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Estimating Capacity for Eight-lane Divided Urban Expressway under Mixed-Traffic Conditions Using Computer Simulation

Ravikiran Puvvala Project Associate,

Department of Civil Engineering,

Birla Institute of Technology and Science, Pilani, Rajasthan, India Email: raki.bits@gmail.com Phone: +919694741579

Balaji Ponnu

Former Graduate Student,

Department of Civil Engineering,

Birla Institute of Technology and Science, Pilani, Rajasthan, India Email: balaji.bitsp@gmail.com Phone: +917598231953

Shriniwas Arkatkar*,

Assistant Professor,

Department of Civil Engineering,

Birla Institute of Technology and Science, Pilani, Rajasthan, India Email: sarkatkar@gmail.com

Phone: +918058321357

Velmurugan S.

Scientist E-II, Traffic Engineering and Safety Division,

Central Road Research Institute (C.R.R.I.)

Mathura Road, New Delhi E-mail: vms_04@yahoo.co.in, vms.crri@nic.in

Tel: 91-11-26845976, Fax: 91-11-26845943

*Corresponding Author

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Estimating Capacity for Eight-lane Divided Urban Expressway Under Mixed-Traffic Conditions Using Computer Simulation R. Puvvala a, B. Ponnub, 1 S. Arkatkarc*, Velmurugan S.d

(a,b,c)Department of Civil Engineering, Pilani, Rajasthan, India,

dCentral Road Research Institute (C.R.R.I.), Mathura Road, New Delhi, India.

Abstract

Expressways in India are vastly different from other roads of the country based on roadway and traffic

conditions and additionally, there is no perfect strict lane discipline. Nevertheless, there is not much research

literature available specific to these categories of roads in India. The knowledge of roadway capacity is an

important basic input required for planning, design, analysis and operation of roadway systems. Hence, this

work aims to model traffic flow on Indian urban expressways with specific reference to Delhi-Gurgaon

expressway and estimate its capacity using the micro-simulation model, VISSIM. Field data collected on traffic

flow characteristics on expressways are used in calibration and validation of the simulation model. The

validated simulation model is then used to estimate Passenger Car Unit (PCU) values for different vehicle types

and derive capacity value. It was found that the PCU value for each of the vehicle categories decreases with

increase in vehicular flow under uncongested regime. It was also found that the PCU value for heavy and larger

vehicle types are higher under congested regime as compared to their respective PCU values near capacity. The

capacity of a eight-lane divided urban expressway in level terrain with 14.0 m wide road space is found to be

about 9700 PCU/h, for one direction of traffic flow.

1. Introduction

An urban expressway is defined as an arterial highway for motorized traffic, with divided

carriageways for high speed travel, with full control of access and usually provided with

grade separators at location of intersections. They are the highest class of roads in the Indian

Road Network. Higher design speeds, restriction on slow moving vehicles, varied traffic

composition with high amount of cars characterize these roads. With such operational

difference and with many urban expressways such as Delhi-Gurgaon, Eastern and Western

Express Highways in Mumbai being in existence and more number of them such as the

Yamuna and Kundli-Manesar-Palwal Expressways being built, a thorough understanding of

their operation assumes high importance. Traffic flow in Indian expressways is quite

interesting to be studied due to two reasons. First, the traffic is multi-class with vehicles such

as cars and pickups with their high maneuverability and heavy vehicles such as trucks and

buses. The speeds of these vehicles may vary from 40 to over 100 km/h. Traffic movement

on Indian expressways may be said to be quasi-lane disciplined, with some vehicles

following a lane-based driving and many others not. Such a lack of lane discipline can be

attributed to combination of factors viz. enforcement and education. Indian drivers are not

educated about the importance of sticking to their lanes other than for overtaking and

improving driving speeds. There are neither video cameras mounted at select locations of the

roads nor a central monitoring system that reports violations. Consequently vehicles tend to

*Corresponding author. Email: sarkatkar@gmail.com

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take any lateral position along the width of roadway, based on space availability. When such

different types of vehicles with varying static and dynamic characteristics are allowed to mix

and move on the same roadway facility, a variable set of longitudinal and transverse

distribution of vehicles may be noticed from time to time. Hence expressways remain as a

partially heterogeneous traffic characterized by poor lane discipline. Given this significant

difference in the nature of traffic flow in expressways in relation with other kind of roads in

India as explained above, there are not much research work on either design or operation of

these facilities.

Under heterogeneous or partially heterogeneous conditions, expressing traffic volume in

terms of vehicles per hour per lane is irrelevant as there is either no or partial lane discipline.

One way to represent the heterogeneous traffic flow is to express each vehicle category in

terms of the interference it causes to the flow in terms of a standard vehicle category such as

car. Such a measure is called the Passenger Car Unit (PCU) as known in India or Passenger

Car Equivalent (PCE) worldwide. In general, heterogeneous flows are expressed as PCU per

hour taking the whole width of the carriageway into account. But there are many

complexities in expressing a vehicle as its equivalent PCU. PCU values of any vehicle

category are highly sensitive to the given roadway and traffic conditions such as roadway

width, traffic composition, flow and speed of the traffic stream. Hence, adopting a single

PCU for a given vehicle is not accurate but rather a dynamic or stochastic PCU that accounts

for all the factors should be adopted.

For correct estimation of PCU values, it is necessary to study accurately the influence of

roadway and traffic characteristics and the other relevant factors on vehicular movement.

Study of these complex characteristics in the field is difficult and time consuming. Also, it

may be difficult to carry out such experiments in the field covering a wide range of roadway

and traffic conditions. Hence, it is necessary to model road–traffic flow for in depth

understanding of the related aspects. The study of these complex characteristics, which may

not be sufficiently simplified by analytical solution, can be done using alternative tools like

computer simulation. Simulation is already proven to be a popular traffic-flow modeling tool

for developing various applications related to the traffic flow on roads. VISSIM is one of the

most widely accepted simulation package for simulation of both homogenous and

heterogeneous traffic flows.

There is a very less literature available on capacity of expressways which has also found a

mention in the 11th Five Year plan (2007-2012) report. Considering all the above, it is

imperative and timely to initiate a study on the capacity of expressways depending upon the

carriageway/roadway widths and other relevant parameters. Thus, this study is aimed at

developing capacity estimates for urban expressway segments under varying traffic

conditions, which will help in meeting the country’s need for design, analysis, operations and

management of expressways. To this end, the traffic flow on the Delhi-Gurgaon Expressway

has been studied and modeled through simulation. The objective of this study is to estimate

and study the possible variation of Passenger Car Unit (PCU) values of different categories of

vehicles at various traffic flow levels under heterogeneous traffic conditions prevailing on

basic urban expressway section of India in level terrain. VISSIM is used to model the

heterogeneous traffic-flow. Field data collected on traffic flow characteristics such as free

speeds, acceleration, lateral clearance between vehicles, etc. are used for calibration and

validation of the simulation model in VISSIM. The validated simulation model is then used

to derive Passenger Car Unit (PCU) values for different types of vehicles. The estimated PCU

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values for different vehicle types were then used to derive a capacity estimate in PCU/h for

the observed traffic composition.

2. Literature Review

Simulation has been recognized as one of the best tools for modeling of traffic flow under

homogeneous as well as heterogeneous conditions. Fellendorf and Vortisch (2001) presented

the possibilities of validating the microscopic traffic flow simulation model VISSIM, both on

a microscopic and a macroscopic level in homogeneous flows. Hossain (2004) calibrated the

heterogeneous traffic model in VISSIM to match saturation flows measured by video at an

intersection in the city of Dhaka, Bangladesh. Matsuhashi et. al. (2005) assessed the traffic

situation in Hochiminh city in Vietnam, using image processing technique and traffic

simulation model (VISSIM). It was found that the high number of motorcycles in the network

interfere with other vehicles which reduces average speed of traffic stream drastically.

Further, the simulation model was applied for deriving the benefits of increasing the share of

public transport. Lownes and Machemehl (2006) performed sensitivity of car-following

parameters in VISSIM. Huang and Tang (2007) calibrated Verkehr in Staedten simulation

using VISSIM, for a bay rapid transit project by visual verification and acceptance criteria

were defined for volumes. Zhang et al. (2008) conducted a study using VISSIM to evaluate a

proposed feedback- based tolling algorithm to dynamically optimize High Occupancy Toll

(HOT) lane operations and performance. Ishaque and Noland (2009) demonstrated the

feasibility of modeling vehicle-pedestrian interactions using VISSIM and provided a tool for

evaluating policies that affect both vehicle and pedestrian flows.

Many researchers tried to build their own simulation software for studying heterogeneous

traffic flow. Arasan and Koshy (2005) developed a heterogeneous-traffic-flow simulation

model to study the various characteristics of the traffic flow at micro level under mixed

traffic condition on urban roads. The vehicles are represented, with dimensions, as

rectangular blocks occupying a specified area of road space. The positions of vehicles are

represented using coordinates with reference to an origin. For the purpose of simulation, the

length of road stretch as well as the road width can be varied as per user specification. The

model was implemented in C++ programming language with modular software design. The

model is also capable of showing the animation of simulated traffic movements over the road

stretch. Dey et al. (2008) developed a simulation program coded in Visual Basic language.

The authors performed number of simulation runs to determine the capacity of a two-lane

road and to study the effect of traffic mix, slow moving vehicles and directional distribution

of traffic on capacity and speed. Mathew, and Radhakrishnan, (2010) presented a

methodology for representing nonlane-based driving behavior and calibrating a micro-

simulation model for highly heterogeneous traffic at signalized intersection. Calibration

parameters were identified using sensitivity analysis, and the optimum values for these

parameters were obtained by minimizing the error between the simulated and field delay

using genetic algorithm. Velmurgan et al., (2010) studied free speed profiles and plotted

speed–flow equations for different vehicle types for varying types of multi-lane highways

based on traditional and microscopic simulation model VISSIM and subsequently estimated

roadway capacity for four-lane, six-lane and eight-lane roads under heterogeneous traffic

conditions with reasonable degree of authenticity. Arkatkar (2012) analyzed heterogeneous

traffic flow using microscopic simulation technique. Bains et al., (2012) developed a model

in VISSIM for simulating the traffic on Mumbai-Pune expressway and estimating the

capacity values.

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In the developed world such as Europe, United States, Canada and Australia the traffic

flow is homogeneous with cars and trucks being the two main categories of vehicles.

Researchers in traffic flow in these countries have used Passenger Car Equivalents (PCE) for

converting trucks into equivalent car units for capacity and level of service estimation. A

review of existing literature reveals that many different methods have been used for

estimation of PCE values. PCE can be estimated based on (i) delay (Craus et al (1980), Keller

and Saklas(1984)), (ii) speed (Linzer et al(1979), Van Aerde and Yagar(1984)), (iii) density

(Huber (1982), Webster and Elefteriadou(1999)), (iv) headway (Werner and Morall(1976),

Krammes and Crowley(1986)) and (v) queue discharge(Al-Kaisy et al(2002), Al-Kaisy et

al(2005)).As all these studies have confined themselves to estimation of PCE for heavy

vehicles only (Trucks or Buses) under homogeneous traffic, the results of these studies are

not applicable for Indian conditions where heterogeneous traffic with ten different categories

of vehicles.

There have been many studies in India on where PCE is called PCU or Passenger Car Unit.

Chandra et al. (1995) and Chandra (2004) proposed a methodology to derive dynamic PCU

values based on the relative space requirement of a vehicle type compared to that of a

passenger car as the basis of measure. They developed a mathematical model for PCU

estimation as the ratio of the speed and projected area ratio of car and subject vehicle.

Chandra and Kumar (2003) studied the effect of road width on PCU of vehicles on two-lane

highways and found that the PCU value increased with increase in width of roadway. Basu et

al. (2006) developed a Neural Network (NN) model to study the non-linear effect of traffic

volume and its composition level on the stream speed. The effect of traffic volume and

composition on PCE for different types of vehicles under mixed traffic condition was

investigated for an urban mid-block section. It was found that the PCE of a vehicle type

varies in a non-linear manner with traffic volume and composition. There have been many

studies aimed at assessing the roadway capacity for varying carriageway widths including

single lane, intermediate lane, two-lane bi-directional, four-lane and six-lane highways

(Tiwari, et. al., (2000), Velmurugan et. al. (2002), Chandra and Kumar (2003), Chandra

(2004), Velmurugan et. al. (2009) and Arkatkar and Arasan (2010)) during the last two

decades.

In regard with expressways capacity ‘Highway Capacity Manual (HCM)-2000’ is the

document considered to be authentic (MORTH, 2010). In absence of capacity and level-of-

service guidelines for expressway segments between interchanges, in India, MORTH has

given a user friendly method considering the default values given in HCM-2000 and

subjective adoption of the necessary parameters. Based on the review of literature both at

National and International levels, the following important points have been identified: (1)

Most of the researchers in India in the past aimed at assessing the roadway capacity for

varying carriageway widths, but only a limited number of studies have been carried out to

determine the capacity of expressways in India, and (2) The guidelines suggested by HCM

may not be applied directly under Indian conditions, as the conditions are vastly different on

Indian expressways. In view of the above points and considering the fact that a huge network

of expressways has been planned in future, there is an urgent need to determine the capacity

of such roads for appropriate design.

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3. Objective and scope

The general objective of the research work reported here is to quantify the vehicular

interaction in terms of Passenger Car Unit (PCU) values of different categories of vehicles

and then deriving capacity estimates for a eight-lane divided urban expressway under

heterogeneous traffic conditions prevailing in India. A commercially available simulation

model, named, VISSIM, is used to study the vehicular interactions, at micro-level, over a

wide range of traffic flow conditions. Field data collected on traffic flow characteristics such

as free speed, acceleration, lateral clearance between vehicles, etc. are used in calibration and

validation of the simulation model. The validated simulation model is then used to derive

Passenger Car Unit (PCU) values for different types of vehicles. The effect of variation of

traffic volume on PCU value is studied by deriving PCU values for the different types of

vehicles, for a set of traffic-volume levels falling over a wide range. Finally, a check for the

accuracy of the estimated PCU values is also made.

4. The Simulation Model

Simulation technique is one of the well-known techniques to study traffic flow and its

characteristics. Simulation gives us the advantage of being able to study how the created

model behaves dynamically over time or after a certain span of time. Traffic characteristics

on roads as a system vary with time and with a considerable amount of randomness and

simultaneous interactions. The most difficult and critical process in simulating any traffic

flow scenario or for that matter any physical phenomena is to calibrate the simulated model

to capture or replicate the ground reality with the desired accuracy. Given this, the results

obtained through a validated simulation model would be more accurate than those obtained

through analytical results.

The simulation model followed in the present study is shown in the form of a flow chart in

Figure 1. Data in the form of videos collected from the study site was analyzed and this

information is used for building the simulation model in the software VISSIM 5.40. Then the

model was calibrated and validated for rendering it suitable for replicating the conditions at

site. Using this validated simulation model, roadway capacity estimation PCU estimation

were done.

5. Model Calibration and Validation

Model calibration is an iterative process of comparing the model to reality, making

adjustments (or even major changes) to the model, comparing the revised model to real

conditions, making additional adjustments, comparing again, and so on. The comparison of

the model to reality is carried out by tests that require data on the system’s behavior plus the

corresponding data produced by the model. The input data required for the above mentioned

heterogeneous traffic-flow model are related to four aspects viz. road geometrics, traffic

characteristics, driver reaction time and vehicle performance. The power of simulation as a

tool for the study of traffic flow lies in ability of the model to include the effect of the random

nature of traffic. Hence, the random variables associated with traffic flow such as headway

distribution are expressed as frequency distributions and input into the simulation model.

These data, pertaining to one direction of traffic flow, was collected at a selected stretch of an

expressway for model calibration and validation purposes.

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Fig. 1. The Simulation Model

6. Study stretch & Data collection

The Delhi-Gurgaon Expressway is an 8-lane divided facility that connects the city of Delhi

with one of its busiest suburbs, Gurgaon. The traffic on the expressway was video graphed

from a vantage point, during both peak and non-peak hours on 20th March, 2012. The study

stretch was selected after conducting a reconnaissance survey to satisfy the following

conditions: (1) The stretch should be fairly straight, (2) Width of Roadway should be

uniform, and (3) There should not be any direct access from the adjoining land uses. The

stretch is a four lane divided road with 5m wide central median. The width of main

carriageway, with bituminous surfacing, for each of the two directions of traffic flow, is 14.0

m. The 1.5 m wide shoulder, on both sides, is paved with bituminous mix. Also, there is no

direct access from the adjoining service road because of raised kerbs as shown in fig. 2. The

overall roadway condition has also been depicted in the form of a Google map in fig 3.

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Figure 2: Snapshot of Video-captured data on Delhi-Gurgaon Expressway

Figure 3: Google image of a study-section on Delhi-Gurgaon Expressway

Free-flow speeds were ascertained by observing 100 vehicles each in different

categories of vehicles during non-peak hours, when a flow is less than 200 vph prevailed.

Then on the same day, the traffic flow on the road was observed for two hours in the evening

peak period from 16:24 to 18:24 hours. This data was then used for the purpose of model

validation. A snapshot of the video-captured data is depicted in Fig 2. The field data input

required for the model were collected at the above location with the help of a digital video

camera for capturing the traffic flow movement for a total duration of one hour. The video

was then analyzed at a speed one-eighth of the actual speed to enable recording and

measurement of data. The traffic flows observed were 7200 vph and 8573 vph in the first and

second hours respectively. Composition of the traffic stream is given in Column (2) of Table

1. Macroscopic parameters such as flow and speed were extracted from the videos at a rate of

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25 frames per second for achieving a high accuracy for every 20 seconds time interval. This

data then was aggregated to obtain macroscopic parameters such as flow and speed for every

1-min, 5-min, 30-min and 60-min intervals for the purpose of model validation. The speeds

of the different categories of vehicles were measured by noting the time taken by the vehicles

to traverse a trap length of 30 m. The observed maximum, minimum and mean speeds of

various classes of vehicles and the corresponding standard deviations are shown in columns

(3), (4), (5) and (6) respectively of Table 1. The speeds of the different categories of vehicles

were measured by noting the time taken by the vehicles to traverse a trap length of 30 m

using video graphic data. The free speeds of the different categories of vehicles were also

measured for the traffic under free-flow conditions.

Table 1 Input data for heterogeneous traffic flow simulation

Vehicle

Type

(1)

Composition

(%)

(2)

Observed speeds,

km/h

Vehicle Dimension,

(m)

Lat. clear.

share, (m)

Max.

Speed

(3)

Min.

Speed

(4)

Mean

Speed

(5)

Std.

Deviation

(6)

Length

(7)

Width

(8)

Min.

(9)

Max.

(10)

BC 20.80 103 78 90 4.00 4.8 1.9 0.40 0.60

SC 50.00 96 65 82 5.33 4.0 1.6 0.30 0.40

Two-wheeler 22.50 87 33 58 8.33 1.8 0.60 0.10 0.30

Three-wheeler 3.30 63 38 50 4.00 2.6 1.4 0.30 0.40

Bus 2.20 93 64 79 5.00 10.3 2.5 0.40 0.60

LCV 0.60 80 63 73 3.33 5.0 1.9 0.40 0.60

Truck 0.60 69 48 60 4.00 7.5 2.5 0.40 0.60 Note: LCVs – Light Commercial Vehicles; BC: big cars and SC: Small cars.

The overall dimensions of all categories of vehicles adopted from literature (Arasan and

Koshy, 2005) are shown in columns (7) and (8) of Table 1. Any vehicle moving in a traffic

stream has to maintain sufficient lateral clearance on the left and right sides with respect to

other vehicles/ curb/ median to avoid side friction. These lateral clearances depend upon the

speed of the vehicle being considered, speed of the adjacent vehicle in the transverse

direction, and their respective vehicle categories. The minimum and maximum values of

lateral-clearance share values adopted from an earlier study (Arasan and Koshy, 2005), are

given in columns (9) and (10) of Table 1, respectively. The minimum and the maximum

clearance-share values correspond to zero speed and free speed conditions of corresponding

vehicles, respectively. The lateral clearance share values in between the above mentioned

traffic flow levels is obtained using linear interpolation in the model. The lateral-clearance

share values are used to calculate the actual lateral clearance between vehicles based on the

type of the subject vehicle and the vehicle by the side of it. For example, at zero speed, if a

motorized two-wheeler is beside a big car, then, the clearance between the two vehicles will

be 0.1 + 0.4=0.5 m. The field observed acceleration values of the different categories of

vehicles over different speed ranges used for simulation are shown in Table 2.

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Table 2 Acceleration values for different vehicle categories

Vehicle

Type

0-30 km/hr.

(m/s2)

30-60 km/hr.

(m/s2)

Above 60 km/hr.

(m/s2)

Big Car 2.15 1.80 1.10

Small Car 2.00 1.60 1.10

Two-wheeler 1.10 0.70 0.45

Three-wheeler 0.80 0.30 0.25

Bus 1.40 1.00 0.45

LCV 1.30 0.80 0.55

Truck 1.00 0.62 0.46 Note: LCVs: Light Commercial Vehicle

7. Simulation Model Development

A model which accurately represents the design and operational attributes of the study

stretch in the simulation software is known as the ‘base model’. The design attributes can be

road configuration (carriageways, medians & shoulders), horizontal curvature and vertical

gradient. Operational attributes can be the vehicle or driver characteristics and the traffic flow

data. When this base model is calibrated and validated to replicate the actual or ground

conditions, the model can be used to study different characteristics that were not defined by

the user as an input. For example, the width of the road can be defined and in turn the

capacity of this road could be measured. The validated base model can also be used to

develop a simulated scenario which is desired to be known. The base model development

involves the following steps: (i) Development of Base Link/Network, (ii) Defining Model

Parameters, (iii) Calibrating the Network, and (iv) Validating the Model

7.1 Development of Base Link/Network.

Development of a link/network that accurately depicts the physical attributes of a test site

is an important stage in the modeling process. The basic key network building components in

VISSIM are links and connectors. In the present simulation model, a unidirectional four lane

test section link spanning 1000 m was created representing the study stretch located on the

Delhi-Gurgaon Expressway as explained above. Additionally, extra links of length 200 m

each were provided at the beginning and end of the main stretch for buffering process. The

test section and the buffer links were joined using the connectors. The buffer links provided

the spatial warm up sections for vehicles entering and exiting the test section thereby

ensuring accurate results.

7.2 Defining Model Parameters

7.2.1. Vehicle model.

Vehicle model deals with defining the dimensions of each vehicle type that are plying on

the test stretch and are hence considered for the simulation. It is also used to define the

acceleration values of vehicles. The dimensions namely the width and the length are

considered for the present simulation model as per the description given in Table 1. The

acceleration values are given as per Table 2.

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7.2.2 Desired speed distribution.

The simulation model requires the desired speed parameters of vehicles to initialize and

assign the speed during their placement in the simulation stretch. The desired speed

parameters obtained through field measurement during lean traffic periods on the study

stretch, are shown in Fig.3. The desired speed of cars depends on the make of the car, the age

of the car, the level of maintenance of the car, the age, sex and the behavior of the driver.

These factors fall over a wide range under Indian conditions. This is the reason for a wide

range in the value of desired speeds of cars. Hence, the cars were further divided into two

categories: small cars and big cars based on their sizes. The desired speed distribution for

each vehicle category was given as input for the simulation model in VISSIM. The maximum

and minimum values of the speeds and distribution between these values were defined in the

model. The desired speed profiles for the vehicle types such as small cars and big cars are

shown in Figure 4, as examples. The desired distribution curve for any vehicle category is

generally a ‘S’ shaped curve as shown in the figure 4. Adequate care was taken to ensure that

the speed distribution defined in VISSIM represented the values observed in the field.

7.2.3. Vehicle composition and Vehicle Flow.

Vehicle composition and vehicle flow based on field observations is given as an input to

simulation model for the given time interval.

7.2.4. Driving behavior characteristics.

The driving behavior characteristics mainly include these two features viz. car following

behavior and lateral distance. The psycho-physical driver behavior based Wiedermann-74

Car-following model has been used for simulating the vehicle following behavior. The

parameters of this car-following model including safety distance during standstill, additive

and multiplicative parts of safety distance are given in Table 3. These values were chosen

based on trial-and-error method, taking the values obtained in the study by Mathew, and

Radhakrishnan, (2010) as base values. For defining the lateral distance between the vehicles

the location of the vehicle on a lane, minimum lateral distance at different speeds etc. were

given as input (column number 9 and 10 of Table 1).

(smal

l Cars)

(Bi

g Cars)

Fig. 4. Desired speed distributions for Cars adopted in VISSIM

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Table 3: Calibrated parameters (Wiedemann-74 model) for the present study

Parameter Calibrated values *Default values

Average standstill distance 1.35 m 2.0 m

Additive part of safety distance 0.25 2.0

Multiplicative part of safety distance 0.35 3.0

Minimum Look-ahead distance 127 m 0.0 m

Maximum Look-ahead distance 250 m 250 m *Source: PTV. 2011: VISSIM Version 5:40-01 user manual

7.3. Calibration of the Simulation Model.

Calibration is a process of adjusting the model to replicate observed data and observed site

conditions to a sufficient level to satisfy the model objectives. This process involves adjusting

the following characteristics: desired speed distribution, acceleration/deceleration of vehicle,

mechanical characteristics of the vehicle, minimum safety distance, minimum lateral distance

and driving behavior characteristics. By giving these parameters as an input to simulation

model, simulation runs have to be carried out in order to estimate the output. In the present

simulation model, the outputs were the traffic flows and average speeds of the vehicles for 10

different random seed values. All the simulations were run for a total time of 7400 seconds

including a temporal warm-up period of 200 seconds to ensure accurate simulation results.

Flow for each 1 hour from the two hour field data was fed which improved the degree of

match between the observed and the simulated. As explained above, a different driving

behavior was considered for each vehicle type to account for heterogeneity in the traffic.

There was no perfect strict lane discipline among the vehicles as observed from the video

(Figure 2). Hence, an entire road width based simulation where there was a one lane model

having an effective width of three lanes was considered in the simulation. Thus each vehicle

was free to choose any lateral position and overtake from any side during the simulation on

this three lane width without any lane discipline similar to site conditions.

The minimum look ahead distance which defines the distance a vehicle can see forward in

order to react to vehicles in front or to the side of it was set to a value of 127 m was found to

be appropriate for the present situation. This value of 127 m was taken based on the

calibration and validation study conducted by Mathew and Radhakrishnan, (2010). The other

values were chosen as per the defaults considered in VISSIM which produced the observed

conditions with required accuracy. The simulated values and the observed values (speed, flow

and area occupancy) were compared and the error was computed. If the error was within the

limits, the calibration process was stopped or otherwise the parameters were modified and

simulation runs were carried out. This process was repeated and the simulation runs were

made till the error was within the satisfactory limits. The calibration process in the form of a

flow chart is shown in Figure 5.

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Fig. 5. Calibration of the simulation model

7.4. Validation of the Simulation Model.

Validation is the process of checking the results obtained from the calibrated model in

terms of simulated values against field measurements for parameters such as traffic volumes

average speeds and area-occupancies. The observed traffic volume and composition was

given as input to the simulation process. The simulation runs were made with 10 random

number seeds for a total run time of 7400 seconds including temporal warm-up period of 200

seconds to ensure accurate simulation results. A sample simulation run is shown in Figure 6.

Fig. 6. A snapshot of simulation runs in VISSIM

Temporal Area occupancy,

speed & Traffic Volume

NO

YES

Estimation of Output

Flow, Speed and area occupancy

Simulation Run in VISSIM

Comparison with

observed data

Define Simulation

Parameters

Stop

If Error <

Defined

Accuracy

1. Desired speed distribution

2. Desired acceleration & deceleration

3. Weight of the vehicle

4. Minimum Safety Distance

5. Min. and max. lateral gaps

6. Traffic volume and composition

7. Vehicle dimensions (length and width)

8. Roadway geometry

9. Total simulation time

Speed

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The average speeds of vehicles from a single run was noted and then the average speed for

each vehicle category from all the ten runs were taken as the final output from the model. The

inter-arrival time gaps of the heterogeneous traffic flow of vehicles was assumed to follow

negative exponential distribution (Arasan and Koshy, 2003) and the free speeds of different

categories of vehicles, based on the results of an earlier study (Velmurgan et al. 2010)), was

assumed to follow normal distribution. These distributions formed the basis for input of the

two parameters for the purpose of simulation.

To check for the temporal validity of the model, the different parameters such as vehicle

speeds, flows and area occupancy values obtained using simulation model were compared

with the corresponding field observed speeds, flows and area occupancy values for every 1-

minute, 5-minute, 15-minute and 60-minute intervals within the two hours (observed traffic).

In the present simulation model, the outputs were the traffic flows, average speeds and area

occupancy values of the vehicles for 10 different random seed values. The comparison of the

observed and simulated speeds, flows and area occupancy values for an observed traffic

conditions are shown in Figure 7 through Figure 9 for every 5-minute and for every 15-

minute intervals within the two hours (observed traffic) in Figure 10 through Figure 12, as

examples. The graphs were plotted based on the average speed and flow values, derived

through the output of ten random runs. The same check was also done for 30-minute and 60-

min intervals with respect to each of the vehicle types. It can be seen that in all the cases, the

simulated speed values, simulated flow values and simulated area-occupancy values are

found to be satisfactorily matching with the speeds, flows and area-occupancy values as

observed in the field for all the vehicle categories. The procedure given by Arasan and

Dhivya (2010) was used for estimating the area occupancy values for observed and simulated

traffic conditions.

15

Fig. 7. Comparison of Observed and Simulated Stream Speeds for every 5-min

Fig. 8. Comparison of Observed and Simulated flows for every 5-min

Fig. 9. Comparison of Observed and Simulated Area-Occupancies for every 5-min

0

20

40

60

80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

simulated observedA

ver

age

Sp

eed

(km

/h)

5-minute Interval

0

2000

4000

6000

8000

10000

12000

1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

simulated observed

5-minute Interval

Tra

ffic

Flo

w (

veh

icle

s/h

)

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Simulated Observed

5-minute Interval

Are

aO

ccu

pan

cy (

%)

16

Fig. 10. Comparison of Observed and Simulated speeds for every 15-min

Fig. 11. Comparison of Observed and Simulated flows for every 15-min

Fig. 12. Comparison of Observed and Simulated Area-Occupancies for every 15-min

The details pertaining to the statistical validation through the paired t-test for different

parameters: speeds, flows and area occupancy (simulated and observed traffic conditions for

17

every 5-minute, 15-minute and 30-minutes intervals) are given in the Table 4. The estimated

t-statistic, p and t-critical values obtained from standard t-distribution table are also given in

Table 4 for 5% level-of-significance (95% confidence level) along with their respective

degrees-of-freedom.

Table 4. Model Validation: Comparison of Observed and Simulated flows and speeds

Traffic flow

parameter

Traffic

interval

t-

statistic

value

t-

critical

value

p-value Critical

p-value

Degrees of

freedom

Speed 5-min 1.52 2.07 0.22746 0.05 23

Speed 15-min 0.997 2.37 0.35308 0.05 7

Speed 30-min 1.03 3.18 0.38017 0.05 3

Flow 5-min 0.95 2.07 0.35181 0.05 23

Flow 15-min 0.801 2.37 0.44942 0.05 7

Flow 30-min 0.558 3.18 0.61559 0.05 3

A-O 5-min 0.539 2.07 0.59831 0.05 23

A-O 15-min 0.321 2.37 0.75729 0.05 7

A-O 30-min 0.220 3.18 0.83989 0.05 3 Note: A-O-Area Occupancy

From the table, it may be noted that, in all the cases, the value of estimated t-statistic is

lesser than the critical value of t-statistic obtained from standard t-distribution table. This

implies that there is no significant difference between the observed and simulated traffic flow

parameters, for a level of significance of 5%.

7.4.1. Model Application

The VISSIM model can be applied to study various traffic scenarios for varying roadway

and traffic conditions. In this study, the application of the model is to study the relationship

between traffic flow and speed on an urban expressway with six categories of vehicles as

given in column number 2 of Table 1. It has also been used to quantify the relative impact of

each category of vehicle on traffic flow in comparison to a reference vehicle category car, by

estimating their PCU values at different volume levels under heterogeneous traffic conditions

prevailing on an urban expressway considered for the study. Further, the estimated PCU

values were used to derive capacity estimate for one direction of traffic flow on a chosen

urban expressway, based on the traffic composition observed in the field through data

collection.

7.4.2. Speed-Flow relationships and Capacity.

One of the basic studies in traffic flow research is to examine the relationship between

speed and volume of traffic. The capacity of the facility under different roadway and traffic

conditions can be estimated using these relationships. In this study, speed-flow relationship

was developed using the validated simulation model for a heterogeneous flow with vehicle

composition and roadway conditions same as that observed in the field. The average speed of

the stream was plotted for different simulated volumes, starting from 500 vph to the capacity

of the road.

The following procedure was adopted for finding the capacity of the facility for developing

the speed-flow relationships. During successive simulation runs with increments in traffic

18

flow from near-zero volume level, there will be a commensurate increase in the exit volume

at the end of simulation stretch. After a specific number of runs, the increments in the input

traffic volumes will not result in the same increase in the exit volume. Such a decrease in exit

volume (in spite of increase in the input) in successive runs indicates that the facility has

reached its capacity. The speed-flow relationships, thus developed based on the every 1-

minute and 5-minute intervals speed and flow data for a eight-lane divided expressway

(roadway space based on the four lanes in one direction of traffic flow) are depicted in

Fig.13a and Fig. 13b, respectively. The 1-minute intervals speed and flow data (120 points)

and 5-minute intervals speed and flow data (24 points) obtained from the observed data for

two hours was also plotted in figures 13a and 13b. It is clear from the figures, that the curves

follow the well established trend. These graphs were plotted based on the average speed and

flow values, derived through the output of ten random runs. The estimated values of capacity

(vehicles/hr/direction), thus obtained using simulation model, based on every 1-minute and 5-

minute interval data, for the observed traffic composition, are given in Table 5.

Fig 13a Speed-flow relationship for every 1-minute traffic flow data

Fig 13b Speed-flow relationship for every 5-minute traffic flow data

19

Table.5. Estimated roadway capacity of eight- lane divided urban expressway

Flow Type Traffic Composition Estimated Capacity,

veh/h/dir

Observed

(5-min interval)

Heterogeneous

(Column 2 of Table 1)

9720

Simulated

(5-min interval)

Heterogeneous

(Column 2 of Table 1)

9606

Observed

(1-min interval)

Heterogeneous

(Column 2 of Table 1)

10740

Simulated

(1-min interval)

Heterogeneous

(Column 2 of Table 1)

10470

From Table 5, it may be observed that the capacity estimate obtained using 1-minute

interval data is significantly higher than the capacity estimate obtained using 5-minute

interval data. This is quite intuitive: when the interval considered is smaller (1-minute in this

case), the flow levels are multiplied by higher factor for converting it into hourly flows,

which may result in a higher capacity value. In the case of 1-minute speed-flow relationship,

there are more number of points in the congested regime as compared to the 5-minute speed-

flow relationship. This may be attributed to the distribution of more number of speed and

flow points over a wider range. The capacity values obtained from every 1-minute and 5-

minute observed and simulated speed and flow data are found to be matching satisfactorily.

7.4.3 Estimation of PCU values.

As explained in the introduction, capacity of a highway facility with heterogeneous traffic

flow with vehicles of widely varying static and dynamic characteristics is best expressed in

terms of PCU/hr. Different vehicle categories such as buses, light commercial vehicles,

trucks, motorized two-wheelers and motorized three wheelers are expressed into equivalent

PCU. This necessitates an accurate estimation of PCU, which varies dynamically with

various traffic flow parameters such as stream speed, vehicle composition and volume-

capacity ratio. Chandra (2004) developed the concept of dynamic PCU considering the

various traffic interactions and flow characteristics. The PCU for a vehicle can be calculated

using Equation (1)

(1)

where, PCUi is the PCU of the subject vehicle i; Vc =Average speed of cars in the traffic

stream, Vi= Average speed of subject vehicles i; Ac= Projected rectangular area of a car as

reference vehicle and Ai= Projected rectangular area of the vehicle type i. For the purpose of

this study, the projected rectangular area of the car, (Ac) (reference vehicle) was considered

as the average value of the rectangular area of big cars and small cars. The average speed

(Vc) of the car (reference vehicle) for the given flow level, was estimated as the weighted

average of the speeds of big cars and small cars, based on their composition in the traffic

composition. The PCU values of different vehicle types were estimated using equation 1,

based on the average speed and flow values, derived through the output of ten random runs.

To emphasize their dynamic nature, PCU values for different categories of vehicles under

heterogeneous traffic conditions at different volume-capacity (V/C) ratios in the uncongested

i

c

i

c

i

AA

VV

PCU

20

regime (upper part of speed-flow relationship as shown in figure 13a) and congested regime

(lower part of speed-flow relationship as shown in figure 13a) were estimated using

simulation. The capacity values for the calculation of these V/C ratios were obtained from the

speed-flow graphs (figure 13a). For the purpose of simulation, different traffic volume levels

corresponding to these V/C ratios with same composition as observed in the field (as in

column 2 of Table 1) were considered. Outputs obtained from simulation runs served as

inputs for Equation 1, for calculating the PCU values of different types of vehicles at

different volume levels. The computed values for uncongested and congested traffic regimes

are shown in Table 6 and Table 7, respectively. The variation of PCU values of different

vehicle categories, over traffic volume, as an example, is depicted in Fig. 14.

Table 6. PCU Estimates of different vehicles at different V/C ratios (Uncongested Regime)

Note :M.Th.W*--Motarized 3-wheeler M.T.W*--Motarized 2-wheeler

Table 7. PCU Estimates of different vehicles at different V/C ratios (Congested Regime)

Note :M.Th.W*--Motorized 3-wheeler M.T.W*--Motorized 2-wheeler

V/C Truck Bus L.C.V. M.Th.W. M.T.W.

0.12 3.59 4.26 1.47 0.81 0.19

0.23 3.41 4.08 1.36 0.79 0.19

0.35 3.33 3.98 1.33 0.75 0.19

0.46 3.32 4.06 1.32 0.71 0.18

0.58 3.28 3.93 1.35 0.70 0.18

0.70 3.08 3.87 1.31 0.68 0.17

0.81 3.03 3.99 1.31 0.65 0.17

0.93 3.04 3.69 1.28 0.63 0.16

0.96 3.22 3.74 1.29 0.62 0.16

0.96 3.02 3.82 1.31 0.61 0.16

0.99 3.02 3.66 1.37 0.60 0.16

1.00 2.86 3.71 1.30 0.60 0.15

V/C Truck Bus L.C.V. M.Th.W. M.T.W.

0.99 3.16 3.72 1.28 0.61 0.15

0.99 3.16 3.72 1.28 0.61 0.15

0.98 2.90 3.76 1.32 0.60 0.15

0.93 3.09 3.82 1.50 0.60 0.15

0.96 2.96 3.82 1.31 0.61 0.16

0.97 2.90 4.13 1.30 0.61 0.15

0.97 2.90 4.13 1.30 0.61 0.15

0.94 3.03 4.02 1.31 0.60 0.16

21

(Trucks)

(Buses)

(Motorized three-wheelers)

(Motorized two-wheelers)

Fig 14. Variation of estimated PCU values for different vehicle types

It can be seen from the tables 6 and 7 and fig. 14 that the PCU values are the highest for

buses, then for truck, then light commercial vehicles, then motorized three-wheelers and

motorized two-wheelers have the least PCU value, this being irrespective of the V/C ratio.

These results are quite intuitive: the heavier and larger is the vehicle, the lesser its

manoeuvrability, the greater its hindrance to other vehicles and greater is its PCU. It can also

be observed that the PCU value for a given vehicle category decreases as V/C ratio increases

for all the vehicle categories for the traffic flows in uncongested regime till capacity. This

phenomenon could be explained by the fact that the speed difference between the subject

vehicle category and the reference vehicle (car) decreases as V/C ratio increases. The PCU

value for heavy and larger vehicle types (buses, trucks, etc.) are found to be higher under

congested regime as compared to their PCU values near capacity. The PCU value for smaller

vehicle types (motorised three-wheelers and motorised two-wheelers) are found to be

relatively same under congested regime as compared to their PCU values near capacity.

Under congested traffic flow conditions, all the vehicles move very close to each other

resulting in frequent acceleration and deceleration. This condition leads to relatively higher

rate of reduction in the speeds of heavy vehicles (trucks, buses, etc.) as compared to smaller

vehicles. As a result of this, under congested traffic regime, the difference in percentage

speed change between cars and heavy vehicle types is higher as compared to the smaller

vehicle types, resulting in higher PCU values.

7.4.4.Disrtribution of PCU Values

The distribution of the PCU values for different vehicle types was also studied in detail and it

was found that for all the vehicle categories, for the given traffic flow level, the PCU values

estimated using simulation follow normal distribution, which was also reported by Al-Kaisy,

et. al. (2005). The corresponding normal probability and normality plots for trucks (heavy

22

vehicle) and motorized two- wheelers (smaller vehicle) are depicted in Figure 15, as

examples.

(Motorized two-wheelers)

(Trucks)

(Motorized two-wheelers)

(Trucks)

Fig 15.Normal distribution fit of PCU values for flow level of 4000 vehicles/h

7.4.5. Check for Accuracy of PCU Values

For the purpose of checking the accuracy of the PCU estimates for different vehicle

categories the following procedure was adopted. Firstly, PCU values were estimated for each

of the vehicle type using speeds of the reference vehicle (cars) and subject vehicle category

(for which the PCU is to be estimated), obtained from the observed data for every 5-minute

interval. Then the vehicles of the different categories were converted into equivalent PCUs by

multiplying the number of vehicles in each category, by the corresponding PCU values

(through every 5-minute observed data). The products, thus obtained, were summed up to get

the total traffic flow in PCU/hour based on the observed data for every 5-minute interval.

Secondly, the heterogeneous traffic flow with same vehicle composition and traffic flow

levels as observed in the field for two hours was simulated and the number of vehicles

passing the simulation stretch, in each category during each run was noted along with their

speeds for every 5-minute interval. PCU values were estimated for each of the vehicle type

using speeds of the refernce vehicle (cars) and subject vehicle category (for which the PCU is

to be estimated), obtained from the simulated data for every 5-minute interval.Then the

vehicles of the different categories were converted into equivalent PCUs by multiplying the

number of vehicles in each category, by the corresponding PCU values (through every 5-

minute simulation data). The products, thus obtained, were summed up to get the total traffic

flow in PCU/hour based on the simulated data for every 5-minute interval. Comparison of the

23

traffic flows measured in terms of PCU/h, based on the observed and simulated data, for the

flow levels as observed in the field, is shown in Figure 16. The PCU values of different

vehicle types were estimated, based on the average speed and flow values, derived through

the output of ten random runs.

Fig. 16. Comparison of Observed and Simulated PCU/hr for every 5-minutes

It can be seen that the traffic flow values in PCU/hour for observed and simulated scenarios

match fairly well, indicating the accuracy of the estimated PCU values. A paired t-test based

on the two values was also done in which the calculated value of t-statistic (t0) was 0.521.

The critical value of t-statistic for a level of significance of 0.05 for 23 degrees of freedom,

obtained from standard t-distribution table is 2.07. This implies that there is no significant

diffrence between the two sets of flows measures in terms of PCU/hour (based on observed

and simulated data sets) implying that the estimated PCU values for different vehicles are

accurate. Also, comparison of the estimated PCU values based on the observed and simulated

speed data for motorised three-wheelers and buses is depicted in fig. 17 and 18, respectively,

as examples. Based on the paired t-test results, it was found that there is no significant

diffrence between both the data sets at 5% level-of-significance, for 23 degrees of freedom,

further indicating the accuracy of PCU estimates. Also as an another check, the simulation

was run for 100% cars and then was compared with the equivalent value of traffic flow in

PCU/h. Comparison of the traffic flows measured in terms of PCU and in terms of number of

passenger cars, through speed-flow relationship, is shown in Figure 19. It can be seen that the

traffic flow in PCU/h and the cars-only flow in cars/h match to a greater extent, indicating the

accuracy of estimated PCU values.

Fig. 17. Comparison of Observed and Simulated PCU of Motorized three-Wheeler for every 5-min

24

Fig. 18. Comparison of Observed and Simulated PCU of Bus for every 5-min

Fig. 19. Comparison of heterogeneous traffic and cars-only traffic flows.

8. Conclusions

The following are the important findings of this study:

1. The validation results of the simulation model VISSIM, used for this study indicate that

the model is capable of replicating the heterogeneous traffic flow on expressways to a

satisfactory extent.

2. From the speed-volume curve developed using the simulation model, it is found that, for

the observed traffic composition, capacity of a eight-lane divided urban expressway in

level terrain with 14.0 m wide road space is about 9700 PCU/h for one direction of traffic

flow.

3. It is found that, the estimated PCU values of the different categories of vehicles are

accurate at 5 % level of significance.

4. It is found that, under heterogeneous traffic conditions, for a given roadway condition and

traffic composition, the PCU value of vehicles vary significantly with change in traffic

volume. Hence, it is desirable, to treat PCU as dynamic quantity instead of assigning

fixed PCU values for the different vehicle categories of road traffic.

25

5. Under heterogeneous traffic conditions, the trend of variation of the PCU value, over

traffic volume, indicates that: (i) the PCU value for all the vehicle category decreases as

V/C ratio increases for all the vehicle categories for the traffic flows in uncongested

regime till capacity, (ii) the PCU value for heavy and larger vehicle types (buses, trucks,

etc. as compared to cars) are found to be higher under congested regime as compared to

their PCU values near capacity, and (iii) the PCU value for smaller vehicle types

(motorised three-wheelers and motorised two-wheelers as compared to cars) are found to

be relatively same under congested regime as compared to their PCU values near

capacity.

6. It is inferred that the change in the PCU value of the different categories of vehicles, due

to change in traffic volume, under heterogeneous traffic condition, is directly influenced

by the change in the speed difference between the reference vehicle (car) and the subject

vehicle (a chosen vehicle type, other than car).

9. Limitations of the Study

The driver behavior, considered in this study can be refined further to consider many more

physiological and psychological factors.

10. Future Research Scope

This study can be further extended to study the following aspects:

1. The effect of vehicle composition on PCU values can be determined from the validated

model.

2. Area-Occupancy can be used as a parameter of surrogate measure for the density for

developing fundamental diagrams and level-of-service criteria.

3. Developing a concept of stochastic capacity estimates under heterogeneous traffic

conditions prevailing on expressways in India. Such estimates would account for vehicle

composition in the traffic stream.

4. The empirical relationship between lateral-clearance share data for different possible pairs

of vehicle categories based on the field observed data over a wider range of speeds may

refine the validation results further.

Acknowledgements

The authors (first three authors) would like to thank Central Road Research Institute (CRRI), New Delhi India

for extending their valuable support for collecting the traffic data used in this study. They would also like to

thank PTV AG, Germany for providing the software VISSIM used in this study.

References

1. Al-Kaisy, A., Jung, Y., and Rakha, H. (2005).Developing Passenger Car Equivalency Factors for

Heavy Vehicles during Congestion. Journal of Transportation Engineering, 131, 514-523.

2. Al-Kaisy, A.,Hall. F., and Reisman, E.(2002). Developing passenger car equivalents for heavy

vehicles on freeways during queue discharge flow. Transportation Research Part A, 36, 725-742.

3. Arasan, V. T., and Koshy, R. Z. (2003).Headway distribution of heterogeneous traffic on urban

arterials. Journal of Institution of Engineers (India), 84, 210–215.

26

4. Arasan, V.T., and Koshy, R. Z.(2005). Methodology for modeling highly heterogeneous traffic

flow. Journal of Transportation Engineering, 131, 544 – 551.

5. Arkatkar S. S. “Analysis of Heterogeneous Traffic Flow Using Microscopic Simulation

Technique” National Symposium on Innovations and Advances in civil Engineering, March 16-

17, 2012, GGCT, Jabalpur, India, 29-40.

6. Arkatkar, S. S., and Arasan V. T. (2010).Effect of Gradient and its Length on Performance of

Vehicles under Heterogeneous Traffic Conditions. Journal of Transportation Engineering, 136,

1120- 1136.

7. Bains M S, Ponnu B, Arkatkar S S (2012). Modeling of Traffic Flow on Indian Expressways

using Simulation Technique. Procedia - Social and Behavioral Sciences, 43, 475 – 493

8. Bang, K.L., Nissan, A., Koutsopoulos, H.N. (2011), Country Reports, the 6th International

Symposium on Highway Capacity and Quality of Service, Stockholm, Sweden.

9. Basu, D., Maitra, S.R., and B. Maitra (2006).Modeling passenger car equivalency at an urban

mid-block using stream speed as measure of equivalence. Journal of European Transport, 34, 75-

87.

10. Chandra, S.(2004). Capacity estimation procedure for two lane roads under mixed traffic

conditions. Journal of Indian Road Congress, 165, 139-170.

11. Chandra, S., and Kumar, U. (2003). Effect of lane width on capacity under mixed traffic

conditions in India. Journal of Transportation Engineering, 129, 155-160.

12. Chandra, S., P.K. Sikdar, and V. Kumar (1995) Dynamic PCU estimation of capacity on urban

roads.Indian Highways, 23, 17– 28.

13. Craus, J., Polus,A.,andGrinberg, I. A.(1980).Revised method for the determination of passenger

car equivalencies. Transportation Research Part A, 14, 241-246.

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

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

15. Directorate General of Highways (DGH) - Ministry of Public Works (1995),”Indonesian highway

capacity manual- Part-II, Interurban roads”, Report No: 05/T/Bt/1995.

16. Fellendorf, M., and Vortisch, P. (2001).Validation of the microscopic traffic flow model VISSIM

in different real-world situations.Proceedings of 80th Annual Meeting of the Transportation

Research Board, Washington D.C.

17. Hossain, M.J. (2004). Calibration of the microscopic traffic flow simulation model VISSIM for

urban conditions in Dhaka city.Master thesis, University of Karlsruhe, Germany.

18. Huang, A., and Tang, M. (2007). AC transit easy bay bus rapid transit project-Alameda and

Contracosta Counties, California.

(www.oregonite.org/2007D6/paper_review/C8_36_Huang_Paper.pdf) accessed on 13 May, 2013

19. Huber, M. J. (1982). Estimation of passenger car equivalents of trucks in traffic stream.

Transportation Research Record, 869, 60-70.

20. Ishaque, M. and Noland, R. (2009). "Pedestrian and Vehicle Flow Calibration in Multimodal

Traffic Micro-simulation." J. Transp. Eng., 135(6), 338-348.

21. Keller, E., and Saklas, J. (1984).Passenger car equivalents from network simulation. Journal of

Transportation Engineering, ASCE, 110, 397-411.

22. Krammes, R. A., and Crowley,K. W. (1986). Passenger car equivalents for trucks on level

freeway segments. Transportation Research Record, 1091, 10-17.

23. Linzer, E., Roess,R., and McShane. W. (1979).Effect of trucks, buses, and recreational vehicles

on freeway capacity and service volume. Transportation Research Record, 699, 17-24.

24. Lownes, N. E., and Machemehl, R. B. (2006) “Vissim: A multiparameter sensitivity analysis.”

Proc., 38th Winter Simulation Conf. ,L.F. Perrone, B. G. Lawson, J. Liu, and F. P. Wieland, eds.,

1406–1413.

25. Mathew, T. and Radhakrishnan, P. (2010). "Calibration of Microsimulation Models for Nonlane-

Based Heterogeneous Traffic at Signalized Intersections." J. Urban Plann. Dev. 136, SPECIAL

ISSUE: Challenges in Transportation Planning for Asian Cities, 59-66.

26. Matsuhashi, N., Hyodo, T., Takahashi, Y. (2005). Image processing analysis on motorcycle

oriented mixed traffic flow in Vietnam. Proceedings of Eastern Asia society for transportation

studies (EAST), Tokyo, 929–944

27

27. Ministry of Road Transport& Highways (India) (2010). Guidelines for Expressways, Part I & II,

Indian Roads Congress, New Delhi, I73-I87.

28. PTV.(2011) VISSIM Version 5.40-01 user manual, Planung Transport Verkehr AG, Karlsruhe,

Germany.

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

using a modified density method. Proceedings of 4th Internationa Symposium on Highway

Capacity, Maui, Hawaii, 246-257.

30. TRB (2000).Highway Capacity Manual, National Research Council, Transportation Research

Board, Washington, D.C.

31. V. Thamizh Arasan and G. Dhivya.(2010). Methodology for Determination of Concentration of

Heterogeneous Traffic.” Journal of Transportation Systems Engineering and Information

technology, Elsevier, 10(4), 50-61.

32. Van Aerde, M., and Yagar, S. (1984).Capacity, speed, and platooning vehicle equivalents for two-

lane rural highways. Transportation Research Record, 911, 58-67.

33. Velmurugan, S., Errampalli, M. and Reddy, T. S. (2002). "Changing operating speeds on rural

highways." Proceedings of Emerging Trends in Road Transport (RORTRAN), Kharagpur, India,

5.29-5.40.

34. Velmurugan, S., Errampalli, M., Ravinder, K. and Gangopadhyay, S. (2009). Updation of road

user cost for economic evaluation of road projects. Indian Journal of Traffic Management, 205-

225.

35. Velmurugan, S., Errampalli, M., Ravinder, K., Sitaramanjaneyulu, K., and Gangopadhyay, S.

(2010). Critical evaluation of roadway capacity of multi-lane high speed corridors under

heterogeneous traffic conditions through traditional and microscopic simulation models. Journal

of Indian Roads Congress, 235-264.

36. Webster, N., and L. Elefteriadou.(1999). A simulation study of truck passenger car equivalents

(PCE) on basic freeway sections. Transportation Research Part B, 33, 323-336.

37. Werner, A., and J. Morrall.(1976). Passenger car equivalencies of trucks, buses, and recreational

vehicles for two-lane rural highways. Transportation Research Record, 615, 10-17.

38. Zhang, G., Wang, Y., Wei, H., Yi, P., (2008). A feedback based dynamic tolling algorithm for

high-occupancy toll lane operations. Transportation Research Record, 2065, 54-63.