, it facilitatedrepresentation of all the relevant characteristics of mixed traffic, andpermitted the variation of all the parameters over a wider range, than whatmight have been possible with field observed data.

3. THE SIMULATION MODEL

In the absence of program packages for simulating heterogeneoustraffic flow, a computer program was newly developed by the authors forthe purpose, and the program code was written in C language. The modeladdresses the stochastic and dynamic nature of heterogeneous trafficflow. It is a discrete-event traffic simulation model, using interval-scanningtechnique with fixed-increment time advance. At higher traffic flow levels,there is a chance of more vehicle arrivals during each scan interval onesecond. To address this issue, a separate clock with precision (scaninterval) of 0.05 second is provided in the headway-generation-module togenerate inter arrival times with 0.05 second accuracy. The model is alsocapable of showing the animation of simulated traffic movements over theroad stretch. For the purpose of modelling, the entire road space isconsidered as single unit, without any lane separation. The vehicles arethen represented, with dimensions, as rectangular blocks, and theirlongitudinal and lateral movements on the road surface are tracked usinga co-ordinate system. The traffic-simulation model was validated bycomparing the simulated and field observed data on a set of roads (SardarPatel road and GST road) in Chennai City. The modelling concepts areonly briefly explained here as the emphasis of this paper is on theestimation of capacity and service-volume standards for urban roads. Thebasic structure of the model is depicted in Fig. 2. The model, as indicatedin the figure, has three major modules, namely, Vehicle Generation, VehiclePlacement, and Vehicle Movement. Inputs required for the model are:


Inputs and Initialization

Generation of Vehicle Arrivals

Vehicle Movement

Start

Vehicle Placement

Is SimulationTime Over?

End

Print Outputs

No

Yes

Fig. 2. Simulation framework



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 anddeceleration characteristics, total simulation period, etc. For generation ofheadways, free speed, etc., the model uses several random-number streams,which are generated by specifying separate seed values. Any generatedvehicle is placed at the beginning of the simulation stretch, consideringthe safe headway (which is based on the free speed assigned to theentering vehicle), lateral gap, and the overall width of the vehicle withlateral clearances. If the longitudinal gap is less than the minimum requiredsafe gap, the entering vehicle is assigned the speed of the leadingvehicle, and once again the check for safe gap is made. If the gap is stillinsufficient to match the reduced speed of the entering vehicle, it is keptas backlog, and its entry is shifted to the next scan interval. During everyscan interval, the vehicles remaining in the backlog will be admitted first,before allowing the entry of a newly generated vehicle. A continuousincrease in the number of vehicles as backlog during the simulation runindicates 100 per cent platoon condition (capacity flow) on the roadstretch. Placement and movement of non-motorized vehicles (Bicyclesand Tricycles) based on field observation, is restricted to the left mostpart of the road only.

The simulation model uses the time-interval scanning technique toupdate the state of the system, the chosen interval being one second.During each scan interval, the positions of all vehicles in the system areupdated using the formulated movement logic. The movement logic alsoincludes the overtaking and car-following logics as applicable toheterogeneous traffic. The model measures the speed maintained by eachvehicle when it traverses a given reference length of roadway which isspecified by the user, in addition to the various other flow characteristicsof interest. Fig. 3 displays a “snapshot” of animation screen while simulatingmixed traffic in one direction on a 7.5 m wide road space.

4. MODEL VALIDATION

The validation is concerned with determining whether the conceptualsimulation model is an accurate representation of the system under study(Law and Kelton, 2000)12. It is a crucial element in assessing the model’svalue for making policy decisions and is aimed to produce a model thatrepresents true system behavior so that the model can be used as asubstitute for the physical system. Since no simulation model can beexpected to capture real behavior exactly, formulating appropriateperformance measures or evaluation functions is fundamental to thevalidation process. Sacks, et al. (2002)18 suggest visual comparison of


Fig. 3. Traffic flow animation snapshot

graphical output (animation) with field data as a validation technique forsimulation models capable of showing animation. Though this process ofvisual validation is highly informal and subjective in nature, it is of greatvalue in assessing the capability of the model to emulate reality as wellas identifying sources of trouble.

As a measure of validation, the simulation model was used toreplicate the mixed traffic flow on a stretch of road. The total length ofroad stretch for simulation was taken as 1000 m. The initial 200 m lengthat the entry point was used as a warm up zone and a similar 200 m longstretch at the exit end was also excluded from the analysis. To eliminatethe initial transient nature of traffic flow, the simulation clock was set tostart only after the first 50 vehicles reached the exit end of the roadstretch. The traffic composition considered for the purpose of simulation(field observed value) is shown in Fig. 4. The data of overall dimensionsand free speeds of the different categories of vehicles, given as input for



Bicycles4.5%

Motorized two-wheelers

50.0%

Cars25.0%

Auto-rickshaws

10.0%

Buses5.0%

Light Commercial

Vehicles3.0%

Trucks2.5%

Fig. 4. Composition of field observed traffic

model validation, are shown in Table1. The data of accelerationcharacteristics of the vehicles are shown in Table 2. The model measuresthe speed maintained by each vehicle when it traverses a given referencelength of roadway which is specified by the user. The output also includesthe number of each category of vehicles generated, the values of all theassociated headways generated, number of vehicles present over a given

TABLE-1. DATA OF FREE SPEED AND OVERALL DIMENSIONS OF THE DIFFERENT TYPES OF VEHICLES

Sl. Type of Vehicle Free Speed in km/h Average Overall No. Dimensions in m

Mean Standard Length BreadthDeviation

(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. MTW 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


TABLE -2. DATA OF ACCELERATION CHARACTERISTICS OF THE DIFFERENT TYPES OF VEHICLES

Type of Vehicle Acceleration Value at Various SpeedRanges (m/s2)

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

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

Bus 0.89 0.45 0.33Truck 0.79 0.45 0.33Light Commercial Vehicle 0.82 0.60 0.35Car 1.50 1.30 1.00Auto-rickshaw 1.01 0.58 0.34Motorised Two-wheeler 1.35 1.03 0.37Bicycle 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 trafficcharacteristics (headway and speed) were then compared with respectivefield values to assess the goodness of fit. The model was first validatedby examining the observed and simulated headways of traffic, moving inone direction on a 7.5 m wide road space at various volume levels. Theresults of the experiment, for two levels of traffic volume, are shown inFig. 5. It can be noted that the observed and simulated cumulativefrequency distributions match to a greater extent in both the cases,indicating the validity of the model. To further ensure the credibility ofthe model’s behaviour under heterogeneous traffic conditions, the modelwas used to simulate one-way traffic on a 7.5 m wide road space withdifferent traffic volumes. The traffic speeds simulated by the model werecompared with observed speed values. It was found that the observedand 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 trafficcomposition (Fig. 4). It can be seen that the simulated speeds of differentcategories of vehicles match with the corresponding observed values toa greater extent. The details of the statistical validation of the model,based on observed and simulated speeds of the different categories ofvehicles, as example, is shown in Table 3. It can be seen that the simulatedspeed values significantly replicate the field observed speeds of thedifferent categories of vehicles.



0

20

40

60

80

100

120

0 2 4 6 8 10

Headway (s)

Cu

mu

lativ

e F

req

ue

ncy

(%

)

observed

simulated

0

20

40

60

80

100

120

0 1 2 3 4

Headway (s)

Cu

mu

lativ

e F

req

ue

ncy

(%

)

observed

simulated

Fig. 5. Model Validation by comparison of headways

Traffic Volume = 2608 v/h

Traffic Volume = 5880 v/h


0102030405060

Mot

oriz

edtw

o-w

heel

ers

Car

s

Bus

es

Tru

cks

Ligh

tC

omm

erci

alV

ehic

les

Aut

o-ric

ksha

ws

Bic

ycle

s

Simulated Observed

Fig. 6. Model validation by comparison of speeds

TABLE-3. STATISTICAL VALIDATION OF THE MODEL BASED ON OBSERVED AND SIMULATED SPEEDS

Vehicle Type Observed Simulated Difference SquaredAverage Average (Deviation) Deviation

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

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

Motorised two-wheelers 40.00 40.01 -0.01 0.0001

Cars 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 = 1.42t statistic of observed speeds, t

o = d

mean /(S

d/Ök), 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/Ö7) = 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 oft statistic calculated based on the observed data (t

o) is less than the corresponding Table

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

Sp

ee

d (

km/h

)



5. MODEL APPLICATION

The model was first used to simulate homogeneous (100 per centpassenger cars) traffic flow, at various volume levels, in one direction, on7.5 and 11.0 m wide road spaces, which are common on major dividedurban roads in India. Speed-flow relationships obtained by simulatingtraffic-flow on these roads are presented in Figs. 7 and 8 for 7.5 m and11.0 m wide road spaces respectively. The simulation runs were repeatedusing three different-random number streams to check for the consistencyof the results. Simulation run-lengths were varied according to the inputtraffic volumes, to obtain adequate data for the calculation of averagespeeds.

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 perhour respectively (refer Figs. 7 and 8). The tentative capacity values, formixed 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, 2,400and 3,600 PCU per hour. It may be noted that the capacity values obtainedthrough the present study are higher than the capacity values recommendedby IRC:86. The relatively lower values given in the IRC code may beattributed to the approximation that might have been made in assigningPCU values for the different types of vehicles on urban roads in India.

Sp

ee

d (

km/h

)


0

10

20

30

40

50

60

70

0 1000 2000 3000 4000 5000

Flow (Cars per hour)

p(

p)

Fig. 8. Speed–flow 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 servicevolumes of 7.5 m and 11.0 m wide roads are found (by multiplying thecapacity values by 0.7) to be about 2,250 PCU/h and 3150 PCU/hrespectively.

For the conversion of heterogeneous traff ic into equivalenthomogeneous traffic, PCU factors of different categories of vehicles arerequired. In this study, an attempt was made to derive PCU values for thevehicles in the mixed traffic with flow rates at LOC C, which is commonlytaken as the basis for design of urban road facilities. For this purpose,passenger-cars-only streams were simulated at various flow levels toobtain the speed- flow relationship of homogeneous traffic. From the flowof passenger-cars-only stream at LOC C, a specified number of cars areremoved 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 thatis equivalent to that of the cars removed from the stream. Then, thenumber of cars removed divided by the number of other vehicle typeintroduced will give the PCU value of the vehicle type. The procedurewas repeated by varying the composition of the chosen vehicle type overa 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, dueto variation in the percentage composition of these vehicles, are depicted

Sp

ee

d (

km/h

)



in Figs. 9 to 14. These trend curves of PCU values of the different typesof vehicles will be very useful to select a more appropriate value of PCUfor a given vehicle type based on the percentage composition of the typeof 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 compositionis 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

PC

U

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


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

PC

U


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

PC

U


Fig. 12. Variation of PCU of bicycles



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

PC

U


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

PC

U


Fig. 14. Variation of PCU of light commercial vehicles


To assess the effect of PCU values derived in this study on theservice volumes of 7.5 and 11.0 m wide urban road spaces, the followingmethodology was adopted. As a first step, three common urban trafficcompositions were assumed. These assumed compositions are shown inTable 4. For each of these compositions, the traffic flow was simulatedto obtain the respective speed-flow relationships. The speed–flowrelationships thus obtained for one-way traffic flow on a 7.5 m wide roadspace is depicted in Fig. 15. As depicted in the figure, the capacity flowsobserved during simulation runs were 3,558, 4,077, and 3,568 v/hrespectively for the assumed traffic compositions 1, 2 and 3. A similarexercise for traffic flow on a 11.0 m wide road space yielded capacityvalues of 5,138, 5,976, and 5,306 v/h respectively for compositions 1, 2and 3 (Fig. 16).

TABLE-4. TRAFFIC COMPOSITIONS CONSIDERED FOR THE STUDY

Percentage composition Vehicle type

Composition 1 Composition 2 Composition 3

Motorised Two-wheelers 50 60 40 Cars 25 15 25 Buses 5 3 3 Trucks 2.5 1.5 1.5 Light Commercial Vehicles 3 2 2 Auto-rickshaws 10 15 20 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

Spe

ed (

km/h

)

comp.1 comp.2 comp.3

Flow (v/h)



0

1 0

2 0

3 0

4 0

5 0

6 0

0 2 0 0 0 4 0 0 0 6 0 0 0 8 0 0 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 estimatedbased on a V/C ratio of 0.7. The service volumes in terms of heterogeneoustraffic were then converted to equivalent passenger cars using the PCUfactors derived in this study. The details are shown in Tables 5 and 6respectively for 7.5 m and 11.0 m wide road spaces. It can be noted thatthe results are consistent (error being 4.3 to 8.8 for the differentcompositions) establishing the credibility of the PCU values derivedusing the model.

TABLE -5. COMPARISON OF SERVICE VOLUMES OF CARS-ONLY AND MIXED TRAFFIC ON

7.5 M WIDE ROAD SPACE

Road width=7.5 m Traffic flow

Capacity flow for cars-only 3200traffic (cars/h)

Flow at LOS C for cars-only 2250traffic (cars/h)

Composit ion Composit ion Composit ion1 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)

Sp

ee

d (

km/h

)


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

11.0 M WIDE ROAD SPACE

Road width=11.0 m Traffic flow

Capacity flow for cars-only 4500traffic (cars/h)

Flow at LOS C for cars-only 3150traffic (cars/h)

Composit ion Composit ion Composit ion1 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 heterogeneoustraffic 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 ahighly satisfactory extent.

3. The trend lines developed to indicate the extent of variation of PCUvalue will be useful to pick an appropriate PCU value for the differenttypes of vehicles in mixed traffic streams based on the observedcomposition 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 wideroad spaces are 2,250 and 3,150 PCU per hour respectively.

5. It has been found that the effect of heterogeneity of traffic on thevariable PCU values is only marginal (the difference in servicevolume values between cars-only and heterogeneous traffic streamslies between 4.3 and 8.8 per cent) and hence the PCU trend lines canbe used to pick out PCU values for different vehicular compositionsof heterogeneous traffic streams.



Limitations of the Study:

1. The application of the model has been demonstrated only for tworoad widths though similar results can be obtained for any normalwidths of road space using the model.

2. The model, in its present form, can simulate only one-way trafficmovement which restricts its application for only one-way streetsand divided urban roads.

Scope for Further Research:

1. The model can be modified to replicate two-way traffic flow onundivided roads by incorporating suitable simulation logic formovement of vehicles in opposing streams of traffic.

2. The model can be extended to cover traffic flow through intersectionsso that the flow of traffic on a corridor can be simulated which willprovide more useful outputs for traffic management.

REFERENCES

1. Arasan, V.T., and Kashani., S.H. (2003). “Modeling Platoon Dispersal Patternof Heterogeneous Road Traffic”. Proceedings of the 82nd Annual Meeting ofTransportation Research Board, Washington, D.C., USA, Jan. 2003.

2. Chandra, S., and Sikdar, P. K. (2000). “Factors affecting PCU in mixed trafficsituations 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 underMixed 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 ruralhighways). Transportation Research Record 905, Transportation ResearchBoard, Washington, D.C., 61-68.

5. Elefteriadou, L., Torbic, D., and Webster, N. (1997). “Development ofpassenger car equivalents for freeways, two-lane highways, and arterials”.Transportat ion Research Record 1572, Transportat ion Research Board,Washington, D.C., 51-58.

6. Huber, M.J. (1982). “Estimation of passenger–car equivalents of trucks intraffic stream”. Transportation Research Record 869, Transportation ResearchBoard, Washington, D.C., 60-68.

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


8. IRC: 106-1990, Guidelines for Capacity of Urban Roads in Plain Areas,Indian Roads Congress, New Delhi.

9. Issac, K.P. (1995). “Studies on Mixed Traffic Flow Characteristics UnderVarying Composition”. Ph.D thesis (Unpublished), Bangalore University.

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14 . Marwah, B. R. and Singh, B. (2000). “Level of service classification for urbanheterogeneous traffic: A case study of Kanpur metropolis.” TransportationResearch. Circular E- C018: 4th International Symposium on Highway Capacity,Maui, Hawaii, 271-286.

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19 . Sumner, R., Hill, D., and Shapiro, S. (1984). “Segment passenger car equivalentvalues for cost allocation on urban arterial roads”. Transportation Research-A., Vol. 18A. No. 5/6, 399-406.

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SERVICE VOLUME STANDARDS FOR URBAN ROADS

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

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