Submission 551 Camera Ready

3rd Conference of Transportation Research Group of India (3rd CTRG)

Saturation Flow of Heterogeneous Traffic

At Signalized Intersections

Shubham Sehgala,*, Neelam J. Guptab,*, Subhash Chandc, S. Velumurugand

aShubham Sehgal, PEC University of Technology, Chandigarh, 160012, India, [email protected] bNeelam J. Gupta, Senior Scientist, CSIR-CRRI, New Delhi, 110025, India, [email protected]

cSubhash Chand, Principal Scientist, CSIR-CRRI, New Delhi, 110025, India, [email protected] dS. Velumurugan, Head & Senior Principal Scientist, CSIR-CRRI, New Delhi, 110025, India, Email ID

* Corresponding Author

ABSTRACT. Saturation flow rate is the maximum rate of flow of traffic during green phase at a

signalized intersection and this is an important parameter for the assessment of performance evaluation of

a signalized intersection. Eventually, it is used extensively in the design of signals and their performance

evaluation. Saturation flow is expressed as the number of passenger car units (PCU) of traffic flow per

hour in a dense flow conditions during saturated green intervals for a specific lane or lane-group or

approach of the intersection. Thus the signal design, capacity and performance of a signalized intersection

critically depend on and Passenger Car Unit (PCU) assigned to different vehicle types and thus this

phenomenon assumes more significance under heterogeneous traffic conditions as the traffic composition

plays a pivot role in the traffic discharge at a signalized intersection. In India the traffic is composed of

highly heterogeneous mix of vehicles and drivers don’t follow traffic rules and lane discipline particularly at the signalized intersections. It is noted in the literature that the traditional methods like the estimation

of average value of observed queue discharge and headways to estimate the saturation flow rate which are

mainly employed under homogeneous traffic conditions headway might lead to underestimation of the

saturation flow rate under the prevailing conditions of heterogeneous traffic on Indian roads. In this paper,

efforts have been made to develop appropriate methodology to calculate the PCU and saturation flow rate

accounting for heterogeneous traffic conditions prevalent in India. In the conclusion we find that the

saturation flow is directly dependent upon the traffic composition and the present guidelines by IRC lead

to either overestimation or underestimation of traffic. Keywords: Signalized Intersection, Passenger Car Unit and Saturation Flow

Preamble

India is a developing country and its cities are undergoing rapid urbanization and in this

regard, the commensurate development of road network plays an important role in the

growth. India has large network of roads amounting to 4.8 million Kilometers

connecting length and breadth of the country through more than 42 Class I Cities

(Census-2011). Increasing urbanization is leading to the increase in the road traffic on

major metropolitan cities warranting the need for the creation of signalized

intersections. Adopting a model from an already developed country is not a feasible

solution to address the traffic heterogeneity as seldom lane based traffic is followed at

the intersections on Indian roads.

Literature Review

The traffic stream in developed countries mainly consists of cars and heavy vehicles

comprising of smaller proportion of buses and goods vehicles, which may be up to 20

%. On the other hand, in a developing country like India, road traffic in general and

urban roads traffic in particular is highly heterogeneous comprising of vehicles of

widely varying static and dynamic characteristics and the vehicles share the same road

space without any physical segregation. Expressing traffic volume as number of


Sehgal, J. Gupta, Chand and Velumurugan

vehicles passing a given section of road per unit time will be inappropriate when the

vehicle types with widely varying static and dynamic characteristics are present in the

Indian road traffic. Passenger Car Units (PCUs) values are used to convert a traffic

stream composed of different vehicle types into an equivalent traffic stream with

standard passenger cars taken as the reference vehicle. Under homogeneous traffic

conditions the volume or capacity can be easily expressed in terms of PCU per hour per

lane. Since the pattern of occupancy of road space by vehicles under heterogeneous

traffic condition differs significantly from that of homogeneous traffic, the volume of

traffic has to be expressed taking the whole width of roadway as the basis, which entails

a little bit more complexity. Moreover, in India the vehicles tend to negotiate through

the lateral gap available between the vehicles so as to reach the head of the queue at the

signalized intersections.

Studies on Passenger Car Units (PCU) for the signalized intersections in India are

primarily carried out referring to IRC SP 41 (1994). This is because this is the only

authentic Indian guideline developed about two decades back for the design of at-grade

signalized intersections in rural and urban areas. The above code suggests PCU values

for different modes of traffic and their usages for design of grade intersections. The

studies done elsewhere reveals that the impacts of different light-duty trucks (LDTs) on

the capacity of signalized intersections were analyzed [1]. The Neural Network (NN)

approach for capturing the non-linear effects of traffic volume and its composition level

on the stream speed was explored [2]. An empirical study to determine the PCU of

different types of vehicles that reflected the actual traffic conditions of Dhaka

Metropolitan City was carried out [3]. The principal methods of measurement of

saturation flow and the selection of a proper method to measure saturation for the traffic

condition prevailing in developing countries was reviewed [4]. The flow rate method to

estimate the PCU values of motorized two wheelers was accomplished with the help of

a recently developed agent-based simulation model. The simulation of the movement

characteristics, which was capable of representing the pattern of movement of

motorized two wheelers, was also accomplished [5]. The existing basic methods for

saturation flow estimation were reviewed and their applicability for Indian traffic

streams was summarized [6]. The determination of PCU factor at mid-block section was

dealt using space headway method under Indian context [7]. There would be a need for

dynamic PCU instead of static PCU recommended by Indian Road Congress, IRC: SP-

41. Therefore based on the various methods reviewed, it would be prudent to deploy

dynamic PCU method. The saturation flow rate is a fundamental parameter to measure

the intersection capacity and time the traffic signals. Traditional saturation flow rate

estimation methods were mostly developed based on the assumption that the queue

discharge headway is a fairly constant and that the average headway estimated from the

first 4-to-10 or 4-to-12 vehicles is representative of the saturation headway meaning

every vehicle in a stable moving platoon consumes seconds [8,9]. However, variability

in queue discharge headways is addressed in more recent studies [10-19] and it is found

that traditional methods, which simply use the average of discharge headway to estimate

the saturation headway might lead to underestimate saturation flow rate. Errors in

saturation flow rates used to estimate vehicle delays carry over onto delay predictions

and level of service (LOS) predictions. Therefore, it is necessary to study and to

improve accuracy of the estimation of saturation flow rate.

Keeping in mind the above reviewed literature and the prevailing traffic conditions on

the Indian roads, a different approach has to be adopted. In this regard, it was felt to



prudent to calculate the PCUs based on the field data as the foremost step. In this paper,

efforts have been made to calculate the dynamic PCU of different vehicle types based

on its clearance time and the area occupied by the individual vehicle assuming that

performance of signalized intersection depend on space and time occupancy of the

vehicles in the traffic stream at the signalized intersection area. The traffic flow at

signalized intersection was captured through Videography. The clearance time data was

decoded from video data and area occupied by different vehicles in the stream was

calculated from their physical dimensions. Based on this, a unique methodology was

developed to calculate the dynamic PCU and saturation flow for heterogeneous traffic

conditions in India.

Study Area

Ten approaches of three 4-arm signalized intersections were selected for the present

study having different approach widths ranging from 6.4m to 11.5m. Intersections

selected for this study are right angle intersections and have level gradient on all

approaches and least interference to entry or exit traffic due to pedestrians, bus stops,

parked vehicles, etc. (figure 1). All the approaches of the intersections reach saturated

stage for major part of the green interval at almost each phase during peak hour, as

traffic flow is very heavy. The traffic does not follow lane discipline and consists of

more than 12 different types of vehicles varying in speed and sizes. The candidate-

signalized intersections considered include:

1- Stadium Chowk, Noida,

2- NTPC Chowk, Noida

3- DTC Depot Chowk, Dwarka, New Delhi.

Stadium Chowk, Noida NTPC Chowk, Noida

DTC Depot Chowk, Dwarka

Figure 1: View of Study Intersection.



Study Methodology

Literature review reveals the ideal base conditions for capacity analysis for signalized

intersections would be uniform traffic and lane driving. US-Highway Capacity Manual

(US-HCM) provides a basis for the capacity analysis based on saturation flow which is

further based on headway measurement at the stop line during saturated flow conditions

for these ideal conditions [9]. Later on different adjustment factors were applied for

different influencing factors. But such an approach is not feasible for the Indian road

and traffic conditions. Most of the solution offered by the Traffic Engineers worldwide

has very less adaptability for the Indian traffic, so we need a completely new outlook to

this major problem.

The solution proposed in this paper is to measure the saturation flow in the field by

measuring the classified traffic flow at the stop line during the green phase at 5 second

interval. Also various factors like width of road, composition of traffic, cycle length,

green phase etc. are also measured so that their impact on the saturation flow is studied.

In order to address the variability in flow due to heterogeneous traffic initial PCU

values are assigned and dynamic PCU value is calculated. Unsaturated intervals of 5-

second flow were neglected from the database based on flow of previous saturated 5-

second interval. The systematic flow chart of methodology of this research work is

depicted in Figure 2.

Figure 2: Flow Chart of Study Methodology.



PCUi = Passenger Car Unit of vehicle type i

Ai = Area of ith vehicle

Ac = Area of passenger car

Vi = Average clearing speed of vehicle type i in m/s

Vc = Average clearing speed of car in m/s

tc = Average clearing time of car in sec

ti = Average clearing time of vehicle type i in sec.

Xn = Nth 5-second interval

First step in this direction is the collection of data by Videography method. Following

guidelines were followed while selecting the candidate intersections for data collection.

No bus stop in the approach length up to 100 m.

No parking in the approach, upstream or downstream up to 100 m.

No separate phase for the pedestrians.

Should be well-channelized intersection preferably with exclusive / free left

turning provision.

Roads should cross at 90 degree.

Flat Gradient

Care to be exercised to record the traffic data for a minimum of 2 to 4 hours

during peak period from a suitable height with an aim to cover the entire

intersection. More than one camera may be used if suitable vantage point is not

available.

Traffic data collected at the candidate intersection would encompass the various

parameters like field traffic flow patterns (i.e. traffic volume) of different turning

movements, traffic composition, and speed / clearance time of different vehicles at each

phase of the signal at different approaches of the signalized intersections. In this regard,

enumeration of the turning movement data was accomplished by deploying a portable

digital video camera mounted at a height of 6 m at the opposite island or median or at a

vantage point at the corner of the intersection so as to cover the designated approach.

The camera was so positioned so as capture view of the approach road from its

corresponding exit line i.e. line joining ends of channelizing islands of both the Straight

Traffic (SH) and Right Turning (RT) movements up to about 10 m inside the Stop Line

on the subject approach as shown in Figure 2. Traffic flow were recorded using the

above video camera setup on a typical working day covering the morning and evening

period from 8:00 am to 12:00 noon and from 2:00 pm to 6:00 pm respectively. The

recorded traffic data was retrieved in the laboratory for each turning movement during

each green phase of approach categorized into six vehicle types namely, Small car (up

to 1400), Big Car (beyond 1400 cc), motorized Two-Wheeler, Three-Wheeler, Light

Commercial Vehicle (LCV) and Heavy Commercial Vehicle (HCVs – included Buses

and all normal Goods Vehicles).

In addition, the signal timing for each phase of each approach was noted manually for

all the intersections. Roadway condition and operational data for different approaches of

all the selected intersections are given in Table 1.



Table 1: Roadway condition and operational data.

Intersection Traffic Approach

From

Width

( m )

Cycl

e

time

( s )

Green

time

Ambe

r Time

Red

Time

Stadium

Chowk

( Noida )

Spice mall ( NB ) 9.0 127 30 3 94

Noida Mor (SB ) 6.4 127 25 3 99

DND ( EB ) 10.3 127 30 3 94

Chora Mor ( WB ) 9.8 127 30 3 94

NTPC Chowk

( Noida )

GIP ( EB ) 11.0 187 60 3 124

Ghaziabad ( WB ) 11.0 187 60 3 124

DTC Depot

Chowk,

Dwarka

( New Delhi )

Dabri Ext. (WB) 11.5 140 30 4 106

Dwarka sec 10 (EB) 10.5 140 30 4 106

Madhu Vihar (SB) 10.7 140 45 4 91

Palam Vihar (NB) 10.0 140 30 4 106

Data Analysis

Data analysis of various traffic characteristics such as traffic volume, traffic

composition, peak hour traffic volume, peak hour factor and traffic composition etc.

was done for each turning movement, each approach and intersection as a whole for the

candidate intersection. There were no specific lanes marked for Right Turning (RT) or

Straight Through (ST) vehicles. The recorded films were replayed in the laboratory on a

large screen in order to retrieve the required data information for the study.

Vehicle count was made at every five second interval during each green time in each

signal cycle for each individual approaches of the intersection. Also clearance time was

recorded at every five second interval in each green time on sample basis during each

signal cycle at all the approaches covering different vehicle types. The clearance time

was recorded as the time difference when the front bumper of the vehicle entered the

intersection and the rear bumper of the vehicle left the intersection. Based on the sample

data, the average clearance time for each vehicle types in each cycle was determined.

Passenger Car Unit (PCUs) recommended by IRC:SP-41 as shown in Table 2 were used

to convert the traffic flow into PCUs.

Table 2: PCU as per IRC: SP-41.

Vehicle Type Small Car Big Car 2W 3W LCV HCV

PCU 1.0 1.0 0.5 1.0 1.5 4.5

The areas of different vehicle types were considered on an average scale by accounting

for the range of vehicle brands cutting across varying vehicle sizes under each vehicle

type. Table 3 presents a summary of the areas of different vehicle types deduced

deploying the above analogy.



Table 3 Vehicle Dimensions of Different Vehicle Types.

S. No. Y

Vehicle Category Average Dimension (m) Projected Rectangular

Area (m²)

Physical

Rectangular Area

Ratio, Ai/Ac Length (m) Width (m)

1. Small Car 3.72 1.44 5.36 1.00

2. Big Car 4.58 1.77 8.11 1.33

3. Two Wheeler 1.87 0.64 1.20 0.21

4. Two Axle / Multi

Axle Truck 7.5 2.35 17.63

3.06

5. Bus 10.1 2.43 24.54 3.06

6. Three Wheeler 3.2 1.4 4.48 0.78

7. LCV 6.1 2.1 12.81 1.39

An important part of the study is to calculate dynamic PCU for each signal cycle. It is

calculated by taking time occupancy of Car to Vehicle to Space ratio of Car to Vehicle

at the intersection shown in following formula [20]. Here standard car is taken as

reference vehicle.

….Equation.1

These dynamic PCU values are used to get the total flow rate of vehicles as follows:

Xn = PCUi * Zi ….Equation.2

Where, Zi is vehicle count

The saturation period has been evaluated by minimizing the following absolute standard

error:

-2=<Xn-X2<=2

Here Xn represents the nth 5-second interval and X2 represents the second interval i.e. the

interval between 5 seconds to 10 seconds and on till the end of the phase time. The

above interval class and error minimization technique was selected to minimize the

error, which is likely to accrue due to the following scenarios commonly noted on the

Indian roads:

Non - adherence to the lane based movement on the Indian roads.

The lead vehicles in the queue seldom follow the practice of stopping behind the

zebra crossing.

Due to the tendency exhibited by the two wheeler riders to squeeze through the

available space by driving their vehicles in between the lanes of the queue and

thereby become the leaders of the pack in the queue.

Vehicles invariably tend to start the crossing maneuver at the intersection when

there are still some seconds left at the red interval.

By considering the successive 5-second interval of the green time, the intervals

wherein the saturation had occurred would be duly accounted for. For those 5-

second time intervals for which the saturation flow had actually taken place, the

maximum flow limit, which is equal to the flow as per dynamic PCU, can be

deduced. Thus the Saturation flow for this saturation period was thus calculated.



During this analysis, it is assumed that the clearance time as the average clearance time

for all vehicle types in that cycle. Later, this assumption was validated and it was found

this holds fairly true when the clearance time for each 5 second interval was determined.

This was validated by considering another candidate intersection namely the Choda Mor

approach of the Stadium Chowk, Noida and the results were deduced by deploying both

methods i.e. one average value for whole cycle and the second being average value for

each 5 second interval (Table 4). It can be noted from Table 4 that there is negligible

difference between the saturation flow values determined using both the methods.

Therefore, the hypothesis of assuming a single average value of the clearance time for a

cycle holds true.

Table 4: Comparison of Clearance Time for 5 second interval and

Average of Whole Cycle.

Method Saturation Flow per hour (as per Dynamic PCU)

Clearing time of every 5

second interval

5499

Average clearing time for

whole cycle

5571

Thus, it can be stated one can solve all the calculations by using the average clearance

time for a particular class of vehicle for the whole cycle length.

Classified average saturation flow for combined through (TH) and Right Turning (RT)

movements of different approaches of the selected intersections were converted into

Passenger car unit (PCU) by multiplying the respective PCU factors estimated in this

study with the number of vehicles of the category in order to derive average saturation

flow in PCU per hour green. Same procedure was used to calculate saturation flow in

PCU per hour green by using PCU values from IRC SP-41 also for comparison purpose.

The data size consists of 20 cycles for any arm of the intersection during the peak hour

duration. Saturation intervals and the saturation flows for those time interval were the

average of all the saturation flow for a particular arm to get the average saturation flow

for that arm. The data was analyzed by applying dynamic PCU values based on average

clearance time of the cycle then minimizing the error and calculate saturation flow

period and saturation flow in those for each approach of each intersection.

In order to optimize the long range of saturation flow values for its average, the values

of Saturation flow observed in each 5 sec time period which lie within the range of,

[0.9*(SIRC)] <= SCAL <= [1.25(SIRC)]

That is from 90% to 125% values of SIRC are considered and their average is considered

as the saturation flow for that approach.

The data for all the three intersection has been compiled and presented in Table 5, 6 and

7 for different approaches.



Table 5: Saturation Flow at different approaches of Stadium Chowk,

Noida.

*We have considered lane width equal to 3.6 m for the calculation of Saturation flow/Hr/Lane

It is observed that at the Choda Mor approach of the Stadium Chowk intersection there

is high percentage of heavy vehicle i.e. nearly 13.27%, thus saturation flow per dynamic

PCU/Hr is higher than flow as per IRC guidelines. Similarly at DND approach of the

intersection saturation flow in dynamic PCU/Hr is higher than that as per IRC due to

presence of heavy vehicles. At the Noida Mor leg of the intersection there is

considerable amount of heavy traffic leading to higher saturation flow in dynamic PCU

than that as per IRC guidelines by considerable amount. In the Spice Mall leg of the

intersection nearly 21% of the traffic is due to big car. Though a higher percentage of

two wheelers is present but big car play a dominating role in saturation flow calculation.

It is found that the PCU value of two wheelers from Dynamic method is far less than its

corresponding IRC PCU value and also for big car Dynamic PCU has value higher than

IRC guidelines.

It is found that higher percentage of two wheeler reduce the per lane saturation flow in

Dynamic PCU for an approach while higher percentage of heavy vehicle increase the

saturation flow per lane. In fact Dynamic PCU flow rate is directly related to the

composition of the traffic at the approach during a green signal phase.

Table 6: Saturation Flow at different approaches of NTPC Chowk, Noida.

Approach

Name

Approach

Width

Green

Time

(In

second

)

Degree

of

Saturat

ion (in

%)

Small

Car

%

Big

Car

%

Two

Whee

ler %

Three

Wheeler

%

LCV

%

HCV

%

Saturat

ion

Flow/H

r (as

per

IRC)

Saturat

ion

Flow/H

r (as

per

Dynami

c PCU)

Saturation

Flow/Hr/

lane*

Ghaziabad 11.0

60 63.2 55.28 13.25 20.70 9.32 0.83 0.62 6190 5970 1954

GIP

11.0 60 65.41 59.58 9.84 20.52 7.23 1.26 1.47 6235 6271 2052

*we have considered lane width equal to 3.6 m for the calculation of Saturation flow/Hr/Lane

Approach

Name

Approach

Width

Gree

n

Time

(In

secon

d)

Degre

e of

Satura

tion(in

%)

Small

Car %

Big

Car %

Two

Whee

ler %

Three

Wheele

r %

LCV

%

HCV

%

Saturati

on

Flow/Hr

(as per

IRC)

Saturat

ion

Flow/H

r (as

per

Dynami

c PCU)

Saturatio

n

Flow/Hr/

lane(as

per

Dynamic

PCU)*

Choda

Mor 9.8 30 76.31 50.24 7.11 24.17 5.21 0

13.27 4840 5571 2046

DND

10.3

30 70.2 43.84 4.45 35.27 4.45 0.68 11.3 4986 5928 2072

Noida Mor 6.4

25 75 25.58 22.09 36.05 4.65 0 11.63 3156 3509 1974

Spice Mall 9.0

30 55.55 26.67 21.11 44.44 6.67 0 1.11 4293 4987 1995



At NTPC intersection it is observed that there is no major difference in the saturation

flow rates by both the methods for the Ghaziabad approach. The major composition of

the traffic is due to small car (More than 50%) whose PCU value have been assumed to

be one in Dynamic PCU method and by the IRC guidelines. Also small percentage of

heavy vehicles is observed at this approach. In the GIP approach of the intersection

there is not much of difference due to dynamic and IRC PCU. It may be due to presence

of high percentage of small cars whose PCU value is one in both the cases. Also two

wheelers and HCV are present in noticeable amount.

Table 7: Saturation Flow at different approaches of DTC Depot Chowk, Dwarka,

New Delhi.

*we have considered lane width equal to 3.6 m for the calculation of Saturation flow/Hr/Lane

From the above table we may conclude that at the Palam Vihar approach, the observed

saturation flow by dynamic PCU is less than that by IRC guidelines. This is due to

presence of nearly 42% of two wheelers in the traffic. Similarly in the Madhu Vihar leg

of the intersection, the observed saturation flow in dynamic PCU is less than the one

calculated by IRC guidelines even though there is presence of heavy traffic, because

nearly 54%of the traffic is composed of two wheelers. Due to this large percentage of

two wheelers, it plays a dominating role in changing the saturation flow. At Dwarka

Sector-10 approach the calculated value for saturation flow is slightly higher due to

presence of 64,4% small car in the traffic volume. We get almost comparable saturation

flow values from both the methods. For Dabri Extension we observe higher saturation

flow than that by IRC due to presence of nearly 19% of big car in the traffic

composition. Having a higher value than small car this brings bout change in the

saturation flow.

Table 8: Dynamic PCU values.

Class of

Vehicle

Small

Car

Big Car Two

Wheeler

Three

Wheeler

LCV HCV

Dynamic

PCU range

1 1.10 -1.60 0.13 – 0.26 0.62 – 0.95 1.5 – 2.20 3 – 4.9

Approach

Name

Appr

oach

Widt

h

Green

Time

(In

second

s)

Degree

of

Satura

tion (in

%)

Small

Car %

Big

Car

%

Two

Wheele

r %

Three

Wheele

r %

LCV

%

HCV

%

Saturation

Flow/Hr

(as per

IRC)

Saturatio

n

Flow/Hr

(as per

Dynamic

PCU)

Saturatio

n

Flow/Hr/

lane*

Palam Vihar 10.0

30 62.7 34.35 8.7 42.17 7.83 3.04 3.91 6503 5723 2060

Madhu Vihar 10.7

45 74 20.21

3.28 54.75 7.25 1.90 5.01 7206 6194 2083

Dwarka

Sector-10 10.7

30 64.4 68.03 8.84 10.88 10.20 0.68 1.36 5410 5450 1869

Dabri Ext. 11.5

30 75 55.045

3.03

18.94 18.18 4.55 03.03 2.27 5820 6374 1995



Conclusion and Recommendations The study clearly emphasize the need for the estimation of PCU values based on actual

field studies at the signalised intersections as it is found to vary considerably as

compared to PCU values provided in IRC:SP-41in most of the cases. The estimated

PCU values provided both higher and lower values of saturation flow from the ones we

have from IRC-PCU values. In most of the cases the variation in saturation flow can be

explained by estimating PCU by clearance time, but in some cases it is not able to

justify the variation of saturation flow during different saturated green phases of the

same approach which may attribute due to its sensitivity of varying composition of

traffic during the different green phases of signal. It is found that higher percentage of

two wheeler reduce the per lane saturation flow in Dynamic PCU for an approach while

higher percentage of heavy vehicle increase the saturation flow per lane. In fact

Dynamic PCU flow rate is directly related to the composition of the traffic at the

approach during a green signal phase. Also presence of big car also influences the

saturation flow. Refer to table 8 for dynamic PCU values.

It affirms that PCU values at signalised intersections are highly dynamic and further

emphasises the need for the estimation of PCU values based on different comprehensive

approach. Further studies based on comprehensive data is needed to establish reliable

model for general application, especially for varying geometric, traffic and

environmental conditions. This study can be used as a baseline for further research work

on analysis of traffic flow at signalized intersection in Delhi as well as in other cities of

India to further develop and update the derived relationship between saturation flow

and approach width, percentage of different classes of vehicles etc. Studying the affect

of approach width on the saturation flow is beyond the scope of this paper. Saturation

flow based on entire width is expected to provide better understanding of relationship,

particularly in India where traffic highely hetrogeneous and there is no lane discipline.

More intersections located in different parts of the city with different approach widths

and varying traffic characteristics, be studied for further refinement and updation of

present models.

Acknowledgement The authors are grateful to Director CSIR CRRI to accord his permission to publish this

paper. The authors are grateful to CSIR for sponsoring the main study titled,

“Development of Indian Highway Capacity Manual (Indo-HCM)” as the outputs of this

research would be directly applicable for the main study.

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