ESTIMATION OF SATURATION FLOW AT SIGNAL CONTROLLED INTERSECTIONS APOORV JAIN

ESTIMATION OF SATURATION FLOW AT SIGNAL CONTROLLED INTERSECTIONS A DISSERTATION submitted in partial fulfillment of the requirements for the award of the degree of MASTER OF TECHNOLOGY in CIVIL ENGINEERING (With Specialization in Transportation Engineering) By APOORV JAIN DEPARTMENT OF CIVIL ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY ROORKEE ROORKEE - 247 667 (INDIA) MAY 2016

CANDIDATE S DECLARATION I hereby declare that the work which is being presented in this dissertation titled, ESTIMATION OF SATURATION FLOW AT SIGNAL CONTROLLED INTERSECTIONS, in partial fulfilment of the requirements for the award of degree of Master of Technology in Civil Engineering, with specialization in Transportation Engineering, submitted in the Department, IIT Roorkee, India, is an authentic record of my work carried out during the period from June, 2015 to May, 2016, under the guidance of Dr. Indrajit Ghosh, Asst. Professor, Department of Civil Engineering, IIT Roorkee and Dr. Satish Chandra, Director, CSIR-CRRI, New Delhi. The matter embodied in this dissertation has not been submitted by me for the award of any other degree. Date: May, 2016 APOORV JAIN Place: Roorkee. (14524003) This is to certify that the above statement made by the candidate is correct to the best of my knowledge. ` Dr. Indrajit Ghosh Asst. Professor, Department of Civil Engineering IIT Roorkee, Dr. Satish Chandra Director Central Road Research Institute Delhi Mathura Road, CRRI (P.O.) Roorkee 247667. New Delhi - 110025

ACKNOWLEDGEMENTS I have a great pleasure in expressing my deep gratitude to my guides, Dr. Indrajit Ghosh, Asst. Professor, Transportation Engineering group, Civil Engineering Department, Indian Institute of Technology Roorkee and Dr. Satish Chandra, Director, CSIR-CRRI, New Delhi for their esteemed guidance, invaluable suggestions and generous help throughout the course of this work. I sincerely thank to all teachers and faculty staff for their suggestions and assistance provided during the work. I am also deeply grateful to our research scholars Arpita Saha and Sabyasachi Biswas who lent me a helping hand from time to time. Lastly but not the least, my heartiest gratitude to my parents for their faith and support, which has been a constant source of inspiration. The humble thanks are for all those who in any manner lent helping hand in every bit of this research. Date: May, 2016 Place: IIT Roorkee. APOORV JAIN

ABSTRACT The general term intersection describes all roadway situations where two or more roads join or cross one another at the same or different elevation. In developing countries like India crossing of two roads at same level is a common feature because later is very expensive. Design of an at-grade intersection under mixed traffic flow conditions to handle the crossing traffic at satisfactory level of services is the biggest problem encountered by traffic engineers. It is generally observed that congestion at intersection occurs at volume level well below the capacity. Saturation flow and green time ratio are the two factors on which the capacity of the signal controlled intersection depends. Saturation flow will be affected by the several geometric, operating, traffic, environmental and other kind of parameters. For the design of efficient and safe intersection to serve the needs of the existing and future coming traffic it is necessary to have an idea on all kind of affecting parameters of saturation flow. In this Dissertation topic attempt has been made to study the concept of saturation flow at signal control intersections and the influence of approach width, traffic composition and turning movement on saturation flow. Data collected at 12 signal controlled intersections, were analysed to estimate the saturation flows for different approaches and to investigate the effect of approach width, turning movement and heavy vehicles proportion on the saturation flow. The vehicles are categorized in to five categories. It is observed that Saturation flow increases with the increase in the proportion of 2- wheelers. Relationships have been established between proportions of vehicle categories and saturation flow. Influence of proportion of heavy vehicles on saturation flow for through movements is examined and presented graphically. Saturation flow of an approach decreases with the proportion of heavy vehicles in the traffic flow which is discharging from that approach. Two more relations have been developed between saturation flow rate and approach width and in between saturation flow rate and RT proportion. It has been observed that with increase in approach width and with decrease in RT proportion, SF increases.

TABLE OF CONTENTS Page No Candidate s Declaration Acknowledgement Abstract Table of Contents List of Figures List of Tables i ii iii iv-vi vii viii 1.0 INTRODUCTION 1-12 1.1 General 1 1.2 Signal controlled Intersection 2 1.3 Saturation flow 3 1.4 Factors affecting saturation flow 4 1.4.1 Geometric Factors 6 1.4.2 Operating Factors 7 1.4.3 Traffic Characteristics 8 1.4.4 Environmental, climatic and other factors 10 1.5 Problem Statement 11 1.4 Objectives 12 1.5 Outline of the report 12 2.0 LITERATURE REVIEW 13-25 2.1 General 13

2.2 Geometric Factors 13 2.2.1 Effect of Approach width 13 2.2.2 Number of lanes 14 2.2.3 Gradient 15 2.2.4 Turning Radius 16 2.3 Operating Conditions 16 2.3.1 Peaking Characteristics 16 2.3.2 Parking Characteristics 17 2.3.3 Bus Operations 17 2.3.4 Signal timing and phasing system 17 2.4 Traffic parameters 19 2.4.1 Traffic composition 19 2.4.2 Effect of turning vehicles 19 2.4.3 Traffic pressure 21 2.5 Environment and other factors 22 2.5.1 Weather 22 2.5.2 Area type 23 2.5.3 Area population 24 3.0 FIELD STUDY AND METHODOLOGY 26-36 3.1 General 26 3.2 Data Collection 26 3.3 Layouts of the intersections 27 3.4 Data collection details 33

3.5 Data extraction 33 3.5 Estimation of Saturate d green time 34 3.6 Estimation of Saturation flow 35 4.0 RESULTS and DISCUSSIONS 37-47 4.1 General 37 4.2 Estimation of Saturation Flow 37 4.3 Developing the equation of SF 38 4.4 Results of the Regression Analysis 39 4.5 Effect of approach width on saturation flow 41 4.6 Effect of turning movement on saturation flow 42 4.7 Combined equation taking all parameters 45 4.8 Validation of results 46 5.0 CONCLUSIONS AND RECOMMENDATIONS 48-49 5.1 Conclusions 48 5.2 Recommendations 49 REFERENCES 50-52

LIST OF FIGURES Fig. No. Caption Page No. 1.1 Signalized intersection 3 1.2 An idealized model of saturation flow 4 2.1 Effect of traffic pressure on saturation flow 22 2.2 Relationship between area population and saturation flow 24 3.1 Layout of intersection 1. 27 3.2 Location of intersection 2 & 3. 28 3.3 Location of intersection 4. 29 3.4 Intersection on Patiala urban state bypass 30 3.5 Intersection at sector 46, Chandigarh 31 3.6 Intersection at Dwarka sector 21, Delhi 31 3.7 Manimajhra railway crossing intersection, Chandigarh 32 3.8 Sector 45 C, Chandigarh 32 4.1 Variation of SF with P1 40 4.2 Variation of SF with P3 41 4.3 Relationship between SF and approach width 42 4.4 Variation of Saturation Flow with RT proportion 43 4.5 Variation of SF with P1RT 44 4.6 Variation of SF with P3RT 44 4.7 Comparison of two saturation flow rates 47

LIST OF TABLES Table.No. Caption Page No. 1.1 List of principle factors affecting saturation flow 5 1.2 Through-car equivalence factors 9 2.1 Effect of approach grade on saturation flow rate 15 2.2 Relationship between area type and saturation slow 23 2.3 Base saturation flows based on Number of lanes and Population 25 3.1 Site details 33 3.2 Classification of vehicles 34 4.1 Average SF obtained as per equation (4.1) 37 4.2 Proportion of different vehicle types 38 4.3 Values of regression coefficients and corresponding R 2 values with different parameters 45

Chapter 1 Introduction 1.1 General An intersection is a location where two or more roads carrying traffic streams in different directions cross each other at different or same elevation. In the case of developing countries like India, intersection at the same grade is common phenomenon as the other one is very expensive. However, they provide more safety and least delay comparatively. The space which is common to all these roads is generally known as the intersection. At such a location, different traffic streams compete with one another for the use of that common space at the intersection. The flow at an intersection is always chaotic; the safety and efficiency at such locations is low. Hence, various strategies are used to control the flow of traffic at an intersection in order to improve the safety and efficiency of traffic flow. In the developing countries like India, the traffic is mix in nature and usually consists of vehicles with different static and dynamic operating features. This kind of traffic tends to be haphazard causing congestion in general, especially at intersections. Vehicles mixing in a traffic stream is unrestricted mostly due to roads design without any physical segregation. This creates several difficulties for the traffic engineers, both in the design of facilities as well as in the management of vehicular operations. Based on the number of approaches crossing together at the intersection, intersection may be defined as 3-legged, 4-legged or multi-legged intersection. 4-legged intersections are the most common type of them. 5-legged intersections are lesser but still existing especially where nonrectangular blocks are present in urban areas. 6-legged or above intersections are very rare. Other than this, based on traffic control exercised on intersecting approaches, Intersections can be classified as: Uncontrolled Intersections, with the help of some priority rules which may be different in different countries (or sometimes using warning signs to ward off accidents). YIELD-sign controlled or STOP-sign controlled Intersections are also a common type of intersections which uses the yield signs and stop signs respectively to control the traffic.

Signal Controlled Intersections using traffic signals in which all approaches are allotted different times to proceed, mostly in urban areas. Traffic Circles, all the vehicles on any approaches are forced to move around the circle. It includes roundabouts, rotaries, mini-roundabouts etc. 1.2 Signal Controlled Intersection An intersection, governed by traffic signals is known as a signal controlled intersection as shown in Figure 1.1. At a signal controlled intersection, the common space is periodically given to certain flows while the other conflicting streams are barred from entry at that time. In a manner of speaking, the common space is time-shared among the various flows. Various kinds of time sharing strategies are followed at signalised intersections like pre-timed, partially actuated, and fully actuated signalizations. In the pre-timed signalization, the different conflicting flows share the space according to a pre-defined strategy which repeats at a fixed interval. This fixed interval is referred to as the cycle length. During the cycle length, the time for which a particular stream can utilize the intersection is referred to as the green time for that stream or movement; the time during which a particular movement cannot utilize the intersection is referred to as the red time for that movement. Invariably during the change over from green to red an amber signal is shown to warn the driver that a red signal is impending. During the amber time for a movement, the vehicles of that movement can use the intersection. Of course, the sum of green, amber and red times for a particular movement is equal to the cycle time. Actuated signals are just like pre timed signals in which they are constrained by time limits, but unlike pre timed signals, actuated signals can switch phases before they reach their limits if the demand is low. They can even skip phases if there is no demand for that phase. For this reason, actuated signals are especially useful in low-demand settings, such as in rural areas or at night. Fully-actuated signals refer to the intersections for which all phases are actuated and hence, it requires detection for all vehicular movements. Fully-actuated signal is ideally suited to isolated intersections where the traffic demands and patterns are varying widely during the course of the day. There are various advantages of instrumenting fully-actuated controls. First, it decreases the delay relative to pre-timed control by being highly responsive to traffic demand and to changes in traffic pattern. In addition, detection information allows the cycle time to be efficiently allocated

on a cycle-by-cycle basis. Finally, it allows phases to be skipped if there is no call for service, thereby allowing the controller to reallocate the unused time to a subsequent phase. Capacity at signal controlled intersections is calculated using saturation flow rate and flow ratio. Flow ratio is the ratio of actual volume (or flow) of approach (Vi) to the saturation flow rate (Si). Capacity at any signal controlled intersection can be adjudged using equation 1.1. C i = S i g i C (1.1) where, Ci = capacity of lane group i (veh/hr) Si = saturation flow rate for lane group i (veh/hr) g i C = effective green ratio for lane group i As per HCM 2010, procedure for estimating traffic signal capacity uses a base saturation flow rate of 1900 PCUs per hour of green per lane and several other adjustment factors. Figure 1.1: Signalized Intersection 1.3 Saturation Flow Saturation flow at signal controlled intersections is maximum discharge at stop line during green time until entire queue dissipates. According to HCM 2010, saturation flow is defined as the

equivalent hourly rate at which previously queued vehicles can traverse an intersection approach under prevailing conditions, assuming that green signal is available at all times and no lost times are experienced. Saturation flow is measured in vehicles per hour of green per lane. It is one of the most important parameters for finding out the capacity and level of service of an intersection. In usual practice, saturation flow is estimated by observing the average headways for vehicles arriving at the intersections or passing any prefixed reference point. Figure (1.2) signifies the traffic departure process at signal controlled intersection for the case where queue waiting on the approach does not vanish by the end of green period. This basic model of the departure process was devised by Webster in traffic signals published in 1958. Figure 1.2. An idealised model of saturation flow (Webster, 1958) 1.4 Factors influencing Saturation Flow Table 1.1 summarizes the various elements typically considered within each of the four broad factors, affecting the saturation flow.

Table 1.1 List of principle factors affecting saturation flow Factor Elements affecting Saturation flow Width of approach Geometric factors Number of lanes Grade Radius of turn Peaking characteristics Operating Conditions Parking characteristics Bus stop operations Signal timing and phasing arrangements Traffic composition Traffic Characteristics Turning movements Pedestrian activity Traffic pressure Weather Area type & Site characteristics Environmental and other factors Driver behaviour Area population Roadway surface conditions

Above mentioned elements have certain effects on the saturation flow which is shortly explained in following sections: 1.4.1 Geometric Factors Type of area, no. of lanes in each approach, approach width, gradient and whether a LT or RT lane exists or not, are the parameters that comes under the category of geometric factors at an intersection affecting the saturation flow. 1.4.1.1 Width of approach Earlier studies conducted by Webster and Cobbe (1966) analysed that saturation flow will vary linearly with the approach width. They developed a relation between saturation flow and the approach width as a linear function as shown in equation (1.2). S = 160 w... (1.2) Where, S = Saturation flow in PCUs per hour of green W = Total approach width in feet Aforementioned equation has been included in IRC (1985), where width of approach has been taken in meters and adjustment factors for right turning and left turning vehicles in the stream, are included and represented as equation (1.3). S = 525 w.. (1.3) But this relation (equation 1.3) is valid only in the range of 5.1 meters to 18 meters as below and beyond this range, relationship between saturation flow and approach width is slightly non-linear. 1.4.1.2 Number of lanes in the approach It has been observed that for same approach width, number of lanes the approach had been divided have an impact on saturation flow. Width divided into more number of lanes provide more saturation flow than that by lesser number of lanes.

Also narrow approaches means lesser number of lanes and eventually lesser width of lanes resulting in longer queues for same vehicular traffic and vehicles will traverse in close proximities to each other laterally. This may be neutralised by slow speeds and acceleration causing delays and therefore, decrease in the saturation flow. 1.4.1.3 Effect of gradient Gradient at an intersection is the difference between the slopes of two points, one at the stop line and other at 60m from the stop line. Studies carried out on gradient have proved that downgrade increases discharge and upgrade decreases discharge. 1% uphill grade reduces the saturation flow by 3% and 1% downhill grade increases the saturation flow by 3%. But studies conducted on grades were in the range of 10% uphill and 5% downhill. 1.4.1.4 Effect of right turn British method recommended saturation flow for approaches with exclusive right turning lanes with no opposing flow, should be obtained separately. Saturation flow of traffic stream varies with the turning radius. These relations have also been adopted in IRC:SP 41 (1994) as given by equation (1.4) and equation (1.5). Single RT lane, s = 1800 1+ 1.5 r Double RT lane, s = 3000 1+ 1.5 r. (1.4)..... (1.5) where, S = saturation flow in PCUs per hour of green r = radius of turn in meters 1.4.2 Operating Factors Operating or control conditions like 1-way or 2-way movement, parking on approach, exit or bus stops and signal or phasing arrangements also affect the saturation flow at signalised intersections. 1.4.2.1 Peaking characteristics

Saturation flow at peak hour and at off-peak hour is different. Webster (1966) suggested that saturation flow at off-peak hours is 6% lesser than the one in peak hours. Several researchers recommended that three parallel lines are a good representation of variation in the saturation flow in different times of the day. 1.4.2.2 Parking Characteristics Parking is one of the important operating parameters that affect the saturation flow at a signalised intersection. Due to parking activities on the road-side, effective road width available is reduced at stop line resulting in the reduction of the saturation flow. This reduction in the saturation flow depends on the vehicle type which has been parked. IRC has given an equation for estimating the loss in effective width due to parking activity. Effective loss = 1.68 0.9 (z 7.5) g. (1.6) where, z = clear distance of parked vehicle from the stop line in meters g = green time in seconds As per British method, if clear distance is less than 7.5 m then z must be taken equal to 7.5 m. Also if parked vehicle is a heavy one then effective loss must be increased by 50%. 1.4.2.3 Bus stop operations Any bus stops adjacent to the intersection would cause frequent stopping of buses and henceforth, stopping of other vehicles in the approach. At stops, buses will be required to accelerate or decelerate resulting in delays to other vehicles in the approach. HCM (2010) had taken in account this effect and proposed a PCE value of 5.0 but notes that further research on this parameter is required. 1.4.2.4 Signal timing and phases system Experimental investigations of intersections with green and two stage crossings at signalised intersections showed that increase in green time have a negative effect on the capacity of the

intersection. Researchers have suggested maximum and minimum green time to be 45s and 20s respectively for better performance. 1.4.3 Traffic Characteristics It includes vehicular characteristics, turning traffic, traffic composition, pedestrian conditions and traffic pressure are main parameters under this section affecting saturation flow. 1.4.3.1 Traffic Composition Composition of traffic implies that proportion of different categories of vehicles in a traffic stream. Static and dynamic characteristics of all the vehicles are not the same. Heavy vehicles like trucks, buses accelerate very slowly and have a greater impedance effect on other vehicle categories. According to impedance imposed by different vehicles type, they have been assigned different equivalence factors as shown in the Table 1.2. Table 1.2. Through-car equivalence factors (Webster and Cobbe, 1966) Vehicle Type Through car equivalence factors Car 1.00 Heavy truck 1.75 Bus 2.25 Light Truck 1.00 Tram 2.50 Motor Cycle 0.33 Bicycle 0.20 1.4.3.2 Turning traffic Right turning vehicles impede the flow of traffic severely because short radius turn requires slowing down of the vehicle considerably below through traffic speeds. Left turning vehicles, if not provided with separate lane or phase must wait for the suitable gap in the approach traffic and therefore, causing delay to themselves as well to following vehicles in the lane. Webster and Cobbe (1966) suggested that one RT vehicle is equivalent to 1.25 times the through vehicle for streams where RT traffic is more than 10%.

1.4.3.3 Pedestrian activity Pedestrians are the most unsafe users and their requirements are different and affect the saturation flow. Their presence at a signalised intersections will affect the headways and other performance parameters of traffic flow. Several researchers have recommended adjustment factors for pedestrian activities ranging from 1.0 for little pedestrian activity to 2.0 for greater pedestrian activity. 1.4.3.4 Traffic pressure At high volume intersections, Bonneson (1992) found that the number of vehicles affect saturation flow rate. Saturation headway measured for 5 th car reduced with increase in queue length. Similar trend was found for 6 th, 7 th and 8 th queue positions too. He names this effect as traffic pressure. 1.4.4 Environmental, climatic and other factors Performance of the driver is strongly related to environmental conditions affecting the capacity of saturation flow. 1.4.4.1 Weather Environmental parameters such as poor visibility or unfavourable conditions cause decrease in the speed and thus on the capacity of the intersection. Many studies have been carried out on effect of weather and visibility on saturation flow. When the surface of the road is wet due to rainy weather or other reasons, driver tends to slow down the speed and hence, throughput of the intersection gets decreased. 1.4.4.2 Area type The type of area around the intersections is also an important parameter affecting saturation flow. As per HCM, saturation flow is 10% lesser in central business districts (CBDs) than other areas. Similarly, flow at recreational areas is smaller as compared to other area types. This reduction is due to high percentage of drivers unknown to local street system. 1.4.4.3 Site Characteristics

It is also a parameter that affects the saturation flow. British method for estimating saturation flow necessitates set of existing conditions. If the intersection visibility is good, having low pedestrian flows and dual carriageway then location is known as good site. For these types of sites, saturation flow is taken as 20% more than the estimated value. 1.4.4.4 Driver Behaviour Majority of the influencing factors as described till now are quantitative in nature which are predictable as we go from one location to another. However, there are certain factors which cannot be quantified. All these factors generally comes under driver behaviour category. Performance of a vehicle is largely dependent on mental, physical and emotional condition of a driver. When taken collectively, these characteristics with little changes in the individuals are the reason why most of the researchers say them as factors other than geometric and traffic controls causing natural variability in traffic data. 1.4.4.5 Area population Various findings have been drawn suggesting that SF in small population areas is lower. Probably due to less aggressive nature of the drivers in small cities than those in large urban areas. Saturation flow is highly sensitive when population is less than 100000 and almost negligible when above 500000. 1.4.4.6 Roadway surface conditions Roads either with lesser number of undulations or constructed with proper care can serve as the corridor with large flows of high speed. Approaches at an intersection, if well maintained, will have larger saturation flow than other approaches. Roads with large undulations cause vehicles to slow down thus resulting in less saturation flow. 1.5 Problem Definition Saturation flow rate is one of the most critical and important factors in evaluating the capacity of a lane or lane group at a signal controlled intersection. There are several factors influencing saturation flow rate as geometric, traffic and operating parameters. Several methods have been developed to estimate the saturation flow rate at a signal controlled intersection and thus capacity

of an intersection approach. U.S. and U.K. methods are two of the most popular methods available in the literature. Indian code of practice has been advocating the use of U.K. method to estimate saturation flow at a signal controlled intersection (IRC: SP 41-1994). U.K. method is based on the total approach width while in U.S. method, width of individual lane or lane groups is considered. Researches have shown that saturation flow is influenced by many geometry, traffic and control factors. Influence of these factors is mainly dependent on the driving culture, therefore is different in different countries. It will be worthwhile to examine the effect of individual or set of some parameters on saturation flow at a signal controlled intersection for Indian conditions. Developing suitable relationship between saturation flow and these factors in the form of an equation is essential requirement of the research. Because vehicular traffic in Indian conditions is largely heterogeneous in nature and vehicles differ based on static and dynamic characteristics. 1.6 Objectives of the study The present study is taken up with the following objectives: i) To study the concept of saturation flow at signalized intersection and the factors affecting saturation flow. ii) To develop relations between the saturation flow and traffic composition, approach width & turning movement, and compare them with those available in the literature. iii) To estimate the degree of saturation of the intersection based on the saturation flow and examine the loading of the intersection. iv) For delay measurement using saturation flow estimated in the study. v) To quickly measure quality of service and loading of the intersection.

1.7 Outline of the Report The report consists of five chapters and documented as following: Chapter 1 gives a brief introduction about the concepts of saturation flow and various factors affecting the saturation flow. Problem definition and objectives of the study has also been outlined here. Chapter 2 presents a comprehensive literature review summarizing the various studies published in the international and national journals and conference proceedings on the different factors influencing the saturation flow and various methods of estimating the saturation flow. Chapter 3 outlines the field studies conducted during thesis period and the methodology that has been adopted for the present study. Chapter 4 includes the analysis part on the estimation of saturated green and thus saturation flow for through and right turning movements, development of the equation showing relation between traffic composition and the saturation flow. Chapter 5 sums up the report and concludes the work done till now and gives recommendations for further work.

Chapter 2 Literature Review 2.1 General Saturation flow is a vital traffic performance measure of efficiency of an intersection. Capacity at signal controlled intersection is based on the concept of saturation flow and saturation flow rates. Saturation flow is defined as the maximum flow rate on an approach or lane group that can pass through the intersection under prevailing roadway and traffic conditions by considering 100 per cent green time is available to the vehicles on the approach. Various factors affecting the saturation flow at signalised intersections have already been explained in section 1.4. This chapter presents the literature related to the work that has been done on the saturation flow and factors that affect saturation all over the world. 2.2 Geometric factors Road Research Laboratory in England has done substantial work on saturation flow at intersections. The best-known research on the relationships between intersection geometries and saturation flow is that of Webster and Cobbe (1966). 2.2.1 Effect of approach width Webster and Cobbe (1966) did most inclusive and thorough work on signalised intersections in London. They found linear relationship between saturation flow (S) and approach width (W), in feet, as given by the equation (1.2). The same formula (equation 1.2) is included by IRC with approach width in metres and certain adjustment factors are taken for the effect of right turns and left turns in IRC SP: 41, 1994 as mentioned in equation (1.3). Saturation flow could easily be measured as per equation 1.3 for the width of approaches more than 5.1 metres. Sarna and Malhotra (1967) gathered data at different traffic signalized intersections in Delhi with different width of approaches and analysed. They gave a model for these two as shown in Equation (2.1). They have estimated the saturation flow at intersections, converted the different category of vehicles into PCUs and with the help of PCUs, analysed the field data. S = 129.5W + 103.5. (2.1)

Correlation Coefficient between the variables was quite high. Therefore, the linearity of regression analysis was very compelling. Suggested equation was established for approach width in the range of 16ft to 28ft with RT traffic to the extent of about 10%. Miller (1969) experimented on various signalized intersections in Australia. He came to the conclusion that Saturation flow increases as the lane width increases but in the range of 3.05m to 3.95m. Bhattacharya et al. (1982) done experiments on number of signal controlled intersections in Calcutta. They suggested a linear relationship between saturation flow for through movements and width of approaches as in equation (2.2). S s = 490W 360.. (2.2) But valid only when width is in between 3.5m to 10.5m. Singh (1992) studied the effect of approach width on saturation flow with the concept of dynamic PCU in India. He found out dynamic PCUs by analysing three intersections data in New Delhi. He concluded that a vehicle s PCU is governed by homogeneity/heterogeneity of the vehicular traffic stream and reduces with increase in its own proportion in the stream and also depends on the approach width and carries a linear relationship with it. Saturation flow is also directly dependent on the approach width and a linear relationship exists between the two parameters. Susilo and Solihin (2011) collected data from various signalised intersections in Indonesia with approach width varying from 3m to 12m. They ranked up data in 3 categories small(3,4,5.9.m), medium(6,7,8.9m) and large(9,10,11,12m) and suggested that S=600We is applicable for small and medium categories while for large types, S=500We+400 is used because for large category, there was significant difference between the observed and calculated values. 2.2.2 Number of lanes Kimber and Semmens (1982) experimented various tracks at TRRL, UK. Whole width of the approaches were divided into number of lanes of certain width (WL). Only through traffic was included in the study. Even though there was already a significant relation between saturation flow

and approach width but with increase in number of lanes (n) (i.e. approaches with lesser lane widths) a rising trend in the saturation flow was observed. Relationship observed between saturation flow and lane width follows non-linear nature as given by equation (2.3). S L = 196 W 2 L 979W L + 2964 PCUs/hr..(2.3) S = n S L SL did not depend on the number of lanes instead on the lane width. However, equation was significant when WL is greater than 2.5m. McMahon et al. (1997) also studied the effect of number of lanes with saturation flow. They examined through movements on 12 intersections in Miami, Florida. Findings of the research shown that, as the number of lanes in approach were increasing there was increment in the saturation flow rate. 2.2.3 Gradient As per Webster and Cobbe (1966), grade of the approach at the intersection is also an important parameter affecting saturation flow. Effect is as per Table 2.1. Table 2.1 Effect of approach grade on saturation flow rate Gradient Effect on saturation flow +1% (i.e. uphill grade) -3% (Reduction) -1% (i.e. downhill grade) +3% (Increase) But applicable only when grade is limited to 10%. Reilly and Pfefer (1982) examined the capacity and level of service at signalised intersections. They also suggested that grade affects the saturation flow as with 1% increase or decrease in gradient changes saturation flow by 0.5%.

2.2.4 Turning Radius Webster and Cobbe (1966) reported a relationship using saturation flow and turning radius. They gave an equation to measure RTSF as mentioned in equation (1.4) and (1.5). These equations are also applicable to left turning traffic. Kimber and Semmens (1982) also studied the effect of radius of turn on the saturation flow. Results obtained by them were similar to the ones by Webster and Cobbe. They gave a general relation of Webster s results as S = A (1 + B..(2.4) r) and suitable fitting of data was obtained when A = 1795 PCU/hr and B= 4.9 ft. However, findings of research did not completely agree with those by Webster. 2.3 Operating Conditions 2.3.1 Peaking Characteristics Webster and Cobbe (1966) studied and noticed that saturation flow is different during off peak than during peak period. Difference between the two saturation flow obtained was around 6%. Branston (1979) examined factors influencing intersection capacity. He reported that saturation flow was different during distinct times of the day. It was so due to difference in the level of illumination and as well as difference in peaking characteristics. He suggested three different equations as (2.5), (2.6) and (2.7). He also proposed that best representation of such changes is by drawing three parallel lines. For off-peak periods: S = 885 + 68 w. (2.5) For day-light peak times: S = 1045 + 68 w.. (2.6) For after-dark peak periods: S = 960 + 68 w.. (2.7)

2.3.2 Parking Characteristics Webster and Cobbe (1966) observed that parked car in the close proximity of the stop line cause reduction in the saturation flow rate as following expression; Loss in Approach width (in ft) = 5.5 0.9(z 25) k...(2.8) HCM also stated that parking must be prohibited for a distance in ft equivalent to 5 times the green time in seconds from the stop line. HCM also suggested that there was an increase in the capacity by P(D-20)5G %, where P=total %age of turns, D= distance in feet from the crosswalk and G= green time (s). This implies that additional space acquired by this prohibition is given to turning vehicles. 2.3.3 Bus operations HCM 1965, took in account of buses effect on saturation flow for the first time. HCM also proposed a passenger car equivalent (PCE) value equal to 5.0 for buses but recommended further study on this parameter. HCM 2000, has taken adjustment factors for the effect of local buses known as bus blockage. It is given by equation (2.9). Adjustment factor for bus blockage, where, f bb = N 14.4N B 3600 N.(2.9) N = number of lanes in a lane group NB= number of buses stopping per hour 2.3.4 Signal timing and phasing system Teply (1983) observed that saturation flow is not only dependent on the site specific characteristics but also on the green time length and on the community type. He tried to find the dependence of the saturation flow on green time, degree of saturation, climate conditions and light conditions.

Data for the study was taken for 5 intersection approach lanes in Edmonton and 14,000 vehicles for 8 intersection approach lanes in Calgary. He made some conclusions as are given below: - For single green periods, average saturation flow rate was observed in the range of 20s to 30s of green. - Saturation flow decreased in after 20s of green in winters. - As the green interval increased, there was drop in the saturation flow The author also proposed minor changes regarding the concept that saturation flow rate is constant irrespective of any changes in the green phase. The maximum saturation flow was observed when the green interval is 30 to 50 s long, and reduces at larger values. Teply et al. (1995) further investigated the effect of longer green time on SF at signalised intersections. As per CCG (Canadian Capacity Guide), saturation flow as the rate at which the vehicles standing in a queue during the red interval cross the stop line of a signalized intersection approach lane during the green interval. Estimation of saturation flow is done by determining headways for the vehicles in the queue from 1 st to last one. Unlike HCM 2000, which estimates it by counting headways for 4 th vehicle to the last one in the queue. They also observed a fall in the flow rate if green time is more than 30s. CCG proposed a relation between SF as per HCM 1985 and as per CCG in equation (2.10). S HCM = 1.05 S CCG (2.10) Based on this, it can be interpreted that SF estimated by the HCM 2000 is an over estimate. CCG also suggested a need of modification to saturation flow values for larger green intervals. Khosla (2005) examined the impacts on saturation flow during longer cycle lengths in the Dallas/Fort worth metropolitan area for longer greens at signalized intersections. After analysis of the data, he concluded that the primarily given hypothesis about slow reduction in saturation flow rate with interval of longer green display can be cancelled based on the results obtained and investigation in Dallas/Fort worth area.

2.4 Traffic parameters 2.4.1 Traffic composition Large vehicles and turning vehicles impose greater impedance than through cars as they require large radius of turning base. These effect are taken care by equating them to equivalent TCUs (through car units). Webster and Cobbe (1966) experimented at TRRL and calculated equivalents car values as presented in Table 1.2. Homogeneous equivalent flow is obtained by changing mixed traffic using PCUs. It is a very typical parameter to examine and proper estimation of this factor has been a tedious ask for the researchers. Kimber et al. (1985) described PCUs and suggested 3 distinct methods to estimate its value for different vehicle types. Three different methods are Webster s method, headway ratio method and regression method. Greenshields et al. (1947) & Molina (1968) had used headway ratio method for the estimation of PCUs of different vehicle types. Benekohal et al. (2000), Rahman et al. (2004) considered delay as the primary parameter for calculating PCUs. However, methods proposed were not suitable for heterogeneous traffic. Anusha et al. (2013) collected data from number of signalised intersection in Bangalore and studied the impact of 2W proportion on saturation flow. They concluded that capacity and two wheeler proportion are in linear relationship with positive slope while other categories of the vehicles had negative impact on the saturation flow rate. They also proposed to take into account the two wheeler proportion while calculating capacity of the intersection. 2.4.2 Effect of turning vehicles Webster (1975) also investigated the effect of RT vehicles with three conditions. 1 st, when RT flow is facing the problem of countering gaps in the opposing traffic so they will get delayed and thus causing delay to non-turning vehicles in the lane. 2 nd, they would try to take place in off side lane used by through traffic with risk of getting delayed and lastly, RT vehicles which require more time to turn would delay the cross phase traffic start. First two effects are compensated by taking a factor of 1.75*through vehicle for a RT vehicle.

The third effect was more complex and equation (2.11) was provided to achieve maximum number of RT vehicles nr per cycle taking advantage of gaps in the opposing traffic stream. n r = s r (g s g c ) s q.... (2.11) where, q = flow s= saturation flow for opposing stream g= green time c= cycle time Sr = effective RT saturation flow in vehicle per hour Leong (1964) formed equation (2.12) to take into account the effect of left turning and right turning vehicles on saturation flow. S = 1700 12CV 11RT 2LT. (2.12) where, S = saturation flow per lane of traffic (vphg) CV = proportion of commercial vehicle.(%) RT = proportion of right turning vehicle.(%) LT = proportion of left turning vehicle (as per cent of flow) Bhattacharya et al. (1982) observed that right turning saturation flow in passenger car volume per hour of effective green time increases as the approach width and radius of turn increases. Equation (2.13) gives the mathematical relationship among these parameters. where, S r = 375w 289 R 47 (2.13)

Sr = saturation flow of right turning movement. R = radius of turn in meter W = approach width in meter. Srinivasam et al. (1989) also investigated the impact of turning vehicles on SF. They formed relation as given in equation (2.14) with SF and %age of turning traffic as parameters. Y = 0.35X + 1.05 (2.14) Where, Y= %age reduction in capacity X= %age turning traffic Guo et al. (2012) examined bicycles effect on operations of intersection where bicycle traffic is significant. They studied effect of bicycle conflicts with turning traffic in different stages and attempted to develop a relation for saturation flow for turning traffic. 2.4.3 Traffic Pressure In a study of saturation flow rate at high volume intersections, Bonneson (1992) found that the number of vehicles in queue had an effect on the saturation flow rate. The saturation headway measured for the fifth car in the queue decreased with increasing queue length. This trend was found for the sixth, seventh and eighth etc. queue positions also. He called this effect of traffic pressure. It is believed to result from the presence of aggressive drivers (e.g., commuters). Bonneson and Messer (1998) examined the effect of traffic pressure at 12 interchanges and 12 intersections. Their analysis of 7704 saturation headways led to the development of the following traffic pressure adjustment factor. f tp = 1 1.07 0.00486v l (2.15) where ftp = adjustment factor for traffic pressure; and vl = average flow rate per lane (i.e. traffic pressure) for lane group in veh/ln/cycle

Equation (2.15) had derived based on the premise that 15 veh/ln/cycle represented the base condition. The effect of traffic pressure on saturation flow rate is shown in Figure 2.1. The trend line indicates that intersection approaches with relatively low volume (and short queues) will have a low saturation flow rate and those with high volume will have a high saturation flow rate. Figure.2.1 Effect of traffic pressure on saturation flow (Bonneson & Messer, 1998) 2.5 Environmental and other factors 2.5.1 Weather Branston (1979) investigated the effect of weather conditions or more precisely distinct daytime on saturation flow rate as explained in section 2.3. He also observed change in saturation flow with changes in weather (dry vs wet). Difference between two values was significant during off peak period at 5% level statistically. Miller (1969) also did comparison among saturation flows for different weather conditions (as rain, overcast, and fine) and observed variation in three values significantly.

Teply (1983) found that headways in winter conditions are higher than those obtained in summers. Dry and wet weather conditions had not affected that much the vehicle headways. Chodur et al. (2011) examined the difference between saturation flow for distinct weather conditions and found out that in rainy weather conditions, saturation flow rate reduces. This decrease was 8.5% to 12.3% for longer rainfalls and 3.6% for short rainfalls. They also noticed saturation flow decreases by 10% in snowfall conditions and by 11.4% for cloudy/foggy weather. 2.5.2 Area type HCM 2000 has also taken an adjustment factor for relatively lower interactions in central business districts (CBDs) than otherwise reflected in other areas. It suggests that there is a reduction of 10% in saturation flow for CBD areas than in non-cbd areas. Also saturation flow with recreational areas around are lower and similar to CBD areas. Zegeer (1986), Le et al. (2000) and Agent and Crabtree (1982) developed several relationship between saturation flow rate and area type. These outings are presented in the Table 2.2. Table 2.2: Relationship between area type and saturation flow Adjustment factors by area type CBD Outlying Residential Recreational Author(s) commercial area area district HCM (2000) 0.90 1.00 1.00 N.A Le et al. (2000) N.A 1.00 1.03 0.92 Zegeer (1986) 0.99 1.00 1.01 N.A Agent & 0.97 1.00 1.00 N.A Crabtree (1982) N.A = Not available

2.5.3 Area Population Two researchers Zeeger (1986) and Agent & Crabtree (1982) have identified the relationship between the population and its traffic movements. Both researchers have found that saturation flow is lower in the area of small population. Presumably, drivers in smaller cities and towns are less aggressive than those in large urbanized areas and prefer to drive at a less hurried place. The relationship between area population and saturation flow rates as recommended by these two researchers is as shown in the figure below. Figure 2.2 Relationship between area population and saturation flow rate (Bonneson, 2005) The data points shown in Figure 2.2 determine the adjustment factors recommended by Zegeer and by Agent and Crabtree. The trend line shown represents a best fit to these factors. The shape of this trend line suggests that saturation flow is highly sensitive to population in areas with population less than 100,000. They have suggested that for area population above 500,000 the effect of population is appears to be negligible. Perez-Cartagena and Tarko (2005) have studied on saturation flow rates in Indianapolis. The authors have observed that the base saturation flow is varying from one location to another location

which are having almost same geometry and traffic conditions. They proposed that area population and number of lanes in the subjective group are the key factors for the variation of saturation flows between the two locations having identical conditions in geometry and traffic. The authors have provided the base saturation flows for the approaches of the intersections having different number of lanes, located in areas which are having different population that are given in Table 2.3. Table 2.3 Base Saturation flows based on Number of lanes and Population Number of lanes Population < 20 thousands 20-100 thousands Indianapolis 1 1,540 1,800 1,960 2 1,580 1,840 2,010 3 1,600 1,860 2,020 They have stated that a saturation flow adjustment factor for population is needed and they have proposed 0.92 and 0.79 as adjustment factors for medium towns and small towns respectively.

Chapter 3 Field Study and Methodology 3.1 General This chapter deals with the methodology that has been adopted to collect and extract the data as the part of the research. The method of analysis is also presented here. Before starting the field study, site identification for the study was very important. Regarding the study, intersections undertaken must be situated in urban or sub-urban area with signal controls available at the site. Any intersection data collected would be suitable for the estimation of saturation flow rate. However, following requirements were considered to select an intersection: Since the scope of the study is limited to the signal controlled intersections, therefore only signal controlled intersection having three or four legs were to be selected for data collection. Intersection must be the one having all the approaches at same level (i.e. at-grade intersection). Good visibility criteria must be fulfilled for all the legs of selected intersections from the reference point. Any bus stop or parking activities must not be present in the close proximity to the intersection. 3.2 Data Collection The data collected for the study can be categorised into two namely, Geometric data and Traffic data. The geometric data at the intersections (i.e. approach width) was collected manually with the help of measuring wheel and measuring tape. However, the traffic data to be collected, needed mounting up of camera at sufficient height so that all arms of an intersection are visible in a single frame or at least two mutually perpendicular legs of the intersection are visible. Twelve intersections locations were selected as per the criteria and data were collected using videography. Intersections selected for the study were from 3 different cities of north India

namely Delhi, Chandigarh and Patiala. Ten of them, had four arms with varying approach width and right turning is allowed at all the approaches in all the intersections except for the intersection at Prithviraj Chowk area, Delhi. Two of the intersections were 3-legged at Dandi Chowk and Mother Teresa Hospital Chowk. 3.3 Layouts of the Intersections Details of the selected intersections are given below: Intersection 1 This intersection was located in Prithviraj chowk area in New Delhi. Figure 3.1 shows the layout of the intersection with geometric details. Total cycle length available on this approach was 210 sec out of which red and green interval were 175 sec and 30 sec respectively with an amber of 5 sec. Figure 3.1 Layout of intersection 1. All dimensions are in m.

Intersection 2: The second intersection was located in Dandi chowk area in New Delhi. Figure 3.2 shows the layout of the intersection. This intersection has 3 legs with each arms having three lanes. Data collected from these three-legged intersections was analysed but not used for the objectives since the data merged was collected from all geometrically alike intersections. Figure 3.2 Location of intersection 2 & 3. Intersection 3: The third intersection was located in Mother Teresa Hospital Chowk area in New Delhi. Figure 3.2 shows the layout of the intersection. This intersection also has 3 legs with each arms having three lanes.

Intersection 4 This intersection was located in Vishwakarma Road area in Patiala (Punjab). The four legs of the intersection lead to the destinations: North leg to Rajpura, South leg to Rajpura, West leg to Arya Samaj and East leg to Vikas Colony. It is shown in Figure 3.3. Figure 3.3 Location of intersection 4 Two approaches of the above mentioned intersection were taken for the study i.e. Rajpura Road approach 1 and approach 2. Signal showed the cycle length of 112 sec of which red and green time were 74 sec and 34 sec respectively with 4 sec of amber on both the approaches. Intersection 5 This intersection was located in Urban State Bypass area in Patiala, Punjab as shown in Figure 3.4. This intersection has also the similar geometry as of the intersection 4. Total cycle length was 118 sec with 79 sec of red signal, 36 sec of green time and 3 sec of amber available at the approach.

Figure 3.4 Intersection on Patiala urban state bypass Intersection 6 This intersection is located in Jawaharlal Nehru Stadium Chowk in Delhi. This intersection is a four legged intersection. Cycle length at this intersection was found to be 160 sec of which red and green interval were 125 sec and 30 sec respectively with 5 sec of amber. Intersection 7 & 8 These intersections were located in Chandigarh sector 46 and sector 47. Geometric details have been given in Table 3.2. Snapshot of the intersection at sector 46 is given in Figure 3.5. Cycle lengths are 122 sec for both of the intersections, out of which 93 sec of red and 25 sec of green interval for both of the intersections are allowed with an amber time of 4 sec.

Figure 3.5 Intersection at Sector 46, Chandigarh Intersection 9 & 10 These intersections were located at Dwarka sector 21 & 22, in Delhi as shown in Figure 3.6. At these approaches, cycle lengths were 169 sec and 168 sec for sector 21 and sector 22 respectively. At sector 21, red and green interval was for 115 sec and 50 sec respectively with amber of 4 sec. Allocated green and red at sector 22 was 60sec and 104 sec respectively with 4 sec of amber. Figure 3.6 Intersection at Dwarka sector21, Delhi

Intersection 11 This intersection was selected at Manimajhra railway crossing intersection in Chandigarh as in Figure 3.7. Cycle of 134 sec was allowed for the approach of which 96 sec of red and 35 sec of green were permitted with amber length of 3 sec. Figure 3.7 Manimajhra Railway Crossing Intersection, Chandigarh Intersection 12 Intersection was at Sector 45C, Chandigarh as in Figure 3.8. 180 seconds of cycle length was available for the approach. Red and green time were 130 sec and 45 sec respectively with amber length of 5 sec. Figure 3.8 Sector 45C, Chandigarh