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TRAFFIC FORECASTING FOR A NATIONAL HIGHWAY BY TRANSPORT DEMAND ELASTICITY METHOD: A Case Study For An Indian National Highway

By Hrmanth M Kamplimath Assistant Professor Department of Civil Engineering Nirma University PREFACE

With the recent thrust on improving and developing highways for boosting the national economy, the importance of Traffic Demand Forecasting (TDF) has increased significantly. Therefore, in order to improve the rationality of traffic forecast, proper estimation of traffic growth rate is of prime importance. In the present study, the complete process of traffic forecasting by transport demand elasticity method, its merits and demerits have been addressed and demonstrated through a case study. It is observed that with the constraints of availability of proper data and fluctuation of developing economy, the task of traffic growth estimation could be quite subjective and approximate. Different approaches and necessary considerations for improving the rationality of traffic growth rate have also been addressed in the study. In the present study an attempt was made to analyze the Origin and Destination data for passenger vehicles and goods vehicles by Roadside Interview method. The results of O-D studies are presented in the form of O-D matrices. The passenger and freight characteristics such as average trip length, frequency, purpose and trip influence were analyzed. Socio-economic data such as Per-capita income, Net State Domestic Product, population and data on vehicle registration were collected from statistical data sources. The relationship between annual growths of vehicles in percentage over a number of years is established. To determine elasticity values, regression analysis was carried out between socio-economic variables growth index and vehicle growth index. The elasticity values for the future years were projected based on the growth trend of vehicles.

ii ACKNOWLEDGEMENT

I would like to express my gratitude to many people who saw me through this book; to all those who provided support, read, wrote, offered comments, allowed me to quote their remarks and assisted in the editing and proofreading. First and foremost I would like to thank Prof. Varuna M, Mr. Vijay Kumar Sagar and Mr. Yashas Barghav for their constant guidance without which this book would have been unsuccessful. I also wish to express my gratitude to Dr. Paresh Patel, Head, Department of Civil Engineering and my colleagues at Nirma University for their continuous encouragement and support. Finally I would like to thank my wife, Madhushree and the rest of my family for their constant motivation. Last and not least: I beg forgiveness of all those who have been with me over the course of the years and whose names I have failed to mention.

Hemanth Kamplimath

iii CONTENTS

Šƒ’–‡”Ǧͳ ͳ

INTRODUCTION ...... 1

1.1 General...... 1 ϭ͘ϭ͘ϭZŽĂĚdƌĂŶƐƉŽƌƚ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϭ ϭ͘ϭ͘ϮZŽĂĚEĞƚǁŽƌŬŝŶ/ŶĚŝĂ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘Ϯ ϭ͘ϭ͘ϯEĂƚŝŽŶĂů,ŝŐŚǁĂLJƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘Ϯ 1.2 Background ...... 4 1.3 Traffic forecasting methodologies ...... 5 ϭ͘ϯ͘ϭ&ŽƌĞĐĂƐƚďĂƐĞĚŽŶƉĂƐƚƚƌĞŶĚƐĂŶĚĞdžƚƌĂƉŽůĂƚŝŽŶ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϱ ϭ͘ϯ͘ϮdžƚƌĂƉŽůĂƚŝŽŶĨƌŽŵWĂƐƚƚƌĞŶĚƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϲ ϭ͘ϯ͘ϯĐŽŶŽŵĞƚƌŝĐDŽĚĞůƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴ ϭ͘ϯ͘ϰdƌĂǀĞůĚĞŵĂŶĚĨŽƌĞĐĂƐƚŝŶŐŵŽĚĞů͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴ 1.4 Limitation of Traffic forecasting ...... 9 1.5 Period of Forecasting ...... 9 1.6 Asian Development Bank Guidelines on Traffic Forecast ...... 10 ϭ͘ϲ͘ϭ'ĞŶĞƌĂů͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϭϬ ϭ͘ϲ͘ϮsĞŚŝĐƵůĂƌ'ƌŽǁƚŚZĂƚĞ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϭϬ ϭ͘ϵ͘ϯZĞĐŽŵŵĞŶĚĞĚ'ƌŽǁƚŚZĂƚĞ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϭϭ 1.7 Demand Modelling a Review ...... 11 1.8 Estimating Future Road Traffic - Studies Conducted Abroad ...... 14 1.9 Origin - Destination Studies ...... 14 ϭ͘ϵ͘ϭEĞĞĚĨŽƌKͲ^ƵƌǀĞLJ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϭϰ ϭ͘ϵ͘ϮƉƉůŝĐĂƚŝŽŶŽĨKͲĂƚĂĨŽƌdƌŝƉ&ŽƌĞĐĂƐƚ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϭϱ 1.10 Trip Length Frequency Studies ...... 16 1.11 Summary of the Literatures reviewed ...... 17 1.12 Need for the Study ...... 17 1.13 Objective and Scope of Work ...... 18 1.14 Traffic Forecasting Methodology ...... 18 1.15 Report Organization ...... 19 Chapter-2 ...... 20

TRAFFIC FORECASTING ± THEORY AND CONCEPTS ...... 20 iv 2.1Traffic Forecasting ...... 20 Ϯ͘ϭ͘ϭ'ĞŶĞƌĂů͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϮϬ Ϯ͘ϭ͘Ϯ&ŽƌĞĐĂƐƚŝŶŐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϮϬ Ϯ͘ϭ͘ϯEĞĞĚĨŽƌdƌĂĨĨŝĐ&ŽƌĞĐĂƐƚŝŶŐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘Ϯϭ 2.2 Econometric Model ...... 22 2.3 Regression Analysis ...... 22 Ϯ͘ϯ͘ϭĞƐĐƌŝƉƚŝŽŶŽĨZĞŐƌĞƐƐŝŽŶŶĂůLJƐŝƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘Ϯϯ 2.4 Elasticity Method ...... 23 2.5 Factors influencing traffic growth ...... 25 2.6 Estimation of traffic growth rates by Transport Demand Elasticity Approach ...... 25 Chapter ± 3 ...... 27

TRAFFIC FORECASTING - METHODOLOGY ...... 27

3.1 Methodology ...... 27 3.2 Reconnaissance Survey ...... 29 3.3 Traffic Surveys ...... 29 ϯ͘ϯ͘ϭůĂƐƐŝĨŝĞĚdƌĂĨĨŝĐsŽůƵŵĞŽƵŶƚ^ƵƌǀĞLJ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘Ϯϵ ϯ͘ϯ͘ϮKƌŝŐŝŶʹĞƐƚŝŶĂƚŝŽŶΘŽŵŵŽĚŝƚLJDŽǀĞŵĞŶƚ^ƵƌǀĞLJƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϯϬ 3.4 Traffic Forecast ...... 30 Chapter - 4 ...... 31

TRAFFIC VOLUME DATA COLLECTION AND ANALYSIS ...... 31

4.1 General...... 31 4.2 Objective of Traffic Studies ...... 31 4.3 Data collection and Surveys ...... 31 4.4 Reconnaissance and Road Inventory Study ...... 32 4.5 Selection of the traffic Survey locations...... 33 4.6 Classified Volume Count Surveys ...... 33 ϰ͘ϲ͘ϭDĞƚŚŽĚŽůŽŐLJ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϯϯ ϰ͘ϲ͘ϮdƌĂĨĨŝĐ^ƵƌǀĞLJWůĂŶŶŝŶŐĂŶĚ^ĞůĞĐƚŝŽŶŽĨdƌĂĨĨŝĐ^ƵƌǀĞLJ>ŽĐĂƚŝŽŶ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϯϰ ϰ͘ϲ͘ϯůĂƐƐŝĨŝĐĂƚŝŽŶŽĨdƌĂĨĨŝĐ͗͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϯϰ ϰ͘ϲ͘ϰWhsĂůƵĞƐĚŽƉƚĞĚ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϯϱ 4.7 Classified Volume Count Survey - Findings ...... 35 4.8 Location-wise analysis of Traffic Volume Count ...... 38 ϰ͘ϴ͘ϭdƌĂĨĨŝĐsŽůƵŵĞĂŶĂůLJƐŝƐĂƚEĂůĂǀĂĚŝ;ϭϱϵ͘ϱϬϬͿ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϯϴ v ϰ͘ϴ͘ϮdƌĂĨĨŝĐsŽůƵŵĞĂŶĂůLJƐŝƐĂƚ,ĂůůŝŬĞƌŝ;ϮϮϭ͘ϰϬϬŬŵƐͿ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϰϭ 4.9 Seasonal variation of Traffic Volume ...... 43 ϰ͘ϵ͘ϭ^ĞĂƐŽŶĂůŽƌƌĞĐƚŝŽŶ&ĂĐƚŽƌ͗͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϰϰ 4.10 Annual Average Daily Traffic (AADT) ...... 46 Chapter - 5 ...... 47

ORIGIN - DESTINATION STUDIES AND ANALYSIS ...... 47

5.1 General...... 47 5.2 Zoning ...... 47 5.3 Sample Size ...... 48 5.4 Influence Factors ...... 49 ϱ͘ϰ͘ϭĞƚĞƌŵŝŶĂƚŝŽŶŽĨ/ŶĨůƵĞŶĐĞ&ĂĐƚŽƌƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϱϬ ϱ͘ϱ͘ϭŶĂůLJƐŝƐŽĨKͲĂŶĚŽŵŵŽĚŝƚLJDŽǀĞŵĞŶƚ^ƵƌǀĞLJ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϱϭ 5.6 O-D Analysis at Nalavadi ...... 51 ϱ͘ϲ͘ϭdƌŝƉŚĂƌĂĐƚĞƌŝƐƚŝĐƐͲWĂƐƐĞŶŐĞƌsĞŚŝĐůĞƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϱϭ ϱ͘ϲ͘ϮƵƐKͲŚĂƌĂĐƚĞƌŝƐƚŝĐƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϱϱ ϱ͘ϲ͘ϯKͲŚĂƌĂĐƚĞƌŝƐƚŝĐƐŽĨ'ŽŽĚƐsĞŚŝĐůĞƐĂƚEĂůĂǀĂĚŝ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϱϳ 5.7 O-D analysis at Hallikeri ...... 61 ϱ͘ϳ͘ϭdƌŝƉŚĂƌĂĐƚĞƌŝƐƚŝĐƐͲWĂƐƐĞŶŐĞƌsĞŚŝĐůĞƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϲϭ ϰ͘ϳ͘ϮƵƐKͲŚĂƌĂĐƚĞƌŝƐƚŝĐƐ͗͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϲϰ ϱ͘ϳ͘ϯKͲŚĂƌĂĐƚĞƌŝƐƚŝĐƐŽĨ'ŽŽĚƐsĞŚŝĐůĞƐĂƚ,ĂůůŝŬĞƌŝ͗͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϲϱ Chapter ± 6 ...... 68

TRAFFIC FORECASTING ...... 68

6.1 Traffic growth rates ...... 68 ϲ͘ϭ͘ϭ'ƌŽǁƚŚZĂƚĞďĂƐĞĚŽŶWĂƐƚdƌĂĨĨŝĐĂƚĂ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϲϴ ϲ͘ϭ͘Ϯ'ƌŽǁƚŚZĂƚĞďĂƐĞĚŽŶsĞŚŝĐůĞZĞŐŝƐƚƌĂƚŝŽŶ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϲϴ 6.2 Project Influence Area ...... 69 6.3 Vehicle Population Growth ...... 70 6.4 Socio-Economic Profile of the Influence Region ...... 70 6.5 Estimation of traffic growth rates by Transport Demand 6.5.1Elasticity Approach ...... 72 ϲ͘ϱ͘ϮůĂƐƚŝĐŝƚLJsĂůƵĞƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϳϮ ϲ͘ϱ͘ϯZĞĐŽŵŵĞŶĚĞĚůĂƐƚŝĐŝƚLJǀĂůƵĞƐ͗͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϳϯ ϲ͘ϱ͘ϰ&ƵƚƵƌĞĞĐŽŶŽŵLJƉƌŽƐƉĞĐƚƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϳϱ 6.6 Traffic growth rates ...... 76

vi ϲ͘ϲ͘ϭƐƚŝŵĂƚŝŽŶŽĨ'ƌŽǁƚŚZĂƚĞƐĨŽƌŽŵŵĞƌĐŝĂůsĞŚŝĐůĞƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϳϳ ϲ͘ϲ͘Ϯ'ƌŽǁƚŚZĂƚĞƐĨŽƌWĂƐƐĞŶŐĞƌsĞŚŝĐůĞƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϳϳ ϲ͘ϲ͘ϯZĞĐŽŵŵĞŶĚĞĚ'ƌŽǁƚŚZĂƚĞƐĨŽƌƚŚĞWƌŽũĞĐƚZŽĂĚ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϳϳ ϲ͘ϲ͘ϰZĞŐŝŽŶĂůĐŽŶŽŵŝĐĞǀĞůŽƉŵĞŶƚ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϳϴ 6.7 Traffic Projection...... 80 ϲ͘ϳ͘ϭEŽƌŵĂůdƌĂĨĨŝĐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴϬ ϲ͘ϳ͘Ϯ'ĞŶĞƌĂƚĞĚdƌĂĨĨŝĐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴϬ ϲ͘ϳ͘ϯŝǀĞƌƚĞĚdƌĂĨĨŝĐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴϭ ϲ͘ϳ͘ϰ&ŝŶĂůdƌĂĨĨŝĐWƌŽũĞĐƚŝŽŶƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴϭ 6.8 Capacity Analysis ...... 83 Chapter- 7 ...... 85

DISCUSSIONS AND CONCLUSIONS ...... 85

7.1 Discussions ...... 85 ϳ͘ϭ͘ϭŝƐĐƵƐƐŝŽŶƐďĂƐĞĚŽŶƚƌĂĨĨŝĐǀŽůƵŵĞĐŚĂƌĂĐƚĞƌŝƐƚŝĐƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴϱ ϳ͘ϭ͘ϮŝƐĐƵƐƐŝŽŶƐďĂƐĞĚŽŶ/ŶĨůƵĞŶĐĞ&ĂĐƚŽƌƐ͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴϱ ϳ͘ϭ͘ϯŝƐĐƵƐƐŝŽŶƐĂƐĞĚŽŶůĂƐƚŝĐŝƚLJsĂůƵĞƐĂŶĚZͲƐƋƵĂƌĞĚǀĂůƵĞƐ͗͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴϲ ϳ͘ϭ͘ϰŝƐĐƵƐƐŝŽŶƐĂƐĞĚŽŶ'ƌŽǁƚŚZĂƚĞƐ͗͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴϲ ϳ͘ϭ͘ϱŝƐĐƵƐƐŝŽŶƐŽŶĂƉĂĐŝƚLJŶĂůLJƐŝƐ͗͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘͘ϴϳ 7.2 Conclusions ...... 88 7.3 Scope for Further Study ...... 89

REFERENCES ...... 90

ANNEXURES ...... 94

vii LIST OF ABBREVIATIONS

ADB: Asian Development Bank

ADT: Average Daily Traffic

AADT: Annual Average Daily Traffic

EME: Earth Moving Equipment

GNP: Gross National Product

HCM: Heavy Construction Machinery

IRC: Indian Road Congress

LCV: Light Commercial Vehicles

NHAI: National Highway Authority of India

NHDP: National Highways Development Project

NSDP: Net State Domestic Produce

O-D: Origin and Destination

PCI: Per-Capita Income

PCU: Passenger Car Unit

PWD: Public Works Department

SCF: Seasonal Correction Factors.

TDF: Traffic Demand Forecasting

TLF: Trip Length Frequency

viii LIST OF TABLES

Sl. No. Title Page No.

Table 1. 1: Elasticity values recommended by A D B ...... 11 Table 1. 2: Suggested Growth Rates for Forecasting Traffic by ADB Guidelines...... 11

Table 4.1: Road inventory details ...... 32 Table 4.2: Location of Traffic Volume Surveys ...... 33 Table 4.3: Vehicle Classification System Adopted ...... 34 Table 4.4: Type of Vehicle and its PCU Equivalency Factors ...... 35 Table 4.5: Details of Traffic Volume Count ...... 36 Table 4.6: Percentage Share of Vehicular Traffic on the Project Stretch ...... 37 Table 4.7: Directional Distribution of Traffic near Nalavadi ...... 40 Table 4. 8: Directional Distribution of Traffic near Nalavadi ...... 43 Table 4.9: Monthly consumption of Fuel on the Project Corridor ...... 44 Table 4.10: Daily Consumption of Fuel on the Project Corridor ...... 44 Table 4.11: Seasonal Correction Factors ...... 45 Table 4.12: ADT and AADT details of the project corridor ...... 46

Table 5. 1: Sample Size Details at Nalavadi ...... 49 Table 5. 2: Sample Size Details at Hallikeri ...... 49 Table 5. 3 Breakup of Trip purpose for Passenger Cars at Nalavadi ...... 52 Table 5. 4: Trip Distance break up of Passenger cars at Nalavadi ...... 53 Table 5. 5: Percentage Trip Frequency details of Cars/Taxi/Trip Vans ...... 54 Table 5. 6: Trip Influence of Passenger Cars at Nalavadi ...... 55 Table 5. 7: Percentage Trip Distance distribution of Buses at Nalavadi ...... 55 Table 5. 8: Trip Influence of Buses at Nalavadi ...... 56 Table 5. 9: Percentage Distribution of Commodities carried by Goods Vehicles ...... 57 Table 5. 10:Percentage Trip Distance Distributions of Goods Vehicles at Nalavadi ...... 58 Table 5. 11: Percentage Trip Frequency Distribution of Goods Vehicles at Nalavadi...... 59 Table 5. 12: Trip Influence of Goods Vehicles at Nalavadi ...... 60

ix Table 5. 13: Breakup of Trip Purpose for Passenger Vehicles at Hallikeri ...... 61 Table 5. 14: Breakup of Trip Distance for passenger cars at Hallikeri...... 62 Table 5. 15: Percentage Trip Frequency Distribution of Passenger cars at Hallikeri ...... 63 Table 5. 16: Trip Influence of Passenger Vehicles at Hallikeri ...... 63 Table 5. 17: Percentage Trip Distance Distribution of Buses at Hallikeri...... 64 Table 5. 18: Percentage of State wise Trip Influence of Buses at Hallikeri ...... 64 Table 5. 19:Percentage Distribution of Commodities carried by Goods Vehicles ...... 65 Table 5. 20: Percentage Trip Distance characteristics of Goods Vehicles at Hallikeri ..... 66 Table 5. 21:Percentage Trip Frequency Distribution of Goods Vehicles at Hallikeri ...... 66 Table 5. 22: Percentage State wise Trip Influence of Goods Vehicles at Hallikeri...... 67

Table 6.1: State wise Influence of Vehicles observed on the Project Road ...... 69 Table 6.2: Summary of CAAGR % in Karnataka state ...... 70 Table 6.3: NSDP at Constant Prices (Rs. Cr) ...... 71 Table 6.4: PCI at Constant Prices (Rs.) ...... 71 Table 6.5: Population (Thousands) ...... 71 Table 6.6: Elasticity Values Derived by Regression Analysis for Karnataka State ...... 73 Table 6.7: Suggested Elasticity Values by Dr. L.R Kadiyali (2001-2021)...... 74 Table 6.8: Suggested Elasticity Values by IRC ...... 74 Table 6.9: Recommended Elasticity Values ...... 75 Table 6.10: Projected growth rates of Economic variables ...... 76 Table 6.11: Projected Traffic Growth Rates from 2013 to 2023 ...... 78 Table 6.12: Base year Traffic...... 82 Table 6.13: Traffic Projections for the two Homogenous Sections ...... 83 Table 6.14: Design service volume for plain terrain...... 84 Table 6.15: Details of the capacity analysis...... 84 Table 7.1: Influence Factors Obtained from O-'$QDO\VLV«««««««««««86 Table 7.2: Elasticity Values Derived based on Regression Analysis for Karnataka ...... 86 Table 7.3: Comparison of Growth Rates ...... 87 Table 7.4: Adopted Growth rates for Traffic Forecasting ...... 87 Table 7.5: Results of Capacity Analysis ...... 88

x LIST OF FIGURES

Sl. No. Title Page No.

Figure 1.1: Project Corridor ...... 4

Figure 3.1: Flowchart showing the Project Methodology««««««««««««28

Figure 4.1: Project stretch««««««««««««««««««««««««32 Figure 4.2: Graph showing Average Daily Traffic at Nalavadi and Hallikeri...... 37 Figure 4.3: Showing Traffic Compositions at Nalavadi ...... 39 Figure 4.4: Hourly variation of Traffic near Nalavadi ...... 39 Figure 4.5: Daily variation of Traffic near Nalvadi ...... 40 Figure 4.6: Directional Distribution of Traffic near Nalavadi ...... 40 Figure 4.7: Traffic Compositions at Hallikeri ...... 41 Figure 4.8: Hourly variation of Traffic near Nalavadi ...... 42 Figure 4.9: Daily Variation of Traffic at Hallikeri ...... 42 Figure 4.10: Directional Distribution of Traffic near Nalavadi ...... 43 Figure 4.11: Average monthly fuel sales on the Project Corridor ...... 45

Figure 5. 1 Zone Map ...... 48 Figure 5. 2: Break up of Trip Purpose at Nalavadi ...... 52 Figure 5.3: Breakup of Trip Distance for passenger cars at Nalavadi ...... 53 Figure 5.4: Percentage Trip Frequency details of Cars/Taxi/Trip Vans ...... 54 Figure 5.5: Percentage Trip Distance distribution of Buses at Nalavadi ...... 56 Figure 5.6: Percentage of Commodity Carried by Goods Vehicles ...... 58 Figure 5.7: Percentage Trip Distance Distributions of Goods Vehicles at Nalavadi...... 59 Figure 5.8: Percentage Trip Frequency details of Goods Vehicles ...... 60 Figure 5.9: Breakup of Trip Purpose for Passenger Vehicles at Hallikeri ...... 62

xi Chapter-1 INTRODUCTION

1.1 General Traffic analysis and the forecast is an important element of any feasibility /detailed project report preparation. Traffic analysis and demand forecasting is directly related to several important aspect of road infrastructure planning and design i.e., lane width requirements, geometric design features, pavement design, economic and financial analysis etc. Towards this, an effort has been made to undertake detailed traffic studies, analysis, forecasting and to carry out laning requirements. Various steps followed in this regard are described in the subsequent sections.

Due to the increasing population and the vehicular volume, there is a demand for traffic forecasting for the future years. Forecasting the traffic is very essential for planning and design of infrastructure facility. Any new construction would mean enormous expenditure and unless the traffic system really warrants the provision, it is advisable to refrain spending from funds on projects, which are economically not viable. In order to estimate traffic on new links, and their impact on existing road system, recourse to scientific modelling techniques becomes necessary. In the choice of the methodology used for forecasting, preferences will have to be given to such techniques, which need minimum data inputs, which are either to be collected or already available through the census.

1.1.1 Road Transport About 60 per cent of freight and 87.4 per cent passenger traffic is carried by road. Easy availability, adaptability to individual needs and cost savings are some of the factors which go in favour of road transport. Road transport acts as a feeder service to the railway, shipping, and air traffic. The number of vehicles has been growing at an average pace of around 10 per cent per annum. The share of road traffic in total traffic has grown from 13.8 percent of freight traffic and 15.4 percent of passenger traffic in 1950-51, to an estimated 60 percent of freight traffic and 87 percent of passenger traffic by the end of 2005-06.The rapid expansion and strengthening of the road network, therefore, is imperative, to provide for both present and future traffic and for improved accessibility to Introduction

the hinterland. In addition, road transport needs to be regulated for better energy efficiency, less pollution and enhanced road safety [1].

1.1.2 Road Network in India India, having one of the largest road networks of 41.09 lakh km, consists of National Highways, Expressways, State Highways, Major District Roads, Other District Roads and Village Roads with following length distribution: x National Highways/Expressway -71,772 km x State Highways- 1,54,522 km x Major District Roads- 2,66,058 km x Other District Roads & Rural Roads- 36,17,240 km

The National Highways have been classified on the basis of carriageway width of a Highway. Generally, a lane has a width of 3.75 m (in the case of the single lane) and 3.5 m per lane in case of multi-lane National Highways [1].

The percentage of National Highways in terms of width is as under: x Single Lane/ Intermediate lane -15,536 km (21%) x Double lane- 38,536 km (54%) x Four Lane/Six lane/Eight Lane -17,700 km (25%)

1.1.3 National Highways At present, National Highway network of about 71,772 km comprises only 1.7% of the total length of roads but carries over 40% of the total traffic across the length and breadth of the country. Considering the target growth rate of about + 9 %, it is estimated that the total target NH network of about 85,000 km may be considered as reasonable for the 12th )LYH

Development of National Highways during 11th Five Year Plan (2007-12) At present, out of 71,772 km of National Highways about 24% length is of 4-lane and above standards, 52% length is of 2-lane standard and 24% length of the single and intermediate standard. As on July   NP OHQJWKV RI 1+¶V ZHre entrusted to

2 Introduction

NHAI, 38,629 km to State PWDs and 3,565 km to Border Roads Organization. As more and more works are awarded under various phases of NHDP and subsequent phases of NHDP are taken up, the additional length of NHs will be transferred from State PWDs to NHAI [2].

National Highways Development Project

In order to take up the improvement and development of National Highways, National Highways Development Project (NHDP), the largest highway project ever undertaken by the country was initiated in a phased manner. Implementing agency for NHDP is National Highway Authority of India (NHAI).

The National Highways Development Project is a project implemented in 1998 by the government to upgrade, rehabilitate and widen major highways in India to a higher standard. The NHDP launched by the government to upgrade highways in the country has been divided into seven phases: x Phase I and Phase II comprised the Golden Quadrilateral (GQ) (5,846 km), NS-EW Corridor (7,142 km), port connectivity and other road projects (1133 km) at an estimated cost of INR 65,000 Crore x Phase-III is up gradation of 12,109 km of national highways at an estimated cost of INR 80,062 crore x Phase IV consists of double-laning 5,000 km of highways at a cost of INR 6,950 crore x Phase V comprises six-laning 6,500 km consisting of 5,700 of the GQ stretch and remaining 800 km of other National Highways at a cost of INR 41,210 crore x Phase VI involves the construction of 1,000 km of expressways at an estimated cost of INR 16,680 crore. x Phase VII involves the construction of ring roads, grade separated intersections, flyovers, elevated highways, rail over bridges, underpass and service roads at an estimated cost of INR 16,680 crore [1].

3 Introduction

1.2 Background The Project stretch is a part of NH-63 in the state of Karnataka. It runs from east to west connecting Karnataka to Andhra Pradesh. The total length of NH-63 is about 432 km, out of which 370 km runs in Karnataka State and about 55.4 km runs in Andhra Pradesh. NH-63 starts from the junction of NH-17 near Ankola in Karnataka and ends at Gooty on NH 7 in Andhra Pradesh. The major cities/towns along NH-63 in Karnataka State are Yellapur, Kalghatagi, Hubli, Gadag, Koppal, Hospet, Torangallu, and Bellary. This highway also connects Balekeri port and Karwar port to the mining areas of Bellary District.

This report deals with Hubli ± Hospet stretch of NH-63. The project stretch starts at km 132+000 of NH-63 at the junction with NH-4 Hubli-Dharwad bypass and ends at km 268+700 at the junction of NH-63 and NH-13, Hitnal Junction. The project corridor is presented in Fig.1.1.

Figure 1.1: Project Corridor

4 Introduction

1.3 Traffic forecasting methodologies: Macro level forecasts for road transport have been made by different expert bodies using one or more of the under mentioned methodologies. x Trend analysis x Time series analysis x Regression analysis x Econometric Demand Models x Activity Demand Models etc.

1.3.1 Forecast based on past trends and extrapolation The simplest method of forecasting is to analyze the past data for a number of years and to extrapolate the past trends assuming that the conditions will continue to change in future at the same rates as in past. Obviously, such a simplification suffers from many disadvantages, although it is relatively easy and cheap. It is good enough in a stable environment, which is beyond the influence of any major change in production, population and so on. The analyst has to carefully study the past data and look out for any indicators that are likely to influence the future pattern.

Data on certain known contributors to traffic are analyzed and then the traffic is forecasted based on any relationship between traffic and these contributors such as population, gross domestic product, vehicle ownership, agricultural and industrial output, fuel consumption etc. Forecast of these parameters may not be very reliable, but a reasonable accuracy should be acceptable for purposes of traffic forecasting [3].

For establishing reliable growth rate, the data on various parameters which influence the present and future traffic collected should be for quite a number of years. The analysis can then be done for the entire period, and also for blocks of 5 years.

The best way to arrive at the rate of growth is through a regression analysis. The formula expressing the compound rate of growth of traffic is:

n Pn = Po (1+r) ...... (1.1)

Where,

5 Introduction

Pn = Traffic in the nth year

P0 = Traffic flow in the base year n = Number of years r = annual rate of growth of traffic expressed in decimals,

Taking log on both sides,

LogePn = logeP0+ n loge (1 +r) ...... (1.2)

Y= A0 + A1 n ...... (1.3)

The above equation can he established from a data set of n values.

1.3.2 Extrapolation from Past trends One of the methods of estimation of the future rate of growth is to assume the same rate of growth as in the past. This may be all right for short-term projects, say 5-10 years. But, for long-term projections, it would be erroneous to assume that the past rate of growth will prevail for a long time in the future. Economic conditions are bound to change over a long period, and it would be necessary to modify the rate of growth accordingly. Subjective, assessments in this regard will have to be cautiously done.

The value of A1 is known as the Elasticity Coefficient. The Elasticity coefficient is the factor by which the GNP growth rate has to be multiplied to arrive at the growth rate of traffic [3].

Trend/Regression Type

Exponential: Applies a curved line to display data values that rise or fall at increasingly higher rates. For an exponential trend line,the data should not contain zero or negative values. [4].

This type of trend line uses the following equation to calculate the least squares fit through points:

Y = cebx ...... (1.4)

Where, c and b are constants and e is the base of the natural logarithm.

6 Introduction

Linear: Applies a best-fit straight line to display simple linear data sets that contain data values that increase or decrease at a steady rate.

This type of trend line uses the following linear equation to calculate the least squares fit for a line:

...... (1.5)

Where, m is the slope and b is the intercept.

Logarithmic: Applies a best-fit curved line to display data values that increase or decrease quickly before leveling out. For a logarithmic trend line, your data can contain negative and positive values.

This type of trend line uses the following equation to calculate the least squares fit through points:

...... (1.6)

Where, c and b are constants, and ln is the natural logarithm function.

Polynomial: Applies a curved line to display fluctuating data values. When this option is selected and a whole number from 2 to 6 is selected in the order box to determine how many bends (hills and valleys) appear in the curved line. For example, if the value of the order is set to 2, the chart typically displays only one hill or valley, a value of 3 displays one or two hills or valleys, and a value of 4 may display up to three hills or valleys.

This type of trend line uses the following equation to calculate the least squares fit through points:

...... (1.7)

Where, b and c1, c2«F6 are constants.

Power: Applies a curved line to display data values that compare measurements that increase at a specific rate. For a power trend line, your data should not contain zero or negative values.

7 Introduction

This type of trend line uses the following equation to calculate the least squares fit through points:

...... (1.8)

Where, c and b are constants.

1.3.3 Econometric Models If the past data is available in traffic for a number of years and the corresponding data on some economic indicator such as Gross National Product (GNP) is also available, then the data can yield an econometric model of the following type:

Loge P = A0 + A1 loge GNP ...... (1.9)

Where,

P = Traffic Volume

GNP = Gross National Product

A0 = Regression Constant

A1 = Regression coefficient

1.3.4 Travel demand forecasting model The travel demand forecasting model is a simulation model based on the functional relationship between the mobility rates, population growth and alternative modes of transport at the city-level.

Modal structures:

The travel demand forecasting model is driven by the variables, which are exogenously specified.

1. Population: projected population of the city for each year of the forecasting period.

2. Per capita trip rate: estimated number of trips per person per day.

3. Modal split: the relative share (%) of each alternative mode of transport in the total number of trips generated.

4. Average trip length: the average distance travelled per person per trip in the city.

8 Introduction

The modal developed by Prem Pangotra [5] projects total travel demand in the city for each year of the projection period. This is computed as follows:

TTDt = [PCTRt * POPt * MODSt ] * ATLt ...... (1.10)

Where,

TTDt = Total daily traffic demand in year t

PCTRt = Per capita trip rate in year t

POPt = City population in year t

MODSt = Share of each mode of travel

ATLt = Average trip length.

The various models developed by a number of researchers do have limitations and hence, the usage is also limited for forecasting. It is well understood that the only solution is to adopt and implement the sustainable multimodal transport to meet the demand and introduce modes based on sustainability. So that, the particular mode can share major trips for the future and should not become obsolete [5].

1.4 Limitation of Traffic forecasting Traffic forecasting, in the present state of knowledge, can at best be appropriate. Traffic is generated as a result of the interplay of a number of contributory factors. Forecasts of the traffic have, therefore, are dependent on the forecasts of contributory factors such as population, gross domestic product, vehicle ownership, agricultural output, fuel consumption and so on. Future pattern of change in these factors can be estimated with only a limited degree of accuracy and hence traffic forecasting cannot be done more precisely than this. In spite of this inherent drawback, traffic forecasting is a very important job a transport planner is called upon to do [6].

1.5 Period of Forecasting The question as to what period should be selected for traffic forecasting is difficult to answer. Since traffic forecasting is needed for transport plan, the design period selected for the transport plans should be sufficient for traffic forecasting. Long-range transport plans are not particularly useful since such plans do not exist in other sectors. In general,

9 Introduction

transport plans are for a period of about ten years- fifteen years in detail and an additional five years in less detail. In India, National Highways are designed for 15years after completion of the work [6].

1.6 Asian Development Bank Guidelines on Traffic Forecast

1.6.1 General The traffic demands forecasts have been prepared using forecast model based on growth in economy specified in terms of growth in population, Net State Domestic Product (NSDP), per ±capita income over the design period as suggested in Asian Development Bank (ADB) guidelines for ADB-III projects.

1.6.2 Vehicular Growth Rate Vehicular traffic growth depends on population, real income (per capita) and growth in a certain section of the economy namely, agricultural, mining, industry and tourism etc., Assuming the construction period as 2 years and design life 20 years the forecasts have been done up to 20 years [7].

Passenger Vehicle and Goods Vehicles;

The annual growth for buses, cars trucks of all types has been worked out from the correlation: [(1+P/100) x (1 + R /100) -1] * 100 *E ««««««««««««««««« 

Where,

P = the growth rate of population (in percent)

R = Real per±capita income (in percent)

E = The Elasticity value.

In the absence of other data for computation of elasticity values for project road influence area, the values of elasticity as recommended for ADB projects for different periods have been adopted. The values of elasticities as adopted are given below in Table 1.1 [7].

10 Introduction

Table 1. 1: Elasticity values recommended by A D B

MODE 1995-2004 2005-2016 Car, Jeep, Van 2.0 1.8 Bus 1.6 1.5 Two, Three Wheelers 2.3 2.1

1.9.3 Recommended Growth Rate The growth rates as worked out based on annual growth rate of state income and traffic demand elasticity as per ADB guidelines are given in Table 1.2

Table 1. 2: Suggested Growth Rates for Forecasting Traffic by ADB Guidelines

MODE 1999-2004 in % 2005-2016 in % Car, Jeep, Van 10.83% 9.05% Bus 8.66% 7.54% Two Wheeler 12.46% 10.56% Three Wheeler 8.62% 8.17%

1.7 Demand Modelling a Review Jason [8] has explored the inadequacy of the abstract model and to propose corrective modifications in order to render it a reliable an applicable tool for travel demand estimation. The purpose of the model modifications was subject to the rationality travel characteristics. The travel model developed in this analysis was formulated by use of a multiplicative form of a log- linear regression equation to evaluate the socio-economic variables.

Although numerous mathematical relationships can be applied to individual variable pairs, it seems that the preceding forms are most representatives for travel estimation. Variables selected in demand forecasting models are:

1. Socio- Economic Variables a) Population,

b) Per Capita Income,

c) Automobile Registration,

d) College Students Enrolments,

11 Introduction

e) Consumer's Sales taxes collected.

2. Transportation Variables

a) Travel time

b) Travel Cost

Virendra Kumar [9] has considered Rajasthan State as the study region which consists of eight districts. Various causal variables were identified which explain the behavior of the transport model. Casual variables uses were land use, commercial activity, agricultural activity, economic level and some other proxy variables such as population, employment size, industrial output, irrigated area, bank deposits both for freight generation and attraction.

The author had shown the use of secondary data to explain the volume of freight generated in the study region. The proxy variables had been used to represents the consumption and production pattern. However, non-availability of data at a finer level of spatial disaggregation had limited the scope of this work. Population emerged as one of the most important variables.

Kadiyali [10], Sarkar [11] and Vijay Kumar [12] tried various approaches in estimating growth rates to avoid assumptions inbuilt in the estimation of passenger and freight traffic. Various approaches tried were: x Based on vehicle population, annual utilization, and load or passenger carried x Based on traffic census data, supplemented by average load / passenger carried x Based on fuel consumption x Based on Socioeconomic variables

Due to the inherent strength of secondary data, it was found that the first approach was favorable. Various time series models were developed for each class of vehicles and freight movements. Economic indicators were GNP, Population, and per-capita income. Various regression equations were developed for passenger and freight transport and economic indicators. Finally, the Elasticity values obtained were used to calculate the growth rate and to project future traffic for the required number of years.

12 Introduction

Building a model was one part of the study and projections of road transport demand for future was another part. Various methodologies given below were considered for future projections: x Extrapolation of time series data on road transport movement for the future year. x Extrapolation of per-capita road transport for the future year. x Use of the economic models between road transport movement and GNP and determining road transport movement for possible values of GNP in the future. x Use of time series share on road/rail movement, extrapolate the same for the future year and determination of road share for possible values of the modal split. x Use of econometric model between total road and rail movement and GNP, projecting the same for possible values for GNP in the future of model split. x Various scenarios of population growth and GNP for projections have done considering pessimistic, attainable, optimistic and highly optimistic.

Thamiza Arasan [13] considered inter- city travel in thirty city-pairs in Tamil Nadu State for analysis of travel demand by bus and rail. The two-way daily inter-city travel, both by bus and rail in thirty city-pairs in the state has been considered for the analysis. The following socio-economic factors influencing the demand for travel were considered: x Total Population expressed in million x Number of households expressed in million x Literate expressed as percentage of total population x Migrants expressed as percentage of total population x Main workers, expressed as percentage of total population x Number of persons per house obtained by dividing the total population The analysis has resulted in the development of travel demand models of multiplicative form. Separate models for business and personnel trip was developed in this study. A model for total travel also was developed to check the travel predictions of the business and personnel trip models. The models were calibrated by multiple linear regression analysis. The demand variables (socio-economic factors of the city pairs) and the supply variables, (transport system characters) of the models together, are found to have considerable influence on the demand for inter-city travel.

13 Introduction

1.8 Estimating Future Road Traffic - Studies Conducted Abroad Different methods of traffic estimation used abroad include both mechanical and analytical. Most of the methods cannot be used by themselves in India at this stage, mainly due to the accelerated development of Indian economy, which makes past trends to be of not much relevance as indicators of future growth and also owing to the absence of systematic time series data on different important factors, The different methods involve a careful study of the economic development in different sectors during the period over which future estimates are required. Especially in the case of a developing economy it is absolutely essential or imperative to make a thorough macro - economic study of the economy, which alone would be a precursor for any traffic estimation. The future traffic on a highway is the result of the combination of four factors: x Current traffic that use the highway. x Diverted traffic, which would be attracted by other less desirable facilities to the new one. x Generated traffic which did not exist before the facility was opened and developed due to better accessibility, etc. and x Normal traffic growth, which results from land development which would not have occurred without the new facility.

1.9 Origin - Destination Studies Origin and Destination studies establish a measure of the patterns of movement of persons and goods within a particular area of interest. O-D studies estimate the travel characteristics observed for a typical day. These studies yield information about origin and destination of trips, time of day in which trips are made and mode of travel. O-D data include trip purpose; land use, social and economic data [14].

1.9.1 Need for O-D Survey In a transportation study, it is often necessary to know the exact origin and destination of the trips. It is not only necessary to know how many trips are made, but also group these trips with reference to the zones of their origin and destination. Other information's

14 Introduction

yielded by the O-D survey include the land use of the zones of origin and destination, household characteristics of the trip making a family, time of the day when the journey are made, trip purpose and mode of travel [15].

1.9.2 Application of O-D Data for Trip Forecast Various studies were conducted on O-D data of different sorts and a brief review of the various studies and their use of O-D data is given below. In a study overcome the limitation of estimating travel cost of internal and external trips and to study the effect of deleting Intra- Zonal trips on other trip types, the database of Texas commodity flows was used. Ghareib [16] conducted an O-D survey using a mailed questionnaire method to find the commodity flows in Taxes, USA. He used two different zoning systems, one of 254 counties and the other of 24 districts. He first calibrated the complete matrix to estimate Inter-Zonal and external trips while ignoring the Intra- Zonal trips. The author calibrated the matrix considering only Intra - Zonal trips and then combined both in a new distribution model. Some of the conclusions of this study were x With an increase in the number of traffic zones, the accuracy of estimated trips increases. x The exclusion of Intra -Zonal trips from the analysis increases the accuracy of the estimated trips at both county and district level. Satish Kumar [17] conducted O-D studies on freight characteristics to determine their trip length patterns, the frequency of travel, trip productions, and attractions. Efforts were made to identify the variables influencing the movement of goods transport and to determine the trip length frequency of Thiruvananthapuram district. From the analysis of primary and secondary data available, variables identified as the influencing factors in freight transportation are land use, employment, agricultural activity and accessibility. From the study, it is seen that the Trip Length Distribution of commodity carriers differs with the type of road, reflecting the functional characteristics of the road i.e., the importance of the road.

15 Introduction

1.10 Trip Length Frequency Studies

The primary characteristics of the highway freight transport are the behavior of transport operators to their haul lengths as reflected by the trip length frequencies. Trip Length Frequencies (TLF) denotes the percentage components of different haul distances. It is associated with the Spatial Distribution of production - consumption centers being served by the highway [18].

The TLF information can be effectively utilized in the following areas of highway planning and operation: x As a parameter for calibration and validation travel demand models and in mode specific demand models for determining the model shares x As an index for studying the distribution of different commodities in general and any specific commodity in particular so as to enable the development of a balanced regional highway network. x To reveal competitive and complementary nature of highway within modes of transport assisting in the development of an overall transportation system. x In the study of sensitiveness of transportation costs for a given commodity with trip lengths. x For planning roadside amenities. x For planning vehicles and crew for scheduling freight transport operation.

It is normally believed that trucks operate mainly for short-haul whereas large goods traffic is accounted by rail. To verify this trip length analysis is carried out of the data collected at the outer cordon of the study of Hyderabad region. Trip Length Frequency (TLF) analysis was presented separately for trucks operating on National Highway (NH), State Highway (SH) and Major District Road (MDR). For the individual category of roads, the trip length frequency reveals different characteristics. The trip length frequency on NH¶V shows that major component of traffic is of a long haul with peak beyond 700 km. The SH cater to short as well as long haul truck traffic while the truck trip length frequency for MDR shows a clear peak for short haul traffic. An interesting finding with respect to the trip length of interregional commodity movement by highway is the fact that around 40% of the trip length is more than 500 km and there are considerable flows even up to 1500 km. These movements are found to be

16 Introduction

dominated by costly goods, together with items like tea, where there is a concentrated production of such items only in specific areas. The higher trip length frequencies by highway vehicles call for an immediate attention to the problems of highway goods operators in providing necessary wayside facilities. Regional Freight Transport Demand Modeling study for Karnataka region by Sanghapriya [19] reveals that the average trip lengths and trip length distribution among commodity carriers, more than 15% of the trucks distances are greater than 500 km. The load carried by the goods vehicle varies according to the type of commodity being transferred.

1.11 Summary of the Literatures reviewed The literature reviewed helped in analyzing the importance of National Highways and accurate prediction of traffic on the Highways. It also highlighted various parameters affecting the estimation of growth rates for accurate forecasting of traffic and what proxy variables (Socio-Economic) that are needed or to be used when the data available is inaccurate or insufficient. The traffic demand models in theory and the traffic demand models actually used to forecast traffic showed that the Elasticity method is the most widely preferred model in India. As travel is the derived demand due to the interaction of numerous socio-economic activities, it is also imperative to study the growth of the socio- economic, demographic profiles in the project area influencing the travel demand.

1.12 Need for the Study

Karnataka has one of the largest reserves of high-quality iron ore in India. The reserves of iron ore in the state are the second largest in the country and estimated at about 3447 Million tons, consisting of about 929 Million tons of hematite ore and about 2518 Million tons of magnetite ore. There are as many as 153 Mining leases for iron ore in 16,000 hectares and 56 Million tons of iron ore is currently being extracted in the state out of which, 25 Million tons is being exported & 31 Million tons is being consumed by domestic industries (units in Karnataka and other states). The production is mainly concentrated in Bellary/Hospet area where the Project corridor is located and these mining/industrial activities have a lot of influence on the project corridor generating a huge amount of freight/goods traffic on the study stretch. As the study stretch is a two-

17 Introduction

lane national highway, it needs an up gradation due to the generated traffic from mining activities and industrialization. Hence, proper estimation of traffic growth rate is needed to predict future traffic. As understood from the literature reviews the task of estimation of traffic growth rates is subjective and approximate. Also, there are a number of methods for estimating traffic growth rates, choosing the best method of estimating the traffic growth rate for the project corridor is of prime importance.

1.13 Objective and Scope of Work The objective of the present study is to establish the existing traffic on the project corridor and to estimate the traffic growth rate in order to determine the future traffic demand. The scope of work includes: ‡ Reconnaissance of Project Corridor with respect to traffic studies ‡ Road Inventory Survey ‡ 7 days 24 hours classified traffic volume counts at the proposed locations ‡ One day 24 hrs Origin-Destination Survey at the proposed locations ‡ A collection RISUHYLRXV\HDU¶VIXHOVDOHVGDWDDORQJWKHSURMHFWFRUULGRUIURPWKH Fuel stations along the project corridor. ‡ Collection of secondary Economic and Demographic data ‡ Estimation of elasticity values. ‡ Estimation of traffic growth rates & future traffic forecasting ‡ Capacity Analysis

1.14 Traffic Forecasting Methodology The exercise of traffic growth rate estimation has been carried out by us using the elasticity approach. The elasticity method relates traffic growth to changes in the related economic parameters. According to IRC-108-1996, elasticity based econometric model for highway projects could be derived in the following form: Log e (P) = A0 + A1 Log e (, «««««««««««««««  Where: P = Traffic volume/Vehicle Registration of the State EI = Economic Indicator (GDP/NSDP/Population/PCI) A0 = Regression constant; A1 = Regression coefficient (Elasticity Index)

18 Introduction

The main steps followed are: ‡ Defining the Project Influence Area from OD analysis of travel pattern. ‡ Estimating the past elasticity of traffic growth from time series of registered vehicles of influencing states. ‡ Assessment of future elasticity values for major vehicle groups, namely, cars, buses and trucks. ‡ Study of past performance and assessment of prospective growth rates of state economies of influence area. All available traffic reports and forecasts were reviewed to compare with the established growth factor.

1.15 Report Organization This report has been segregated into six sections,

Chapter 1- Starts with a brief introduction on forecasting, factors influencing forecasting process and various forecasting models developed. This chapter also summarises the background study on various researches that have been carried out in the field of traffic growth rates and its projections.

Chapter 2- Deals with the need for the study, its objectives and scope of work along with brief methodology.

Chapter 3- Deals with the Traffic Volume survey and Estimation of Annual Average Daily Traffic.

Chapter 4- Provides details of O-D survey and its analysis.

Chapter 5- Deals with estimation of traffic growth rates by elasticity method, traffic projection, and capacity analysis.

Chapter 6 ± Results and Discussions

The survey formats, supporting data and additional Photographs have been included in the Appendices.

19 Chapter-2 TRAFFIC FORECASTING ± THEORY AND CONCEPTS

2.1Traffic Forecasting

2.1.1 General In the present chapter, the necessity of forecasting, as well as various models formulated by researchers, is highlighted. The need for traffic forecasting, a period of forecasts and its limitations are clearly stated. The limitations are huge in the Indian context, as most of the time the data collected are random with insufficient and inaccurate data. A brief outline of various forecasting methods, their advantages and disadvantages are made on the basis of literature available traffic and transport planning. Finally, the best-suited method for forecasting of traffic on the project corridor is decided.

2.1.2Forecasting It is the process of estimating the number of vehicles or people that will use a specific transportation facility in the near future. For instance, a forecast will give the estimates for the projected year the number of vehicles on a planned road network. Forecasting process begins with the collection of data for a good number of years. This traffic data is combined with other known data such as population, employment, trip length, travel cost etc. A great disparity exists between the direction of traffic demand forecasting by researchers and the traffic demand models used by transportation planning organizations. Activity-based models of travel demand have become increasingly studied in the academic realm and vast developments have been made over the past many years. However, among the forecasting tools used in practice by traffic/transportation planning organizations, traffic demand forecasting by the econometric method has been used widely in India for over 40 years [20].

The earliest models of traffic demand were intended to provide information on future infrastructure investments. Since that time, however, the focus for policy decisions has shifted from such long-term capital investments to short-term policies such as congestion management, promotion of alternative transport modes and demand management.

20 Traffic Forecasting- Theory & Concepts

In the past studies, different methodologies and techniques have been used to predict traffic volumes of both short term and long term. The traffic forecast models are predominantly based on time series analysis in which prediction of future is based on past values of variables. The time series models identify the pattern in the past data and extrapolate that pattern into future [21].

2.1.3 Need for Traffic Forecasting An estimation of future traffic on major roads is required for a variety of purposes. Forecasts of traffic are essential as they influence the engineering design of the facility and economic analysis for maintenance and rehabilitation. Overestimation of the traffic will result in more than necessary capital being tied up in a single project thus preventing other potential projects being taken up. Whereas, underestimation of the projected traffic will result in escalated maintenance along with, premature failures of the pavement structure thus causing heavy financial losses. The width of a pavement is decided on the basis of traffic volume that can be efficiently accommodated. A pavement needs to be widened when the traffic flow exceeds its capacity. Pavements are designed on the basis of the volume of commercial vehicles using the facility and more importantly the number of repetitions of standard axle loads during the design period. The volume of commercial vehicles and the repetition of standard vehicles govern the manner in which the pavements perform and deteriorate. The economic analysis of highway project relies for its accuracy on the correct assessment of the future traffic flows and the benefits derived from highway improvements. For a toll project, the stream of cash flow from toll collection is determined from traffic volume that is likely to use the facility. In all the above situations, the accuracy of which future traffic is predicted is of prime importance.

21 Traffic Forecasting- Theory & Concepts

2.2 Econometric Model If the past data is available in traffic for a number of years and the corresponding data on some economic indicator such as Gross National Product (GNP) is also available, then the data can yield an econometric model of the following type:

Loge P = A0 + A1 loge GNP ...... (2.1)

Where,

P = Traffic Volume

GNP = Gross National Product

A0 = Regression Constant

A1 = Regression coefficient

The value of A1 is known as the Elasticity Coefficient. The Elasticity coefficient is the factor by which the GNP growth rate has to be multiplied to arrive at the growth rate of traffic.

2.3 Regression Analysis Regression models, which deal with either single or multiple variables, are used for forecasting both the travel demand and the model share. In the Regression analysis, the past traffic data is required with one or more factor to cause and the effect of relationship.

Multiple linear regression models can be developed to explain theoretically any situation where a relationship exists between a dependent variable (say trips generated or trips made by a mode) and several independent variables. The same relationship can be used to forecasts future trips when the values for such independent variables would undergo changes while assigning inter-city distances. The section and formulation of variables are critical. The dependent variable should provide an adequate explanation of what the independent variable should measure adequately and what is to be predicted while the independent variable should provide an adequate explanation of the dependent variable as well as retain a separate identity. The assumptions are necessary before multiple linear regressions can be used: x A linear relationship exists between the dependent and the set of independent variables.

22 Traffic Forecasting- Theory & Concepts x The influence of the independent variable is additive, i.e., the inclusion of each variable contributes towards accounting the value of dependent variable [22]. The traffic projections can also be done by knowing the vehicle's ownership. The products of the average run of a vehicle in a year and the vehicle population gives the total amount of travel per year expressed in a vehicle-km. This method is mainly used for projection of urban traffic [23].

2.3.1 Description of Regression Analysis The regression analysis tool performs linear regression analysis by using the "least squares" method to fit a line through a set of observations. We can analyze how a single dependent variable is affected by the values of one or more independent variables. In the present case, registered vehicles by type are dependent variables whereas the economic parameters are independent variables.

(a) T-statistic

The t-statistic is a measure of how strongly a particular independent variable explains variations in the dependent variable. The larger the t-statistic, the better the independent YDULDEOH¶V explanatory power. Next to each t-stat is a P-value. The P-value is used to interpret the t-stat. In short, the P-value is the probability that the independent variable in question has nothing to do with the dependent variable. Generally, we look for a P-value of less than .05, which means there is a 5% chance that the dependent variable is unrelated to the dependent variable. If the P-value is higher than 0.10, a strong argument can be made for eliminating this particular independent variable from a model because it ³LVQ¶WVWDWLVWLFDOO\VLJQLILFDQW´ (b) R Square R Square is another measure of the explanatory power of the model. In theory, R-square compares the amount of the error explained by the model as compared to the amount of error explained by averages. The higher the R-Square, the better it is [24].

2.4 Elasticity Method The elasticity is defined as the transport demand increase in percentage terms of the economic indicator. Thus, an elasticity of two with reference to Gross National Product

23 Traffic Forecasting- Theory & Concepts

(GNP) indicates that for every one percent increase in the GNP, the passenger transport increase by 2 percent.

Future traffic depends on development in industrial, agriculture, mining, and other as sectors of the economy, as well as population development in these areas. Based on the past relationships between production-consumption and traffic requirements viz., the elasticity of transport demand is estimated.

According to the elasticity method, the rate of growth to be adopted for forecasting the traffic is determined based on the growth rates of population and per-capita income. Estimated annual production increase in the agricultural and industrial sector, for the specific project, are used as input parameters for each of the affected road networks in the corridor. The model is general and valid for both passenger and freight traffic. However, the elasticity with respect to income and population may differ for two and these are determined using the data on freight and passenger traffic separately. But what is not accounted for the Elasticity Method is that the transport demand elasticity changes over time and with the level of economic development. With the greater diversification of the economy, the elasticity of transport demand declines. Both these approaches have their limitations that rely mainly on extrapolating from past trends of various parameters and adjusting for specific foreseeable developments in the economy as gathered from plan documents and secondary data. Besides, the elasticity method addresses the problem at a macro level. It makes use of national level transport GHPDQGHODVWLFLW\¶VDVLWLVQRWHDV\WRFRPSXWHWKHVHDWGLVDJJUHJDWHGXQLWDQDO\VLVOLNH the state or district, in the absence of districts level income data [25]. In the study entitOHG³'HYHORSPHQWRI/RQJ7HUP3ODQIRU([SUHVVZD\VLQIndia´, traffic IRUHFDVWLQJ ZDV GRQH XVLQJ (ODVWLFLW\ 0HWKRG 7KH YDOXHV RI HODVWLFLW\¶V RI IUHLJKW DQG passenger traffic were estimated by calibrating the national level data on population, total income, and total road traffic both for freight and passenger traffic. The road traffic was considered as dependent variable and growth rates of population and income as the independent variables. Since more than one independent variable always gives a better relationship between traffic and socioeconomic factors, this method is an improvement over the methods adopted by the Steering Committee on Transport Planning (SCPT) and Asian Development Bank (ADB) which use an only single independent variable. The method also considers that transport elasticity of demand declines with economic

24 Traffic Forecasting- Theory & Concepts development. The growth rates for freight and passenger traffic are in terms of tonne-km and passenger-km respectively. These were converted into vehicular growth rates assuming the constant average vehicle utilization and changing vehicular mixes. Traffic projections were then made using the vehicular growth rates.

2.5 Factors influencing traffic growth The traffic growth is influencing by number of factors such as: 1. Economic factors: x Gross National Product (GNP) x Gross Domestic Product (GDP) or Net State Domestic Produce (NSDP) x Per Capita Income x Agricultural Product x Industrial Product 2. Demographic factors: x Population x Rural/Urban mix of population

Since above economic factors vary across the country, traffic growth rate varies from state to state, and within a state from region to region. The traffic growth rate has to be established for each location by giving the due consideration to the above factors.

2.6 Estimation of traffic growth rates by Transport Demand Elasticity Approach

Investment priorities are governed by traffic demand, assessed benefits and cost of the project. Demand plays the important role, which governs which type of facility / infrastructure to be created. This, in turn, determines likely benefits and costs to develop the same. A highway project of this nature calls for significant investment. Prediction of traffic demand becomes an important task and has to be carried out as accurately as possible. Accurate estimation of traffic has a direct bearing on the viability of the project. Recognizing this, efforts need to be made to carefully assess all the parameters that help in predicting the traffic demand in future, which necessitates realistic estimation of traffic growth rates. Traffic growth on a road facility is generally estimated on the basis of historical trends. In the present case, traffic growth rates are estimated using elasticity

25 Traffic Forecasting- Theory & Concepts method as per IRC-108-1996. Demand changes are usually because of shifts in the pattern of economic activities in the surrounding regions. Hence, future traffic estimation necessitates a preview, however imprecise, of the probable pattern of future growth of the economy. Elasticity in the present context is defined as the ratio of percentage change in traffic to the percentage change in socio-economic parameters. It is the concept of developing a regression equation to express dependent variable in terms of one or more independent variable. The preferred dependent variable would have been past traffic on the Project Road. However, due to inconsistencies in past traffic data, a number of registered motor vehicles is taken as dependent variable. The independent variables are socioeconomic parameters. The choice of an independent variable depends on vehicle type under consideration. It is logical to relate growth in cars and two-wheelers with Net State Domestic Product (NSDP) and per capita income; buses with NSDP, PCI and population; and commercial vehicle growth with NSDP, Industrial and agriculture output, etc.

26 Chapter ± 3 TRAFFIC FORECASTING - METHODOLOGY

3.1 Methodology In any project, methodology defines the process and type of project work. The methodology for predicting the traffic growth rates and future traffic is as shown below. Stage 1 Data Collection x Traffic Volume Count x Origin and Destination Studies x Collection of Fuel Sales on the Project Corridor

Stage 2 Data Analysis x Estimation of Average Daily Traffic and Average Annual Daily Traffic x Analysis of Origin and Destination Data x Desire line Diagram and Project Influence Area

Stage 3

Estimation of Traffic Growth Rate by Econometric Method (IRC-108-1990) x A collection of Secondary data on Vehicle Registration, Population, Per Capita Income and Net State domestic Produce of all the states influencing project corridor. x Estimating Elasticity Values and Traffic Growth on the Project Corridor x Traffic Forecasting and Capacity Analysis

The surveys, data collection, and analysis carried out during the course of the project work is shown in the Fig. 3.1

27 Traffic Forecasting - Methodology

Setting out Objectives

Primary Data Collection

Traffic Volume Count Origin and and Fuel Sales on the Destination Studies Project Stretch

Traffic Data Analysis

Estimation of Average Analysis of O-D Surveys Daily Traffic and Annual and determining Influence Average Daily Traffic Factors

Collection of Secondary Data on Vehicle Registration, Economic Indicators and Population

Estimating Elasticity Values and Traffic Growth on the Project Corridor

Traffic Forecasting and Capacity Analysis Flowchart Showing Methodology of Project

Figure 3.1: Flowchart showing the Project Methodology

28 Traffic Forecasting - Methodology

3.2 Reconnaissance Survey

The primary tasks of the reconnaissance survey included: x Topographical features of the area x Typical physical features along the existing alignment within and outside Right of Way (RoW) i.e. Land use pattern. x Traffic pattern and preliminary identification of traffic homogenous links and x Selection of Suitable Location for carrying out traffic surveys. x Inventory of major aspects including land width, terrain, pavement type, carriageway type

All the traffic surveys were undertaken on the basis of information derived from the reconnaissance survey.

3.3 Traffic Surveys The objective of the traffic study is to provide basic input for the following components of the Study: x Capacity assessment and lane recommendation based on demand forecasting for the next 20 years. x The pattern of commodity movement. x Identification of zone of influence of the project stretch as per O-D survey.

3.3.1 Classified Traffic Volume Count Survey Directional classified traffic volume count surveys were carried out for continuous 7 days x 24 hours at appropriate locations. The vehicle classification system is as per IRC: 64 - 1990. The primary data collected have been analyzed to derive the hourly and daily variations and presented in Chapter-3 in tabular form along with a pie-chart showing ADT composition pattern, classified hourly average traffic, and a graphical representation of the average hourly variation of the fast and slow moving vehicles.

29 Traffic Forecasting - Methodology

3.3.2 Origin ± Destination & Commodity Movement Surveys O-D and Commodity Surveys were carried out continuously for one normal day (24 Hrs) in both directions at the two locations. As per IRC: 102- 1988 it is preferable to conduct the O-D and Commodity Survey for all Tollable vehicles. To facilitate this, the locations of the survey stations were chosen at approximate mid-block locations.

The data were analyzed and the locations of the Origin & Destination zones were selected suitably. The trip matrices were worked out for each vehicle category and desire line is represented for a passenger vehicle, commercial vehicle, and combined vehicles. The trip characteristic for passenger and commercial vehicles is represented in graphical format.

3.4 Traffic Forecast Traffic forecast was made for the period of 20 years for all categories of vehicles, based on the O-D survey and adopted growth factors. From the O-D survey, it could be seen what percentage of the vehicles plying the project stretch has its origin and destination beyond the stretch. This established whether there would be any local impact or not, and the zone of influence. The traffic growth factor presented for the present year and every 5 years interval up to 20th year is based on: x Past traffic growth rate on the project stretch x Vehicle registration figures as per zone of influence for at least the last 5 years x Influence of the road for both transport and cargo freight x Population growth rate as per zone of influence x Change of annual vehicle utilization (if earlier information is available) x Regional and national economic growth. Developmental activities in and around the project influence area. Elasticity of Road Transport Demand in relation to GNP/GDP x Industrial growth.

30 Chapter - 4 TRAFFIC VOLUME DATA COLLECTION AND ANALYSIS

4.1 General Traffic is one of the most important components of a road project feasibility study. The study of traffic & travel characteristics is conducted to assess the nature and magnitude of traffic problems on the project road. A correct assessment of the existing traffic condition along with past traffic flow trends forms a basis for further analysis of estimation of traffic flow for the horizon years. Keeping these things in view, utmost care has been taken to study the traffic and travel characteristics on the project road. This would enable to plan and design the project road to meet future traffic demands and ensure safe and efficient movement of traffic for horizon years.

4.2 Objective of Traffic Studies The traffic characteristics on the project road for the base year are essential for formulating improvement programs and in estimating the economic/commercial viability of the project.

1. Traffic estimation in terms of volume and travel pattern on various sections. 2. Identification of influence region of the project road based on O-D Survey. 3. Traffic growth rate estimation for traffic forecasting. 4. Capacity assessment based on traffic forecasting for next 30 years.

4.3 Data collection and Surveys Traffic data is one of the important inputs required for a highways project. This chapter deals with various traffic studies carried out and the analysis of the data obtained from these studies. The following traffic studies have been carried out for the project work. Primary Data Collection: 1. Reconnaissance Survey and road inventory study 2. Classified traffic volume counts for 7 days and 24 hours duration. 3. Origin and Destination Studies for 24 hours. 4. Data on fuel sales along the project corridor.

31 Traffic Volume Data Collection and Analysis

Secondary Data Collection:

In addition to these primary surveys, secondary information collected that include: 1. Socio-Economic data of previous years. 2. Information on relevant parameters from Statistical Handbooks.

4.4 Reconnaissance and Road Inventory Study A reconnaissance survey was carried out mainly to divide the project corridor into homogenous sections and to select the location for traffic volume count and O-D studies. The Fig 4.1 shows the starting and ending of the project stretch and the details of road inventory are as shown in Table 4.1.

Figure 4.1: Project stretch

Table 4.1: Road inventory details

SL No Particulars Parameters Remarks 1 Start km 129+249 Start of Hubli bypass (NH 63) 2 End km 267+000 Near Hitnal Junction 3 Total Length 137.75 km x Dharwad (km 129+249 to 177+000) 4 Districts 3 x Gadag (km 177+000 to km 223+400) x Koppal (km 223+400 to km 268+700)

5 Terrain Plain 100% Plain 2 lane (7.0m) with 6 Carriageway Except four at Hubli / Gadag Built-up areas Earthen Shoulder 7 Shoulder Earthen Shoulder (1.5m) Varies from 1.0 to 2.0 meters x Predominantly Agricultural (60%), 8 Land Use x Built-up areas (20%) Near the Cities of Hubli and Gadag x Industrial area 5% near Hospet

32 Traffic Volume Data Collection and Analysis

4.5 Selection of the traffic Survey locations Reconnaissance survey was conducted on the Project Road for selection of locations for various traffic surveys. The first location has been selected near Nalavadi village- Chainage- 159.500 Km, which is between Hubli and Gadag. The second survey location has been selected near Hallikeri village- Chainage-221.400 between Gadag and Koppal as seen in Table -4.2.

Table 4.2: Location of Traffic Volume Surveys

Sl. No. Chainage Location Survey Dates 1 159.500 Near Nalvadi village (Dharwad District) 04/02/2013 to 11/02/2013 2 221.400 Near Hallikeri village (Gadag District) 04/02/2013 to 11/02/2013

4.6 Classified Volume Count Surveys

4.6.1 Methodology The classified traffic volume counts were conducted manually by counting vehicles on both directions. The vehicles were broadly classified into various categories i.e., car/jeep/van, buses, trucks, multi-axle vehicles (MAV¶V  light commercial vehicles (LCVs), tractors, two-wheelers, auto rickshaws and slow moving vehicles (SMVs) which include cycles and carts. Buses were classified into government, private and mini buses. In order to assess the Average Daily Traffic at any section of road, classified traffic counts are carried out for a continuous period of 7 days and 24 hours to average any variation in the short term.

The counts were carried out by trained enumerators and recorded in the specified formats. The formats are included in the annexure. All the traffic surveys were carried out as per guidelines given in IRC: SP 19-2001, IRC 102-1998, IRC 09-1972. The 7 days CVC traffic survey started from 4-Feb-2013 at 8.00 A.M. (Morning) and ended on 11-Feb- 2013 at 8:00 A.M, at both the locations continuous and direction wise. The volume counts were recorded at 15 minutes interval. All results have been presented in tabular and graphical forms. The data collected was computerized using MS-Excel software. [26], [27]

33 Traffic Volume Data Collection and Analysis

4.6.2 Traffic Survey Planning and Selection of Traffic Survey Location The entire process of selection of traffic survey locations for project road is described as under. 1. The entire road network consisting of NH, SH, and MDR etc. in the vicinity of the project road within a radius of 50km is thoroughly studied. 2. The kind and pattern of traffic and diversions on the road network is visually studied. Locations are selected so as to capture representative traffic volume on the homogeneous sections and not to capture local traffic nearby the urban settlements. With a view to capture section wise traffic flow characteristics, the total stretch can be segmented into a number of homogeneous sections, based upon the major intersections that act as main collectors or distributors (diversion) of traffic along the project road. The locations for the various surveys are so selected that all vehicles can be viewed and interpreted easily without endangering the safety of enumerators and drivers.

4.6.3 Classification of Traffic: The vehicle classification system adopted for the study is as given in IRC: 64-1990, ³*XLGHOLQHVIRU&DSDFLW\RI5RDGVLQ5XUDO$UHDV´ as shown in Table 4.3. [28] Table 4.3: Vehicle Classification System Adopted Vehicle Type Two Wheeler Three Wheeler/Auto Rickshaw Passenger Car Mini Bus Buses Standard Bus Fast Moving LCV less than 5 tons Vehicles 2- Axle Truck greater than 5 ton 3- Axle Multi Axle Without Trailer Agricultural Tractor With Trailer Bicycle Cycle Rickshaw Slow Moving Animal Drawn Vehicles Vehicles Hand Cart Others

34 Traffic Volume Data Collection and Analysis

4.6.4 PCU Values Adopted In order to convert the recorded vehicles into a common scale, Passenger Car Units 3&8¶V KDVEHHQDGRSWHG$VDOPRVWWKHSURMHFWFRUULGRUIDOOVZLWKLQWKHUXUDODUHD the PCUs applicable for rural situation have been adopted. PCU values are adopted for the present study as per IRC: 64-1990 recommendations for rural roads are as given below in table 4.4. Table 4.4: Type of Vehicle and its PCU Equivalency Factors Type of Vehicle PCU Equivalent Two Wheelers 0.5 Car/Jeep/Auto Rickshaw/ Mini LCV 1.0 LCV/Minibus/Tractor without trailer 1.5 Bus/2-Axle Truck/ Three-Axle truck 3.0 Multi Axle Truck/Tractor with Trailer 4.5 Cycle 0.5 Cycle Rickshaw 2.0 Hand Cart 3.0 Bullock Cart 8.0 Horse Drawn Vehicles 4.0

4.7 Classified Volume Count Survey - Findings The summary of the Volume Count Surveys for the locations is given in Table 3.4. The volume count details are given in Annexure. It has been observed that the traffic LVYDU\LQJIURP3&8¶VWR3&8¶V%RWKFRPPHUFLDODQGSDVVHQJHUYHKLFOH traffic is observed uniform on the project stretch. Traffic is high in the initial section due to the close influence between Hubli and Gadag, and the last section due to industrial activities. The summary of all data collected from traffic volume survey for the 2 locations on the Project Road is presented in Table 4.5 and Fig.4.2. Average Daily Traffic (ADT) and percentage share of vehicles for the month of February 2013 are summarized in Table 4.6.

35 Traffic Volume Data Collection and Analysis

Table 4.5: Details of Traffic Volume Count Survey Location Nalavadi -159.500 Hallikeri-221.400 ADT ADT ADT ADT Vehicle Category Vehicles (PCU) Vehicles (PCU) Trip Van 40 40 47 47 Taxi 407 408 221 221 Car/Jeep/Van 1488 1488 744 744 Mini Bus 24 36 18 28.5 School Bus 12 36 6 21 Govt. Bus 509 1527 207 621 Pvt.Bus 124 372 44 132 Tollable Mini LCV 366 366 212 212 Traffic LCV 4Tyre 82 123 51 76.5 LCV 6Tyre 199 298.5 160 240 2 Axle Truck 397 1191 284 852 3 Axle Truck 489 1467 438 1314 MAV (4-6 Axle) 140 630 202 909 MAV (7 Axle & More) 5 22.5 2 9 HCM / EME 3 13.5 5 22.5 Two Wheeler 898 449 329 164.5 Auto Rickshaw 40 40 36 36 Non- Tractor 40 60 12 18 Tollable Tractor + Trailer 54 243 71 315 Traffic Cycle 53 27 10 5 Cycle Rick. 1 2 1 2 Animal Drawn 4 24 3 18 Car 25 25 7 7 Toll Bus 13 39 2 6 Exempted LCV 1 1.5 1 1.5 Vehicles Truck 1 3 1 3 Tollable Traffic 4285 8019 2641 5450 Totals Non Tollable Traffic 1130 914 473 576 Total All Traffic 5415 8932 3114 6026

36 Traffic Volume Data Collection and Analysis

Table 4.6: Percentage Share of Vehicular Traffic on the Project Stretch Survey Location Nalavadi -159.500 Hallikeri-221.400

ADT ADT % Share ADT ADT % Share Vehicle Category Vehicles (PCU) of Traffic Vehicles (PCU) of Traffic Two Wheeler 898 449 16.6% 329 164.5 10.6% Auto Rickshaw 40 40 0.7% 36 36 1.2% Car/Jeep/Van/Taxi 1960 1961 36.2% 1019 1019 32.7% Mini Bus 24 36 0.4% 18 28.5 0.6% Buses 658 1974 12.2% 259780 8.3% Mini LCV 366 366 6.8% 212 212 6.8% LCV (4&6 Tire) 282 423 5.2% 212 318 6.8% Truck (2 and 3 Axle) 887 2661 16.4% 723 2169 23.2% Multi Axle Trucks 145 652.5 2.7% 204 918 6.6% HCM / EME 3 13.5 0.1% 5 22.5 0.2% Tractor 40 60 0.7% 1218 0.4% Tractor + Trailer 54 243 1.0% 71 315 2.3% Cycles 53 27 1.0% 105 0.3% Cycle Rickshaw 1 2 0.0% 1 2 0.0% Animal Drawn Carts 4 24 0.1% 3 18 0.1% Total 5415 8932 100.0% 3114 6026 100.0%

Figure 4.2: Graph showing Average Daily Traffic at Nalavadi and Hallikeri

37 Traffic Volume Data Collection and Analysis

4.8 Location-wise analysis of Traffic Volume Count The following observations were made from the analysis of survey data location wise along the project stretch.

4.8.1 Traffic Volume analysis at Nalavadi (159.500) x This is the first location for volume count survey on the project stretch of NH-63, chainage 159.500km. x 7KH$YHUDJH'DLO\7UDIILF $'7 REVHUYHGDWWKLVORFDWLRQLV3&8¶V (5414 vehicles). x Passenger vehicles constituted 66.1% while slow moving vehicles constituted 1.1 % and commercial traffic shared 32.8% of the total traffic. 36.2% of the total traffic on the project stretch is Car / Jeep/Van & Taxi and 16.6% is two-wheeler, whereas buses are observed to be 12.6% of total traffic as seen in Fig.4.3 x Peak traffic is observed in the evening (05.00pm ± 06.00 pm) and is 554 PCU's and the peak hour factor is 6.2% (Fig. 4.4). x 7KH GDLO\ YDULDWLRQ RI WUDIILF UDQJHV IURP D PD[LPXP RI  3&8¶V WR D PLQLPXPRI3&8¶V (Fig. 4.5) x Table 3.7 shows the directional distribution of traffic volume at Nalavadi and can be seen that the traffic distribution is almost homogenous with 49% of vehicles moving from Hubli to Gadag and 51% of vehicles from Gadag to Hubli. (Fig.4.6)

38 Traffic Volume Data Collection and Analysis

Figure 4.3: Showing Traffic Compositions at Nalavadi

Figure 4.4: Hourly variation of Traffic near Nalavadi

39 Traffic Volume Data Collection and Analysis

Figure 4.5: Daily variation of Traffic near Nalvadi

Table 4.7: Directional Distribution of Traffic near Nalavadi Gadag to Hubli Total Day Hubli to Gadag Vehicles in PCU Vehicles in PCU Vehicles in PCU Monday 5346 5008 10354 Tuesday 5244 5032 10276 Wednesday 3613 4403 8016 Thursday 4298 4422 8720 Friday 4099 4382 8481 Saturday 4214 4152 8366 Sunday 3995 4250 8245 Average 4401 4521 8923 Percentage 49.3% 50.7% 100.0%

Figure 4.6: Directional Distribution of Traffic near Nalavadi

40 Traffic Volume Data Collection and Analysis

4.8.2 Traffic Volume analysis at Hallikeri (221.400kms) x This is the second location for volume count survey on the project stretch of NH-63, chainage 221.400km. x 7KH$YHUDJH'DLO\7UDIILF $'7 REVHUYHGDWWKLVORFDWLRQLV3&8¶V (3112 vehicles). x Passenger vehicles constituted 53.4% while slow moving vehicles constituted 0.4 % and commercial traffic shared 46.2 % of the total traffic. 32.7% of the total traffic on the project stretch is Car / Jeep/Van & Taxi and 10.6% is two-wheeler, whereas buses are observed to be 8.6% of total traffic as represented in the Fig.4.7 x Peak traffic is observed in the evening (07.00pm ± 08.00 pm) and is 322 PCU's and the peak hour factor is 5.3% as seen in Fig.4.8. x 7KHGDLO\YDULDWLRQRIWUDIILFUDQJHVIURPDPD[LPXPRI3&8¶VWRa minimum RI3&8¶V Fig.4.9) x Table 3.8 shows the directional distribution of traffic volume at Nalavadi and can be seen that the traffic distribution is almost uniform with 52% of vehicles moving from Gadag to Koppal and 48% of vehicles from Koppal to Gadag. (Fig.4.10)

Figure 4.7: Traffic Compositions at Hallikeri

41 Traffic Volume Data Collection and Analysis

Figure 4.8: Hourly variation of Traffic near Nalavadi

Figure 4.9: Daily Variation of Traffic at Hallikeri

42 Traffic Volume Data Collection and Analysis

Figure 4.10: Directional Distribution of Traffic near Nalavadi

Table 4. 8: Directional Distribution of Traffic near Nalavadi Gadag to Koppal Koppal to Gadag Total Day Vehicles in PCU Vehicles in PCU Vehicles in PCU Monday 3626 2844 6470 Tuesday 3608 3332 6940 Wednesday 3085 3046 6131 Thursday 2778 2772 5550 Friday 2749 2774 5523 Saturday 3111 2802 5913 Sunday 2824 2807 5631 Average 3112 2911 6023 Percentage 51.7% 48.3% 100.0% 4.9 Seasonal variation of Traffic Volume Traffic levels along a study stretch vary during different periods of time i.e., in different months/seasons. Information on this aspect is necessary to estimate the AADT(Average Annual Daily Traffic). This is best understood by studying monthly historical traffic volumes on the project corridor. This, however, is not available for the study stretch. In the absence of this direct information, it is customary to consider the monthly sales of petrol and diesel, at the fuel stations along the project corridor or on the road stretches in its environment. This information is presented in Table4.9 and depicted in Fig.4.11. The factors for passenger vehicles are based on petrol sales and WKDWRIJRRGVYHKLFOHV 7UXFNV/&9¶V DQGEXVHs on diesel sales [29].

43 Traffic Volume Data Collection and Analysis

Table 4.9: Monthly consumption of Fuel on the Project Corridor

Diesel Petrol Consumption Consumption Both Month No of Days (liters) (liters) (liters) April 30 139634 11892 151526 May 31 140014 12921 152935 June 30 128658 12005 140663 July 31 113930 12143 126073 August 31 103085 11280 114364 September 30 105970 11242 117212 October 31 116896 13560 130456 November 30 129238 13252 142490 December 31 138691 13763 152454 January 31 134339 13898 148237 February 28 133527 13244 146771 March 31 145100 14927 160028 Average 127423 12844140267.5

Table 4.10: Daily Consumption of Fuel on the Project Corridor

Daily Diesel Daily Petrol Consumption Consumption Both Month (liters) (liters) (liters) April 4654 396 5051 May 4517 417 4933 June 4289 400 4689 July 3675 392 4067 August 3325 364 3689 September 3532 375 3907 October 3771 437 4208 November 4308 442 4750 December 4474 444 4918 January 4334 448 4782 February 4769 473 5242 March 4681 482 5162 Average 4194 422 4616

4.9.1 Seasonal Correction Factor: Seasonal Correction Factor (SCF) is obtained by dividing the average daily fuel consumption with the respective month on which the traffic volume count was carried out. Table 4.11 shows the seasonal correction factors for all the months in a year [30].

44 Traffic Volume Data Collection and Analysis

Table 4.11: Seasonal Correction Factors

Month Diesel Consumption Petrol Consumption Both April 0.90 1.07 0.91 May 0.93 1.01 0.94 June 0.98 1.06 0.98 July 1.14 1.08 1.14 August 1.26 1.16 1.25 September 1.19 1.13 1.18 October 1.11 0.97 1.10 November 0.97 0.96 0.97 December 0.94 0.95 0.94 January 0.97 0.94 0.97 February 0.88 0.89 0.88 March 0.90 0.88 0.89

Figure 4.11: Average monthly fuel sales on the Project Corridor

45 Traffic Volume Data Collection and Analysis

4.10 Annual Average Daily Traffic (AADT) Average Daily Traffic (ADT) from the volume counts were accounted for the monthly variations (within one year) to obtain the Annual Average Daily Traffic (AADT) which is represented in Table 4.12 below.

Table 4.12: ADT and AADT details of the project corridor

Survey Location Nalavadi -159.500 Hallikeri-221.400

Vehicle Category ADT (PCU) AADT ADT (PCU) AADT Two Wheeler 449 395 164.5 147 Auto Rickshaw 40 36 36 32 Car/Jeep/Van/Taxi 1961 1727 1019 897 Mini Bus 36 32 28.5 25 Buses 1974 1739 780 686 Mini LCV 366 322 212 186 LCV (4&6 Tire) 423 372 318 279 Truck (Two axle and Three Axle) 2661 2341 2169 1908 Multi Axle Trucks (4 axles and more) 652.5 574 918 807 HCM / EME 13.5 12 22.5 20 Tractor 60 54 18 16 Tractor + Trailer 243 214 315 277 Cycles 27 27 5 4 Cycle Rickshaw 2 2 2 2 Animal Drawn Carts 24 24 18 18 Total 8932 7869 6026 5305

The consumption of Petrol / Diesel is directly proportional to the traffic intensity. The analysis of the available information as explained above indicates that the average of a seasonal factor for passenger vehicles is 0.89 and that of goods vehicles is 0.88. An average of petrol and diesel sales data that works out to be 0.885 had been taken for cars/taxi and trip van since petrol and diesel cars are observed on this stretch. Thus multiplying the seasonal correction factors with ADT gives AADT for the project corridor.

46 Chapter - 5 ORIGIN - DESTINATION STUDIES AND ANALYSIS

5.1 General The Origin ± Destination (O-D) studies are carried out to study the travel pattern of goods and passenger traffic along the study corridor. O-D survey provides the input for estimating the traffic in respect of: x Traffic influence region for estimation of traffic growth rate; x Percentage of divertible traffic; x Commodity movement pattern; x Trip patterns; x Estimation of Tollable traffic.

5.2 Zoning The defined study area is sub- divided into smaller areas called zones. The purpose of such a sub-division is to facilitate the spatial quantification of land use and economic factors, which influence travel patterns. Subdivision into zones further helps in geographically associating the origin and destination of travel. Zones within the study area are called internal zones and those outside the study area are called external study zones. In large projects, it is more convenient to divide the study area into sub-zones depending on land use. The following points should be considered while deciding the area into zones: x Land use is the important factor in establishing the zones for a transportation survey. x The zones should have a homogeneous land use so as to reflect accurately the associated trip- making behavior. x Anticipated changes in the land use should be considered when sub-dividing the study area into zones. x The zones should not be too large to cause considerable errors in data. x The zones should preferably have the geometry for easily determining the centroid that represents the origin and destination travel [31, 32].

47 Origin ± Destination Studies and analysis

To study the travel pattern the project corridor influence area is divided into 56 Zones. Zoning is done in such a way that the characteristics of inter-zonal as well as intrazonal trips could be clearly analyzed and their influence is assessed on the project corridor.

The study and other influencing areas were divided into 56 zones based on the study need focusing more on the through traffic. The project stretch is divided into 14 internal zones; the adjoining area of the project stretch is divided into 23 zones, rest of Karnataka State into 10 zones as shown in Fig. 5.1. The details of zone list and its distance matrix are provided in the Annexure.

Figure 5. 1 Zone Map

5.3 Sample Size The sample size for Roadside Interview Survey is defined as the number of vehicles sampled at the survey station wherein the drivers successfully completed the interview.

Sample Size = B/$««««««««««««««««««««««««««  Where, A= number of vehicle in the specified class counted B= number of vehicles of the class interviewed

48 Origin ± Destination Studies and analysis

It is impracticable to stop and interview all the vehicles. Sampling is, therefore necessary. As far as possible the survey should cover a maximum percentage of traffic, and it is preferable to cover the entire traffic giving one hundred sample sizes. When this is not possible the survey should cover a minimum percentage of traffic as given below [33].

During peak period: 15% - 25% of volume of traffic

During normal period: 30% - 50% of volume of traffic

In addition to this sample survey, traffic count should be conducted simultaneously during the survey period. This is mainly required to expand the sample to the total population. Traffic count data by vehicle type should also be collected for every 15 minutes interval. Tables 5.1 and 5.2 provide the details of the sample size collected during the survey on 06/02/2013 (Wednesday) at the two locations Nalavadi and Hallikeri respectively.

Table 5. 1: Sample Size Details at Nalavadi

Vehicle Category Traffic Volume O-D Sample Size Percentage Passenger Cars 2023 727 48% Buses 681 334 35% LCV 432 204 47% Tollable Traffic 2 Axle Truck 249 231 58% 3 Axle Truck 432 173 35% Multi Axle Vehicles, 129 50 23% Special Vehicles

Table 5. 2: Sample Size Details at Hallikeri

Vehicle Category Traffic Volume O-D Sample Size Percentage Passenger Cars 1021 380 37% Buses 254 109 43% LCV 468 186 40% Tollable Traffic 2 Axle Truck 293 96 33% 3 Axle Truck 471 191 41% Multi Axle Vehicles, 195 95 49% Special Vehicles 5.4 Influence Factors The O-D results provide a clear indication of the regions, which contribute to the traffic on a road. The number of trips originating from and destined to any zone represents the

49 Origin ± Destination Studies and analysis influence of that zone in traffic generation. The sum of trip originating (production) from and destined (attraction) to any zone divided by twice the total number of the trip in percentage terms gives the influence factors of that zone.

5.4.1 Determination of Influence Factors The O-D results provide a clear indication of the regions, which contribute to the traffic on roads. The number of trips originating from and destined to any zone represents the influence of that zone in traffic generation.

The sum of trips originating (production) from and destined (attraction) to any zone divided by twice the total number of trips in percentage gives the influence factors of that zone for a particular vehicle type [34].

™2™' Influence Factor = ------;««««««««  ;™2'DOO Where,

™2 7RWDOWULSVRULJLQDWLQJIURP]RQHi;

™' 7RWDOWULSVGHVWLQHGWR]RQHi;

™2'DOO 7RWDOWULSVIURPDOO]RQHV

5.5 Road Side Interview Method (RSI) To capture the productions and attractions of passenger and goods movement, from the respective zones, O-D survey was carried. Roadside Interview method, as detailed in IRC: 102-1988, IRC SP: 19-2001, was used for O-D survey. The survey was carried out for passenger cars, buses and goods vehicles for 24 hours (in both directions) on a representative volume count day, the vehicles were stopped for interview and trip data was collected at the volume count locations by trained enumerators. Sampling was carried out to ensure that sufficient sample of each vehicle category is captured for interview. Appropriate coding is adopted for zones and type of vehicle/commodity being transported. From the O-D survey, travel characteristics like vehicle type, origin and destination, trip purpose and length of trip by mode type are captured. For goods vehicles, the survey elicited characteristics like origin and destination, commodity type, trip

50 Origin ± Destination Studies and analysis frequency and length of the trip. The format for the RSI survey is given in Annexure. A reasonable sample size (about 25%-30%) of vehicles was collected. Travel patterns for were established on the basis of these surveys.

5.5.1 Analysis of O-D and Commodity Movement Survey For analysis of O-D data appropriate zoning system as indicated above was used. The selected zoning system may be further modified and improved if required. Proper codes for commodity and zoning were used. Following analysis was carried out:

i. Trip Purpose ii. Trip Frequency iii. Trip Length iv. State-wise analysis of passenger trips v. State-wise analysis of freight trips vi. Commodity analysis showing distribution of various commodities

5.6 O-D Analysis at Nalavadi The OD survey has been carried at km: 159.500 near to Nalwadi:

5.6.1 Trip Characteristics - Passenger Vehicles (a) Trip Purpose: As seen from Fig. 5.2 The trip characteristics of passenger vehicles reveal that thirty-nine (39%) of the trips were work oriented trips whereas nearly twenty percent (20%) of the trips were found to be business oriented trips and others shared twelve percent (11%). Tourism and social recreation share was 5.3% and 12.6% respectively due to the presence of Hampi and Tunga Bhadra reservoir near Hospet and also other tourist attraction in North Karnataka. Table 5.3 gives the trip purpose break up for passenger vehicles at Nalvadi.

51 Origin ± Destination Studies and analysis

Table 5. 3 Breakup of Trip purpose for Passenger Cars at Nalavadi

Trip Purpose %Car %Taxi %Trip van Average 1. Work 39.5% 33.0% 43.8% 38.7% 2. Business 23.4% 25.8% 9.4% 19.5% 3. School / College/Education 1.7% 0.0% 15.6% 5.8% 4. Marriage / Functions / Social 12.0% 16.5% 9.4% 12.6% 5. Tourism 7.9% 10.3% 0.0% 6.1% 6. Hospital / Health 4.5% 2.1% 9.4% 5.3% 7. Other 11.0% 12.4% 12.5% 12.0% Total 100.0% 100.0% 100.0% 100.0%

Figure 5. 2: Break up of Trip Purpose for passenger cars at Nalavadi

52 Origin ± Destination Studies and analysis

(b) Trip Distance of Passenger Vehicles: It can be seen from the below Table 5.4 that the trip distance for passenger cars is maximum in the range of 50km ± 100 km and 101 km ± 200 km. This is the characteristic feature of a National Highway. Fig.5.3 represents the breakup of trip distances for passenger cars.

Table 5. 4: Trip Distance break up of Passenger cars at Nalavadi

Average Trip Trip Distance in % of Cars % of Taxi % of Trip Vans Distance km 0-20 km 0.2% 0.0% 0.0% 0.1% 21-50 km 7.4% 5.6% 14.3% 9.1% 51-100 km 56.9% 41.7% 85.7% 61.4% 101-200 km 22.9% 35.2% 0.0% 19.4% 201-300 km 8.4% 12.0% 0.0% 6.8% 301-400 km 2.2% 3.7% 0.0% 2.0% 401-500 km 0.8% 0.0% 0.0% 0.3% greater than 500 km 1.3% 1.9% 0.0% 1.1%

Figure 5.3: Breakup of Trip Distance for passenger cars at Nalavadi

(c) Trip Frequency of Passenger Vehicles: The trip frequency of Vehicles represented in Table 5.5 provides details of how often vehicles use a particular road or a road network and whether the trip made is a single one- way trip or a return journey. Fig.5.4 shows the percentage trip frequency distribution of vehicles in the passenger car category at Nalavadi.

53 Origin ± Destination Studies and analysis

Table 5. 5: Percentage Trip Frequency details of Cars/Taxi/Trip Vans Trip Frequency ( Single trip) Car Taxi Trip Van 1.Daily Single Trip 8.2% 9.3% 0.0% 2.Alternate Days Single 0.2% 0.0% 0.0% 3.Weekly Single 24.1% 23.7% 18.8% 4.Monthly Twice Single 7.0% 7.2% 0.0% 5.Monthly Single 12.4% 6.2% 6.3% 6.Occationally Single 5.9% 4.1% 3.1%

Trip Frequency (Return within 24 Car Taxi Trip Van hrs) 7.Daily Trip 19.4% 22.7% 37.5% 8.Alternate Days 0.3% 1.0% 0.0% 9.Weekly 15.2% 20.6% 25.0% 10.Monthly Twice 1.3% 1.0% 3.1% 11.Monthly 5.2% 4.1% 6.3% 12.Occationally 0.8% 0.0% 0.0%

Figure 5.4: Percentage Trip Frequency details of Cars/Taxi/Trip Vans

54 Origin ± Destination Studies and analysis

(d) Trip Influence of Passenger Vehicles Trip influence is an important parameter of O-D study; it is widely used in the calculation of traffic growth rates. The trip influence of Passenger vehicles is shown in Table 5.6. It is observed that the Karnataka state has the maximum influence on the project corridor. Table 5. 6: Trip Influence of Passenger Cars at Nalavadi State Cars Taxi Trip Van Karnataka 98.2% 96.9% 100.0% Andhra Pradesh 0.4% 0.0% 0.0% Maharashtra 0.4% 1.0% 0.0% Goa 1.0% 2.1% 0.0% Kerala 0.0% 0.0% 0.0% Tamil Nadu 0.0% 0.0% 0.0% Rest of India 0.0% 0.0% 0.0%

5.6.2 Bus O-D Characteristics (a) Trip Distance The Trip Distance characteristics of buses at Nalavadi are shown in Table 5.7 and represented graphically as seen in Fig.5.5. It has been observed that the Trip Length distribution of Buses seen at Nalavadi is maximum in the range 51km-100km.

Table 5. 7: Percentage Trip Distance distribution of Buses at Nalavadi Trip % of Govt % of Private % of School % of Mini Distance Average Bus Buses Buses Buses LQNP¶V 0-20 km 0.0% 0.0% 14.3% 0.0% 3.6% 21-50 km 12.9% 0.0% 42.9% 0.0% 14.0% 51-100 km 56.1% 93.6% 28.6% 50.0% 57.1% 101-200 km 11.5% 2.1% 14.3% 50.0% 19.5% 201-300 km 8.6% 4.3% 0.0% 0.0% 3.2% 301-400 km 4.3% 0.0% 0.0% 0.0% 1.1% 401-500 km 0.7% 0.0% 0.0% 0.0% 0.2% > 500 km 5.8% 0.0% 0.0% 0.0% 1.4%

55 Origin ± Destination Studies and analysis

Figure 5.5: Percentage Trip Distance distribution of Buses at Nalavadi

(b) State wise Trip Influence of Buses on the Project Corridor at Nalavadi The state wise influence of buses on the project corridor is as shown below from the Table 5.8. Karnataka State has the maximum influence on buses accounting for more than 95% of Buses trips origination and destination in Karnataka State itself.

Table 5. 8: Trip Influence of Buses at Nalavadi

Influence of Influence of Influence of Influence of State Govt Buses Private Buses School Buses Mini Buses Karnataka 96.9% 98.9% 100% 100% Andhra Pradesh 1.8% 0.0% 0% 0% Maharashtra 1.1% 0.0% 0% 0% Goa 0.2% 1.1% 0% 0% Kerala 0.0% 0.0% 0% 0% Tamil Nadu 0.0% 0.0% 0% 0% Rest of India 0.00% 0.00% 0% 0% Total 100.0% 100.0% 100.0% 100.0%

56 Origin ± Destination Studies and analysis

5.6.3 O-D Characteristics of Goods Vehicles at Nalavadi

(a) Commodity analysis at Nalavadi:

Analysis of data reveals that there is no single dominant commodity of goods. Food products, Building materials, Petroleum products and Parcel material movement is appreciable. The empty vehicles were found to be 36% percent (29%) of the total commercial vehicles as seen from Table 5.9. Figure 5.6 gives the graph of commodity characteristics for Commercial vehicles near Nalavadi.

Table 5. 9: Percentage Distribution of Commodities carried by Goods Vehicles 2-Axle 3-Axle Multi Axle Type of Commodity LCV Trucks Trucks Trucks 1.Perishable Products 13.7% 5.6% 4.0% 8.2% 2.Food Crops 3.9% 2.2% 2.3% 2.0% 3.Cash Crops 1.5% 1.3% 0.0% 2.0% 4.Manufactured Products 2.0% 0.9% 0.6% 0.0% 5.Consumer Products 1.0% 0.4% 1.7% 0.0% 6. Chemical Products 1.5% 2.6% 0.6% 2.0% 7. Petroleum Products 3.4% 14.7% 11.6% 8.2% 8. Textile Product 0.5% 1.3% 0.0% 0.0% 9. Building Materials 4.4% 18.6% 22.5% 18.4% 10. Parcel & Paper Products 8.3% 6.9% 6.9% 6.1% 11. Wood & Forest Products 3.4% 4.3% 5.2% 6.1% 12. Machinery & Tools 3.9% 0.4% 4.0% 4.1% 13. Metal, Mineral & Ores 1.0% 1.7% 6.4% 10.2% 14. Rubber & Plastic 1.0% 1.3% 0.0% 0.0% 15. Miscellaneous 2.5% 4.3% 4.0% 0.0% 16. Empty 48.0% 33.3% 30.1% 32.7% Total 100.0% 100.0% 100.0% 100.0%

57 Origin ± Destination Studies and analysis

Figure 5.6: Percentage of Commodity Carried by Goods Vehicles (b) Trip Distance of Goods Vehicles at Nalavadi From the Trip Distance distribution Table 5.10, it LVREVHUYHGWKDW/&9¶VDQG7ZR$[OH Trucks are maximum in the range from 51-100km whereas Three Axle and Multi-Axle Trucks are maximum in the distance range 100-200km. The percentage trip distance of Goods vehicles at Nalavadi has been graphically represented in Fig.5.7.

Table 5. 10:Percentage Trip Distance Distributions of Goods Vehicles at Nalavadi Three Multi- Trip Distance 2- Axle Oversized LCV's Axle axle Average in km Trucks Trucks trucks trucks 0-20 0.0% 0.9% 0.0% 0.0% 0.0% 0.2% 21-50 11.3% 9.5% 1.7% 0.0% 0.0% 4.5% 51-100 50.0% 46.3% 21.4% 8.2% 0.0% 25.2% 101-200 21.6% 25.5% 31.8% 36.7% 0.0% 23.1% 201-300 9.8% 7.8% 12.1% 4.1% 0.0% 6.8% 301-400 3.9% 3.0% 15.0% 18.4% 0.0% 8.1% 401-500 0.0% 0.4% 1.7% 0.0% 0.0% 0.4% > 500 3.4% 6.5% 16.2% 32.7% 100.0% 31.8%

58 Origin ± Destination Studies and analysis

Figure 5.7: Percentage Trip Distance Distributions of Goods Vehicles at Nalavadi

(c) Trip Frequency of Goods Vehicles

From the Trip Frequency Table 5.11 and figure 5.8 it is seen that the distribution of trip frequency by goods vehicles is maximum on a daily and weekly basis. Table 5. 11: Percentage Trip Frequency Distribution of Goods Vehicles at Nalavadi

Three Trip Frequency Two Axle Axle Multi Over Sized ( Single) LCV Truck Truck Axle Vehicles 1.Daily 23.0% 24.7% 16.8% 14.3% 0.0% 2.Alternate Days 4.4% 3.9% 2.3% 10.2% 0.0% 3.Weekly 12.3% 16.9% 39.9% 34.7% 100.0% 4.Monthly Twice 2.0% 0.4% 0.6% 0.0% 0.0% 5.Monthly 2.9% 3.5% 7.5% 6.1% 0.0% 6.Occasionally 0.5% 0.0% 0.6% 2.0% 0.0%

Three Multi Trip Frequency Two Axle Axle Axle Over Sized (Return within 24 hrs) LCV Truck Truck Truck Vehicles 7.Daily Trip 26.0% 26.4% 11.6% 10.2% 0.0% 8.Alternate Days 1.5% 0.4% 0.0% 0.0% 0.0% 9.Weekly 12.3% 19.5% 18.5% 16.3% 0.0% 10.Monthly Twice 0.5% 0.4% 0.0% 0.0% 0.0% 11.Monthly 2.0% 0.0% 0.6% 6.1% 0.0% 12.Occasionally 12.7% 3.9% 1.7% 0.0% 0.0%

59 Origin ± Destination Studies and analysis

Figure 5.8: Percentage Trip Frequency details of Goods Vehicles

(d) Trip Influence of Goods Vehicles at Nalavadi: The Trip Influence of Goods Vehicles is maximum in the State of Karnataka (greater than 80% influence) and is followed by Andhra Pradesh, Goa, and Maharashtra. Kerala, Tamil Nadu and Rest of India have a minute share in the Percentage trip Influence as seen in Table 5.12. Table 5. 12: Trip Influence of Goods Vehicles at Nalavadi Influence Influence Influence Influence Influence State (Over Sized (LCV) (2-Axle) (3-Axle) (Multi-Axle) Vehicle) Karnataka 96.8% 95.9% 86.7% 75.5% 50.0% Andhra Pradesh 1.2% 2.4% 6.1% 11.2% 0.0% Maharashtra 0.5% 0.4% 1.7% 4.1% 50.0% Goa 1.5% 0.9% 4.9% 8.2% 0.0% Kerala 0.0% 0.0% 0.0% 0.0% 0.0% Tamil Nadu 0.0% 0.0% 0.0% 0.0% 0.0% Rest of India 0.0% 0.4% 0.6% 1.0% 0.0%

60 Origin ± Destination Studies and analysis

5.7 O-D analysis at Hallikeri The OD survey has been carried at km: 221.400 near to Hallikeri Village:

5.7.1 Trip Characteristics - Passenger Vehicles (a) Trip Purpose The trip characteristics of passenger vehicles as seen from Table 5.13 reveal that twenty- two percent (22%) of the trips were work oriented trips whereas thirteen percent (13%) of the trips were found to be business oriented trips and others shared twenty-two percent (22%). Tourism and social recreation share was 20.1% and 5.8% respectively due to the presence of Hampi and Tunga Bhadra reservoir near Hospet and also other tourist attraction in North Karnataka. Fig.5.9 gives the percentage trip purpose break up for passenger vehicles.

Table 5. 13: Breakup of Trip Purpose for Passenger Vehicles at Hallikeri Trip Purpose % Car % Taxi % Trip van Average 1. Work 24.2% 17.2% 25.0% 22.2% 2. Business 26.4% 13.8% 0.0% 13.4% 3. School / 5.0% 1.7% 0.0% 2.3% College/Education 4. Marriage / Functions / 12.3% 5.2% 0.0% 5.8% Social 5. Tourism 17.3% 43.1% 0.0% 20.1% 6. Hospital / Health 6.6% 10.3% 25.0% 14.0% 7. Other 8.2% 8.6% 50.0% 22.3% Total 100.0% 100.0% 100.0% 100.0%

61 Origin ± Destination Studies and analysis

Figure 5.9: Breakup of Trip Purpose for Passenger Vehicles at Hallikeri

(b) Trip Distance Characteristics:

It can be seen from the below Table 5.14 that the trip distance for passenger cars at Hallikeri is maximum in the range of 101 km ± 200 km. Whereas the Trip vans are maximum in the distance range 21-50km. Table 5. 14: Breakup of Trip Distance for passenger cars at Hallikeri Trip Distance in % of Trip Average Trip % of Cars % of Taxi km Vans Distance 0-20 0.0% 0.0% 0.0% 0.0% 21-50 0.9% 0.0% 100.0% 33.6% 51-100 21.1% 18.6% 0.0% 13.2% 101-200 48.4% 55.9% 0.0% 34.8% 201-300 17.6% 18.6% 0.0% 12.1% 301-400 4.7% 1.7% 0.0% 2.1% 401-500 1.3% 1.7% 0.0% 1.0% greater than 500 6.0% 3.4% 0.0% 3.1%

62 Origin ± Destination Studies and analysis

(c) Trip Frequency of Passenger Vehicles at Hallikeri:

From the Trip Frequency Table 5.15, it is seen that the Percentage distribution of trip frequency by Passenger car is maximum weekly and occasionally on a single trip basis (no return within 24hrs). Table 5. 15: Percentage Trip Frequency Distribution of Passenger cars at Hallikeri Trip Frequency ( Single) Car Taxi Trip Van 1.Daily Single Trip 1.9% 1.7% 0.0% 2.Alternate Days Single 4.4% 3.4% 0.0% 3.Weekly Single 25.2% 22.4% 25.0% 4.Monthly Twice Single 2.5% 1.7% 0.0% 5.Monthly Single 13.8% 8.6% 0.0% 6.Occationally Single 28.9% 24.1% 25.0%

Trip Frequency (Return within 24 hrs) Car Taxi Trip Van 7.Daily Trip 9.1% 22.4% 25.0% 8.Alternate Days 2.8% 3.4% 0.0% 9.Weekly 3.5% 6.9% 0.0% 10.Monthly Twice 1.3% 0.0% 0.0% 11.Monthly 1.6% 0.0% 0.0% 12.Occationally 5.0% 5.2% 25.0%

(d) Trip Influence of Passenger Vehicles at Hallikeri:

The state wise influence of Passenger cars on the project corridor is as shown below from the Table 5.16. Karnataka State has the maximum influence on buses accounting for more than 95% of trips origination and destination in Karnataka State itself. Andhra Pradesh and Goa have a minor share in the Trip Influence. Table 5. 16: Trip Influence of Passenger Vehicles at Hallikeri

State Influence (Car) Influence(Taxi) Influence(Trip Van)

Karnataka 95.4% 97.4% 100.0% Andhra Pradesh 2.4% 1.7% 0.0% Maharashtra 0.8% 0.0% 0.0% Goa 1.4% 0.9% 0.0% Kerala 0.0% 0.0% 0.0% Tamil Nadu 0.0% 0.0% 0.0% Rest of India 0.0% 0.0% 0.0%

63 Origin ± Destination Studies and analysis

4.7.2 Bus O-D Characteristics: (a) Trip Distance: The Trip Distance characteristics of buses at Hallikeri are shown in Table 5.17 It has been observed that the Trip Length distribution of Buses seen at Hallikeri is maximum in the range 101-200km and 201-300km for Government Buses and above 400kms for Private Buses. Table 5. 17: Percentage Trip Distance Distribution of Buses at Hallikeri Trip Distance in km Govt Bus Private Bus 0-20 km 0% 0% 21-50 0% 0% 51-100 14% 0% 101-200 23% 0% 201-300 29% 0% 301-400 10% 0% 401-500 10% 33% greater than 500 14% 67%

(b) State wise Trip Influence of Buses on the Project Corridor at Hallikeri: The state wise influence of buses on the project corridor at Hallikeri is as shown below from the Table 5.18. Karnataka State has the maximum influence on buses accounting for 90% of Buses trips origination and destination in Karnataka State itself. Andhra Pradesh and Maharashtra has a share of 6.5% and 3.2% respectively. Table 5. 18: Percentage of State wise Trip Influence of Buses at Hallikeri State Influence Karnataka 89.9% Andhra Pradesh 6.5% Maharashtra 3.2% Goa 0.5% Kerala 0.0% Tamil Nadu 0.0% Rest of India 0.0%

64 Origin ± Destination Studies and analysis

5.7.3 O-D Characteristics of Goods Vehicles at Hallikeri:

(a) Commodity analysis at Hallikeri:

Analysis of data reveals that there is no single dominant commodity of goods. Food products, Building materials, Petroleum products and Parcel material movement is appreciable. The percentages of empty vehicles were found to be around 43%. Table 5.19 gives the commodity characteristics for Commercial vehicles near Hallikeri.

Table 5. 19:Percentage Distribution of Commodities carried by Goods Vehicles 2-Axle 3-Axle Multi Axle Oversized Type of Commodity LCV Truck Truck vehicle Vehicles 1.Perishable Products 7.0% 0.0% 1.6% 2.4% 0.0% 2.Food Crops 4.8% 4.2% 4.2% 5.9% 0.0% 3.Cash Crops 2.7% 1.0% 0.5% 0.0% 0.0% 4.Manufactured Products 4.3% 1.0% 1.0% 0.0% 0.0% 5.Consumer Products 4.8% 1.0% 3.1% 1.2% 0.0% 6. Chemical Products 3.8% 6.3% 7.9% 5.9% 10.0% 7. Petroleum Products 3.2% 19.8% 8.9% 1.2% 0.0% 8. Textile Product 1.1% 0.0% 1.0% 0.0% 0.0% 9. Building Materials 4.3% 13.5% 20.9% 24.7% 20.0% 10. Parcel & Paper Products 7.0% 6.3% 6.3% 1.2% 0.0% 11. Wood & Forest Products 2.7% 4.2% 6.8% 2.4% 0.0% 12. Machinery & Tools 4.8% 2.1% 4.2% 1.2% 0.0% 13. Metal, Mineral & Ores 2.7% 3.1% 12.0% 8.2% 0.0% 14. Rubber & Plastic 0.0% 2.1% 0.5% 0.0% 0.0% 15. Miscellaneous 3.2% 1.0% 0.0% 1.2% 0.0% 16. Empty 43.5% 34.4% 20.9% 44.7% 70.0% Total 100.0% 100.0% 100.0% 100.0% 100.0%

65 Origin ± Destination Studies and analysis

(b) Trip Distance of Goods Vehicles at Hallikeri: From the Trip Distance distribution table 5.20 LWLVREVHUYHGWKDW/&9¶VDQG7ZR$[OH Trucks are maximum in the range from 51-100km whereas Three Axle and Multi-Axle Trucks are maximum in the distance range 100-200km.

Table 5. 20: Percentage Trip Distance characteristics of Goods Vehicles at Hallikeri Trip Three Multi- 2- Axle Oversized Distance LCV's Axle axle Average Trucks Trucks in km trucks trucks 0-20 0.5% 1.0% 0.0% 0.0% 0.0% 0.3% 21-50 2.2% 0.0% 0.5% 1.2% 0.0% 0.8% 51-100 24.2% 31.3% 8.4% 9.4% 30.0% 20.6% 101-200 39.2% 40.6% 23.6% 21.2% 20.0% 28.9% 201-300 17.7% 8.3% 13.6% 10.6% 0.0% 10.1% 301-400 4.8% 5.2% 18.8% 16.5% 20.0% 13.1% 401-500 1.1% 3.1% 5.2% 4.7% 0.0% 2.8% >500 10.2% 10.4% 29.8% 36.5% 30.0% 23.4%

(c) Trip Frequency of Goods Vehicles at Hallikeri: From the Table 5.21, it has been observed that the Trip Frequency is maximum on a weekly single trip and monthly single trip.

Table 5. 21:Percentage Trip Frequency Distribution of Goods Vehicles at Hallikeri Trip Frequency 2- Axle 3-Axle Multi Axle Over Sized LCV (Single trip) Truck Truck Truck Vehicles 1.Daily Single Trip 9.1% 10.4% 2.1% 4.7% 10.0% 2.Alternate Days Single 10.2% 11.5% 17.3% 7.1% 10.0% 3.Weekly Single 23.7% 29.2% 32.5% 42.4% 0.0% 4.Monthly Twice Single 5.4% 10.4% 7.9% 9.4% 0.0% 5.Monthly Single 9.7% 8.3% 18.3% 25.9% 10.0% 6.Occationally Single 7.0% 10.4% 15.7% 8.2% 10.0%

Trip Frequency 2- Axle 3-Axle Multi Axle Over Sized LCV (Return within 24 hrs) Truck Truck Truck Vehicles 7.Daily Trip 23.1% 15.6% 3.1% 0.0% 0.0% 8.Alternate Days 3.2% 1.0% 1.6% 0.0% 0.0% 9.Weekly 3.2% 2.1% 1.6% 0.0% 0.0% 10.Monthly Twice 0.0% 0.0% 0.0% 1.2% 0.0% 11.Monthly 1.1% 0.0% 0.0% 1.2% 0.0% 12.Occationally 4.3% 1.0% 0.0% 0.0% 0.0%

(d) Trip Influence of Goods Vehicles at Hallikeri:

66 Origin ± Destination Studies and analysis

From the Table 5.22, Trip Influence of Goods Vehicles is maximum in the State of Karnataka (greater than 80% influence) and is followed by Andhra Pradesh, Goa, and Maharashtra. Kerala, Tamil Nadu and Rest of India have a minute share in the Percentage trip Influence. Table 5. 22: Percentage State wise Trip Influence of Goods Vehicles at Hallikeri 2- Axle 3-Axle Multi Axle Over Sized LCV State Truck Truck Truck Vehicles Karnataka 91.4% 93.2% 74.1% 76.5% 85.0% Andhra Pradesh 5.1% 5.7% 14.7% 14.7% 0.0% Maharashtra 0.5% 0.0% 0.8% 1.8% 15.0% Goa 3.0% 1.0% 8.6% 4.7% 0.0% Kerala 0.0% 0.0% 0.0% 1.2% 0.0% Tamil Nadu 0.0% 0.0% 0.8% 1.2% 0.0% Rest of India 0.0% 0.0% 1.0% 0.0% 0.0%

Summary: A comparative study of the influence factors indicated that Karnataka State, where the project stretch runs has the majority influence of ninety-two percent (92%). State of Goa, Andhra Pradesh and Maharashtra that has its border abutting Karnataka State has an influencing factor of two percent (2%), four percent (4%), and two percent (2%) respectively.

67 Chapter ± 6 TRAFFIC FORECASTING

6.1 Traffic growth rates Long term forecasting of traffic on the Project Road during the time horizon of the study is required for the design of highway and assessing the economic and financial viability of the proposed investment. To establish the future traffic growth rates, following approaches have been explored. x Past trends in Traffic growth on the Project Road. x The growth of registered motor vehicles. x Transport demand elasticity approach.

6.1.1 Growth Rate based on Past Traffic Data Past traffic data as collected from PWD is available for two locations (near Annigere and Gadag) along the project corridor. These data are available from January to July months of last 10 years. The growth rates were worked out for various categories of vehicles and conclusions were drawn. Non-Uniformity in past traffic data of PWD may be attributed to errors during collection and processing of data and policy measures of the Government and other influences etc. To illustrate this point, during recent years some of the mining activities around the project corridor have been banned by the Government which has caused a substantial decrease in the amount of Trucks and Lorries. As the past traffic data on the Project Road is not showing any definite trend, one should not be guided by past traffic data for deriving growth rates.

6.1.2 Growth Rate based on Vehicle Registration An alternative approach is to explore the registered motor vehicles growth in the influence area and assume a growth rate equal to the average growth of vehicle registration. Such an assumption may not be correct unless the area of influence is well defined and the general development pattern of influence area remains same. The Cumulative Average Annual Growth Rates (CAAGR) for various modes of traffic are estimated and presented in Table 6.2, Growth of Registered Motor Vehicles in Karnataka. It can be observed from the table, during the last 7 years, the average growth of two-

68 Traffic Forecasting

wheelers, cars and that of trucks is around 11%-12%. This high growth rate of more than 10% may not sustain in future. Therefore, other rational approaches were explored in order to derive realistic growth rates. However, this would be an alternative approach in the absence of any additional information or useable past traffic data on the Project Road. It may be emphasized here that 94-97% of passenger traffic and 84% - 89% of freight traffic is destined / originating within Karnataka. Therefore, there is a strong influence of a number of registered vehicles in Karnataka on traffic on the Project Road.

6.2 Project Influence Area The results obtained from the Origin -Destination surveys were used to identify the project influence area. The ratio of the total traffic originated/destined to a particular zone to the total traffic gives the influence factor for the particular zone. The influence factors were developed from the OD matrices and influence of each State is given in Table 6.1 A comparative study of the influence factors indicated that Karnataka State, where the project stretch runs has the majority influence of ninety-two percent (92%). State of Goa, Andhra Pradesh and Maharashtra that has its border abutting Karnataka State has an influencing factor of two percent (2%) four percent (4%) and two percent (2%) respectively. Tamil Nadu/Kerala and Rest of India have a minute share. These factors have all been accounted in the derivation of the combined growth factor and utilized for the project sections. Table 6.1: State wise Influence of Vehicles observed on the Project Road States Cars Buses Truck Average Karnataka 97.0% 93.9% 86.4% 92.4% Goa 1.3% 0.5% 3.7% 1.8% Andhra Pradesh 1.1% 3.7% 6.6% 3.8% Maharashtra 0.6% 1.9% 2.7% 1.7% Rest of India 0.0% 0.0% 0.7% 0.2% Total 100.0% 100.0% 100.0% 100.0%

69 Traffic Forecasting

6.3 Vehicle Population Growth Time series data collected from the secondary sources and used for the analysis including the periodical growth rates achieved for Karnataka State. The growth rates for various modes of Vehicles are estimated and presented in Table 6.2, Growth of Registered Motor Vehicles in Karnataka. It can be observed from the table, during the last 7 years, the average growth of two-wheelers, cars and that of trucks is around 11%-12%. This high growth rate of more than 10% may not sustain in future. Therefore, other rational approaches were explored in order to derive realistic growth rates [35-38].

Table 6.2: Summary of CAAGR % in Karnataka state Goods Two Three Year Buses Car/Jeep/Taxi Vehicles Wheelers Wheelers 2004-2005 221913 89294 841846 3957762 284078 2005-2006 276013 95627 958300 4512910 307862 2006-2007 312272 99202 1030629 3755719 359920 2007-2008 344764 110558 1209431 4230864 403910 2008-2009 366597 115016 1326395 4796587 364781 2009-2010 377495 159377 1398221 6404905 349729 2010-2011 415491 167087 1561131 7033045 440368 CAAGR in % 11.22% 11.62% 10.91% 11.10% 8.27% Source: Ministry of Road Transport & Highways, Government of India (MoRT&H)

6.4 Socio-Economic Profile of the Influence Region Socio-economic profile of the major project influence areas is considered to derive the future growth prospects of the region and correspondingly the traffic growth in the region. The past performance of the economic indicators for the project influence area, i.e.; Karnataka, Andhra Pradesh, Goa, Maharashtra and All India were collected and the Cumulative Annual Average Growth rates (CAAGR) were worked out as seen in Table 6.3 to Table 6.5 with the objective of establishing elasticity of traffic demand to the different economic indicators. The economic indicators considered for the analysis include: x Net State Domestic Produce (NSDP) x Per Capita Income [39] x Population [40]

70 Traffic Forecasting

Table 6.3: NSDP at Constant Prices (Rs. Cr) Andhra Year Karnataka Goa Maharashtra All India Pradesh 2004 148729 201303 10999 368369 2651573 2005 164031 220901 11916 426503 2902180 2006 181086 244587 13085 488079 3178664 2007 203810 272726 13655 542311 3469008 2008 218309 292258 14724 556006 3689772 2009 226278 310009 16383 634829 3987317 2010 232541 340792 17987 702832 4321491 2011 248354 363835 20216 773115 4618739 CAAGR 7.6% 8.8% 11.2% 8.3% 6.4% (2004-2011)

Table 6.4: PCI at Constant Prices (Rs.) Andhra Year Karnataka Goa Maharashtra All India Pradesh 2004 26882 25321 76968 35915 24143 2005 29295 27486 80844 40947 26015 2006 31967 30114 86257 46158 28067 2007 35574 33239 87085 50532 30332 2008 37687 35272 90386 51053 31754 2009 38646 37061 96885 57458 33843 2010 39301 40366 102844 62729 35993 2011 41545 42710 112372 68375 38005 CAAGR 6.5% 7.8% 5.6% 9.7% 6.7`% (2004-2011)

Table 6.5: Population (Thousands) Year Karnataka Andhra Pradesh Goa Maharashtra All India 2004 55327 79852 1429 102566 1089007 2005 55992 80712 1474 104160 1105535 2006 56647 81554 1517 105740 1121914 2007 57292 82375 1568 107321 1138169 2008 57927 83178 1629 108908 1154311 2009 58552 83964 1691 110485 1170307 2010 59170 84735 1749 112042 1186146 2011 59780 85491 1799 113569 1201810 CAAGR 1.1% 1.0% 3.3% 1.5% 1.4% ((2004-2011)

71 Traffic Forecasting

6.5 Estimation of traffic growth rates by Transport Demand 6.5.1Elasticity Approach

The analysis of the O-D survey data along the project corridor indicates a strong influence of Karnataka state in the traffic generation / attraction. Nearly 94%-97% percent of passenger traffic and 84%-89% percent of freight traffic in this section are either originating from or destined to various parts of Karnataka. Adopting the transport demand elasticity method, which is a proven technique worldwide and is the preferred technique in India, traffic forecast for the project road was carried out using this method. The project road traffic passing through various districts in the state of Karnataka was considered for estimating the required elasticity values. As the past traffic data on time series basis collected from PWD sources was totally unreliable and the data provided was insufficient and inaccurate no specific growth trend was observed. Therefore, an effort has been made to build time series past data on vehicle population available for Karnataka state from the available sources, as a proxy variable to the project road traffic. This past traffic data has been obtained from the available state government sources. Similar time series past data on economic and demographic variables for Karnataka such as population, state income (NSDP) at constant prices and per capita income at constant prices has been developed. The methodology involved fitting log-log regression equations to the time series data. NSDP, Population, Per Capita Income, are considered as independent variable and number of registered motor vehicles in Karnataka state is considered as dependent variables for passenger and freight vehicles. Elasticity values for registered motor vehicles with respect of NSDP, Population, and Per-capita Income and are worked out and conclusions are drawn [41-42].

6.5.2 Elasticity Values

Elasticity value is the factor by which the socio-economic growth rate is multiplied to get the growth rate of traffic. Traffic is directly linked to the economic growth such as per- capita income, population, and NSDP/GDP. Considering the time series data on category wise registered vehicles and the economic variables, by regression analysis elasticity values is estimated as shown in Table 6.6

72 Traffic Forecasting

Table 6.6: Elasticity Values Derived by Regression Analysis for Karnataka State Mode Variable Elasticity R square T-Static P-Value PCI 2.86 0.80 3.45 0.04 Two NSDP 4.00 0.91 4.52 0.05 Wheelers Population 2.86 0.80 3.45 0.04 PCI 1.48 0.97 12.27 0.00 Cars NSDP 1.80 0.93 5.17 0.04 Population 9.23 0.99 22.31 0.00 PCI 1.45 0.75 3.92 0.01 Buses NSDP 1.27 0.78 4.19 0.01 Population 9.78 0.90 6.56 0.00 Goods NSDP 1.23 0.97 11.79 0.00 PCI 0.84 0.7 3.38 0.02 Auto NSDP 5.13 0.67 3.22 0.02 Rickshaw Population 0.73 0.7 3.40 0.02

6.5.3 Recommended Elasticity values: Vehicle registration data represents all vehicles registered in the state but does not indicate an actual number of vehicles plying on the road owing to vehicles taken off the road due to lack of fitness certificate. Consequently, the elasticity values based on registration data are usually higher than those based on actual traffic. Hence, there is a need to moderate values obtained from registration data. In order to arrive at realistic future elasticities for the project road; various factors relating to vehicle technology changes, besides character of traffic and travel pattern on the project road have been considered: x High elasticity of cars being witnessed now is because of large demand facilitated by financing schemes and loans. Factors like growth of household incomes (particularly in urban areas), reduction in the prices of entry-level cars, growth of the used car market, changes in lifestyle, growing personal incomes, desire to own a vehicle facilitated by availability of loans/financing schemes on easy terms, etc. have all contributed to the rapid growth in ownership of cars. However, such trend would slow down and elasticity can be expected to decline. The elasticity obtained by using registered vehicles is actually an overestimate for the traffic moving on suburban roads/rural highways. In view of all this, combined with the travel pattern of vehicles moving on the road, elasticity value obtained by using registration data has been moderated for future years.

73 Traffic Forecasting

x Over the years, there is a change in passenger movement with more and more persons shifting towards personalized modes. Moreover, buses are usually plying on fixed pre-decided routes and thus elasticity values for buses have been considered accordingly. x With the changing freight vehicle mix in favor of LCV for short distance traffic and 3-axle/MAV for long-distance traffic, higher elasticity values for these have been considered as compared to 2-axle trucks. Considering the ongoing technical advancements in the automobile industry, some of the standard two-axle trucks would gradually be replaced by three-axle truck and MAVs, leading to a reduction in a number of trucks. This shift has already started taking place in different parts of the country.

Considering the Project Influence Area (PIA) and economic indicators of the influencing states, the projected elasticity values for various vehicle types are presented in Table 6.9 which has been used to estimate the growth rates of each vehicle type. The transport demand elasticity by vehicle type over a period of time tends to decline and approach unity or even less as seen in Table 6.7 and 6.8. As the economy and its various sectors grow, every region tends to become self-sufficient. Moreover, much of the past growth KDV EHHQ DVVRFLDWHG ZLWK WKH FRXQWU\¶V WUDQVLWLRQ IURP D ODUJHO\ UXUDO VXEVLVWHQFH economy to cash based urban economy, dominated by regional and national linkages. As the transition proceeds, its impact on transport pattern can be expected to become less dominant. Therefore, the demand for the different type of vehicles falls, over time, despite greater economic development. The same is also clear from the relationships of the economy and transport demand elasticity over time nationally and internationally [44]

Table 6.7: Suggested Elasticity Values by Dr. L.R Kadiyali (2001-2021) Elasticity Pessimistic Most Likely Optimistic Two Wheelers 1.6 1.5 1.4 Cars 1.3 1.2 1.1 Buses 0.9 0.875 0.8 Trucks 1.2 1.1 1

Table 6.8: Suggested Elasticity Values by IRC Vehicle Type 2006-2011 2011-2016 2016-2021 Cars 1.6 1.5 1.4 Buses 1.3 1.2 1.1 Goods 1.4 1.2 1.1

74 Traffic Forecasting

Table 6.9: Recommended Elasticity Values Estimated Recommended 2013- 2018- 2023 and Mode Elasticity(2004-2011) Elasticity 2018 2023 Beyond Goods 1.20 1.20 1.08 0.97 0.87 Buses 1.45 1.20 1.08 0.97 0.87 Passenger Cars 1.50 1.5 1.43 1.28 1.15 Two Wheelers 2.86 1.6 1.52 1.37 1.23 Three Wheelers 0.84 0.84 0.80 0.72 0.65

6.5.4 Future economy prospects Considering the following factors such as past performance of the economy against their set targets, recent developments in economic liberalization measures, a shift in between sectors, opportunities available in local and global markets etc., future economic growth scenario is formulated for the following time periods.

‡ 2013 ± 2017 ‡ 2018 ± 2022 ‡ 2023 and beyond

Any projection beyond 10-15 years will have little relevance, and no further projection is attempted. Hence, the entire period beyond 2023 has been kept as one slab.

Based on the likely future orientation of the economy growth prospects, its population changes and the resultant per capita income growth trend in the project influence area, as discussed above, the following three growth scenarios1 are worked out and presented in Table 6.10 below.

x Normal scenario ± realistic scenario x Optimistic scenario ± 1% addition to normal scenario x Pessimistic scenario ± 1% reduction to normal scenario

1 For sensitivity purpose, the projected economic variables are penetrated as follows for pessimistic and optimistic scenarios, with the assumption that their growth rates will vary between these ranges only. Here the assumption is under optimistic scenario, NSDP will grow than normal scenario and population growth will less than normal scenario. In the pessimistic scenario it will be vice versa. NSDP : Pessimistic Scenario = Normal growth ± 1%; Optimistic Scenario = Normal growth + 1% Population : Pessimistic Scenario = Normal growth + 1%; Optimistic Scenario = Normal growth - 1% PCI: NSDP Growth ± Population growth Using the estimated economic parameters estimated under three scenarios (normal, pessimistic and optimistic scenarios) and the elasticity values, traffic growth under three different scenarios are estimated. 

75 Traffic Forecasting

Table 6.10: Projected growth rates of Economic variables NSDP Population PCI 2013- 2018- 2023 ± 2013- 2018- 2023 ± 2013- 2018- 2023 ± Details 2018 2023 2033 2018 2023 2033 2018 2023 2033 Karnataka Optimistic 8.27% 8.63% 8.25% 0.94% 0.89% 0.85% 7.33% 7.74% 7.40% Pessimistic 6.27% 6.63% 6.25% 1.15% 1.09% 1.04% 5.12% 5.54% 5.21% Normal 7.27% 7.63% 7.25% 1.05% 0.99% 0.94% 6.22% 6.64% 6.31% Goa Optimistic 9.66% 10.09% 9.63% 2.80% 2.66% 2.53% 6.86% 7.43% 7.11% Pessimistic 7.66% 8.09% 7.63% 3.42% 3.25% 3.09% 4.23% 4.84% 4.54% Normal 8.66% 9.09% 8.63% 3.11% 2.96% 2.81% 5.54% 6.13% 5.83% Andhra Pradesh Optimistic 9.40% 9.82% 9.38% 0.84% 0.80% 0.76% 8.56% 9.02% 8.62% Pessimistic 7.40% 7.82% 7.38% 1.02% 0.97% 0.92% 6.38% 6.85% 6.45% Normal 8.40% 8.82% 8.38% 0.93% 0.88% 0.84% 7.47% 7.94% 7.54% Maharashtra Optimistic 11.69% 12.22% 11.66% 1.24% 1.18% 1.12% 10.44% 11.04% 10.54% Pessimistic 9.69% 10.22% 9.66% 1.52% 1.44% 1.37% 8.17% 8.78% 8.29% Normal 10.69% 11.22% 10.66% 1.38% 1.31% 1.25% 9.31% 9.91% 9.41% All India Optimistic 8.84% 9.24% 8.82% 1.21% 1.15% 1.09% 7.63% 8.08% 7.73% Pessimistic 6.84% 7.24% 6.82% 1.48% 1.41% 1.34% 5.36% 5.83% 5.49% Normal 7.84% 8.24% 7.82% 1.35% 1.28% 1.22% 6.50% 6.96% 6.61% 6.6 Traffic growth rates Based on the moderated elasticity values and the projected economic/demographic indicators and with the given model as follows, the future average annual compound traffic growth rates by vehicle type are estimated.

Traffic Growth for Passenger Vehicles:

[(1+P/100) x (1 + R /100) -@  (««««««««««««««««« 

Where, P = the growth rate of population,

R = Real per±capita income

E = The Elasticity value.

Traffic Growth for Goods Vehicles:

Growth Rate for Goods Vehicles = Elasticity Value * N6'3*URZWK5DWH«« 

76 Traffic Forecasting

6.6.1 Estimation of Growth Rates for Commercial Vehicles The growth trend for economic indicators listed above was used for arriving weighted growth rates for commercial vehicles. These weighted growth rates were multiplied with the elasticity values to obtain the final growth rates. The growth rate of Trucks, LCV and 0$9¶s were obtained separately by applying suitable weightages to the corresponding sector of development. The influence of different zones on sectional traffic was made use for arriving at the growth rates.

6.6.2 Growth Rates for Passenger Vehicles

For estimating car traffic growth rate, growth in population, tourism and per capita income for the influence areas was considered. The weighted growth rates have been multiplied with elasticity values to obtain the final growth rates.

6.6.3 Recommended Growth Rates for the Project Road

It was observed that the growth rates arrived on the basis of socio-economic growth of the project area are more realistic. As such, it is adapted for the projection of traffic for the design period. The final recommended growth rates are given in Table 6.11. The growth rates arrived at for different vehicle categories were adopted for all the sections.

The slow moving vehicles essentially cater to localized demand for transportation of passengers and goods from rural areas to the nearest markets. Motorized vehicles are gradually replacing carts. The slow moving traffic is not expected to have high growth rates. Therefore, a 2% growth rate has been adopted for the cycle, agricultural tractors and carts. The resultant traffic growth scenarios are presented in Table 6.10.

77 Traffic Forecasting

Table 6.11: Projected Traffic Growth Rates from 2013 to 2023 Projected Traffic Pessimistic Normal Approach Optimistic Approach Growth Rates Approach

Projected Traffic Projected Traffic Projected Traffic Sl. Growth Rate (%) Growth Rate (%) Growth Rate (%) N Vehicle Type 2013 2018 2023 2013 2018 2023 2013 2018 2023 o ------2018 2023 2033 2018 2023 2033 2018 2023 2033 1 LCV 8.0% 7.6% 6.5% 9.2% 8.7% 7.5% 10.5% 9.8% 8.5%

2 2-Axle Truck 4.9% 4.6% 3.9% 5.6% 5.3% 4.5% 6.4% 6.0% 5.2%

3 3-Axle Truck 8.0% 7.6% 6.5% 9.2% 8.7% 7.5% 10.5% 9.8% 8.5%

Multi-Axle 4 7.0% 6.6% 5.6% 8.0% 7.6% 6.5% 9.1% 8.6% 7.4% Truck

5Bus 6.8% 6.6% 5.7% 7.9% 7.6% 6.6% 9.0% 8.5% 7.4%

6Car 8.5% 8.3% 7.1% 10.0% 9.6% 8.3% 11.4% 10.9% 9.4%

7 Two Wheeler 8.9% 8.7% 7.5% 10.5% 10.1% 8.7% 12.1% 11.5% 10.0%

Auto 8 5.2% 5.1% 4.4% 6.0% 5.8% 5.0% 6.8% 6.5% 5.6% Rickshaw

6.6.4 Regional Economic Development

As the projected growth rates are based on state-level vehicle population growth (as a proxy variable to the project road traffic data) it is necessary to moderate these growth rates further to the project road specific conditions.

Industrial and tourism related activities are the major potential traffic generators for the project road and most of the activities indicated below. These will generate additional traffic (both goods and passenger) on the project road in future. Details of the major economic activities along project road are given below.

Industrial scenario

Large Scale industries:

Hospet steels Kalyani steels, Mukand steels, Kirloskar ferrous industries.

78 Traffic Forecasting

Small scale industries:

Gadag co-operation textile mill, Abhay solvents, Spinning mill co-operation.

Upcoming Industrial Proposals

Pusco: Near Halligudi village,Koppal road 3382 acres, steel industry Notification date- March 2011, Proposal completion tentative date- December 2012.

Kalavati Industries Ltd:Steel/Power plant, near harlapur (Gadag) 580 acres, tentative completion of proposal-December-January-2012-13.

Adunica Metallics: near Harlapur, steel industry, 780 acres Two land Banks to be acquired, in Mundargi Taluk(near gentli Shirur and Mevandi), 1780 acres each.

Arcelor Mittal: Arcelor Mittal has decided on Kudithini and Torangal in Bellary district in Karnataka to set up an integrated steel plant. 6 million tons per annum integrated steel plant and 750 MW power generations. The company will invest around Rs.300, 000 million and has requested 4000 acres to set up the project. Karnataka government is identifying land near the Bellary power plant and due notification is in progress. The proposal is expected to be taken to the state government's high-powered committee. The project will be commissioned in 4 to 5 years. The company has acquired 1827 acres in possession and is hoping to get another 927 acres by May 2012.

Major diversion/addition point:

Annigere (km 172+000), Gadag(km 192+000), Koppal(km 250+000)

Commercial vehicle scenario:

Major commercial vehicles include 2 and 3 axle and MAV carrying fuel, steel and iron ore other commercial goods, diversion of the same taking place at Annigere which leads to Navalgund, Saudatti, Bijapur and so on.

Alternative route scenario:

Major alternative routes are Dharwad-Hebsur-Annigere, Hubli-Hebsur-Annigere and Adavisompur-Mundargi-Koppal, although they are not competitive in terms of time taken to travel and present road condition.

79 Traffic Forecasting

6.7 Traffic Projection For traffic projection, expected traffic on the road comprises three components: Normal traffic; Generated traffic; Diverted traffic. The project section has been divided into three sections for traffic analysis based on primary traffic survey.

6.7.1 Normal Traffic Applying the recommended growth rates for the different category of vehicles, the AADT obtained from the analysis are projected and assigned for the different sections.

6.7.2 Generated Traffic Generated traffic is the additional vehicle travel that results from the new facility. There would be some amount of induced traffic on the project corridor due to the reduction in the VOC and savings in travel time. The new facility will cause an improved land use pattern resulting in more trip generation. It is premature at this stage to assess such growth.

The project stretch will have additionally generated traffic because of the upcoming LQGXVWULHV 6(=ெV HWF 6RPH RI WKH PDMRU SURMHFWV WKDW LPSDFW WKH SURMHFW VWUHWFK DUH already listed above.

With improved industrialization and resulting changes that could be anticipated in land use pattern, additional trips of about five percent can be expected to be generated in the 1st year of completion of the project.

Impact of Mining Activities

Karnataka has large reserves of high-quality iron ore. The reserves of iron ore in the state are the second largest in the country and estimated at about 3447 Million tons, consisting of about 929 Million tons of hematite ore and about 2518 Million tons of magnetite ore. There are as many as 153 Mining leases for iron ore in 16,000 hectares and 56 Million tons of iron ore is currently being extracted in the state out of which, 25 Million tons is being exported & 31 Million tons is being consumed by domestic industries (units in Karnataka and other states). The production is mainly concentrated in Bellary/Hospet area with the balance from Chitradurga, Bagalkot and Tumkur districts.

80 Traffic Forecasting

The discussion had with port officials reveal that about 400 mining trucks are allowed in Karwar Port and 1000 mining trucks are allowed in Belekeri port daily in the normal period prior to the ban currently enforced. A cross check of the same with the past traffic counts obtained from PWD was sought to be made, however, there is no separate count of mining trucks in PWD data. As far as the current scenario on mining is concerned, the SC has lifted the ban on selected mining companies (which have not been booked for illegal mining) to start the mining operations.

With reasonable assumptions on the distribution, additional numbers of mining trucks that are likely, once the activity is restored is estimated to be around 3000 trucks respectively. This would be in addition to the traffic already forecast. However, there being no expansion plan for mining activities only 2% growth rate is adopted for such trucks.

6.7.3 Diverted Traffic The project corridor is a major road in the study region traversing south to north connecting several States and there are no new competing roads other than those that already exist. The existing state roads are farther away that would result in the substantial detour and further the terrain and road conditions do not induce traffic diversions. As such, diverted traffic is not anticipated for the project road.

6.7.4 Final Traffic Projections With all the above considerations kept in mind and assumed that all the upcoming industries are constructed and completely operational from the year 2016, an additional traffic of 5% is generated by this new facility and adopting optimistic growth rates considering the regional economic development, the base year traffic and the future traffic projected at the two locations Nalavadi and Hallikeri is as seen from Table 6.12 and Table 6.13

81 Traffic Forecasting

Table 6.12: Base year Traffic Survey Location Nalvadi Hallikeri Vehicle Category ADT Vehicles AADT (PCU) ADT Vehicles AADT (PCU) Two Wheeler 898 395 329 147 Auto Rickshaw 40 36 36 32 Car/Jeep/Van/Taxi 1960 1727 1019 897 Mini Bus 24 32 18 25 Buses 658 1739 259 686 Mini LCV 366 322 212 186 LCV (4&6 Tire) 282 372 212 279 Two Axle Truck 398 1050 285 752 Three Axle Truck 489 1290 438 1156 Multi Axle Trucks 145 574 204 807 HCM / EME 3 12 5 20 Tractor 40 54 12 16 Tractor + Trailer 54 214 71 277 Cycles 53 27 10 4 Cycle Rickshaw 1 2 1 2 Animal Drawn Carts 4 24 3 18 Mining trucks 3000 9000 3000 9000 Total 8415 7869 3112 5305

82 Traffic Forecasting

Table 6.13: Traffic Projections for the two Homogenous Sections Traffic projections for different sections Nalavadi Hallikeri Year Vehicles PCU's Vehicles PCU's 2013 8415 16868 6112 14299 2014 9033 17789 6487 14964 2015 9712 18788 6894 15681 2016 10788 20292 7523 16753 2017 11643 21511 8029 17616 2018 12586 22837 8582 18549 2019 13575 24216 9162 19516 2020 14659 25710 9792 20558 2021 15849 27331 10481 21682 2022 17156 29092 11234 22897 2023 18591 31006 12058 24211 2024 19977 32848 12853 25477 2025 21473 34821 13707 26827 2026 23098 36944 14632 28273 2027 24864 39231 15633 29825 2028 26784 41694 16718 31489 2029 28871 44350 17892 33277 2030 31141 47215 19165 35196 2031 33609 50305 20545 37260 2032 36295 53641 22041 39478 2033 39218 57242 23665 41865

6.8 Capacity Analysis The capacity analysis is fundamental to the planning, design and operation of roads. It is a valuable tool for evaluation of the investment needed for the future improvements. The capacity figures used for determining the desired carriageway width in differing terrain with respect to traffic volume and composLWLRQ DUH DV SHU ³0DQXDO RI 6SHFLILFDWLRQ  6WDQGDUGV IRU )RXU /DQQLQJ RI +LJKZD\V´ )RU WKH SXUSRVH RI DXJPHQWDWLRQ RI WKH facilities and up gradation of the project highway, the design service volume for the plain terrain condition and level of Service B and LOS C is shown in Table 6.14 [45, 46]

83 Traffic Forecasting

Table 6.14: Design service volume for plain terrain

Lane Configuration Design Service Volume (PCUs per day)

2-Lane with1.5m Paved Shoulder (LOS B) 18000

2-Lane with1.5m Paved Shoulder (LOS C) 24000

4-Lane with 1.5m Earthen Shoulder (LOS B) 35000

4-Lane with 1.5m Earthen Shoulder (LOS C) 49000

4-Lane with 1.5m Paved Shoulder (LOS B) 40000

4-Lane with 1.5m Paved Shoulder (LOS B) 60000

Table 6.15: Details of the capacity analysis Design Traffic Homogenous Sections Service Project facility Level of Service Volume Hallikeri - Nalavadi - (Year) ( PCUs (Year) per day) Maximum 2-Lane with1.5m LOS B 15000 Capacity Reached 2014 Earthen Shoulder LOS C 21000 2016 2020 2-Lane with1.5m LOS B 18000 2014 2017 Paved Shoulder LOS C 24000 2018 2022 4-Lane with 1.5m LOS B 35000 2025 2029 Earthen Shoulder LOS C 49000 2030 - 4-Lane with 1.5m LOS B 40000 2027 2032 Paved Shoulder LOS C 60000 - -

As seen from Table 6.15 The Present traffic at Nalvaadi LV3&8¶VZKLFKPHDQVWKH maximum capacity has already been reached at this Location and the capacity reaches maximum value for the second location Hallikeri in the year 2014 (for LOS B) hence it is suggested that 4- laning with a Paved shoulder should be taken up immediately for both the homogeneous location.

84 Chapter- 7 DISCUSSIONS AND CONCLUSIONS

7.1 Discussions

7.1.1 Discussions based on traffic volume characteristics x The Average Daily Traffic (ADT) oEVHUYHGDW1DODYDGLLVIRXQGWREH3&8¶V YHKLFOHV DQGWKH$'7DW+DOOLNHULLV3&8¶V YHKLFOHV  x At Nalavadi the Passenger vehicles constituted 66.1% while slow moving vehicles constituted 1.1 % and commercial traffic shared 32.8% of the total traffic. 36.2% of the total traffic on the project stretch is Car / Jeep/Van & Taxi and 16.6% is two- wheeler, whereas buses are observed to be 12.6% of total traffic. x At Hallikeri Passenger vehicles constituted 53.4%, while slow moving vehicles constituted 0.4 % and commercial traffic shared 46.2 % of the total traffic. 32.7% of the total traffic on the project stretch is Car / Jeep/Van & Taxi and 10.6% is two- wheeler, whereas buses are observed to be 8.6% of total traffic. x At Nalavadi the Peak Hour Traffic is observed in the evening (05.00pm ± 06.00 pm) is 554 PCU's and the peak hour factor is 6.2% whereas at Hallikeri the Peak traffic is observed in the evening (07.00pm ± 08.00 pm) and is 322 PCU's and the peak hour factor is 5.3%. x At Nalavadi thHGDLO\YDULDWLRQRIWUDIILFUDQJHVIURPDPD[LPXPRI3&8¶V WRDPLQLPXPRI3&8¶Vwhereas at Hallikeri the daily variation ranges from a PD[LPXPRI3&8¶VWRDPLQLPXPRI3&8¶V x AADT after applying Seasonal Correction Factors is found to be 7869 and 5305 3&8¶VDW1DODYDGLDQG+DOOLNHULUHVSHFWLYHO\

7.1.2 Discussions based on Influence Factors A comparative study of the influence factors indicated that Karnataka State, where the project stretch runs has the majority influence of ninety-two percent (92%). State of Goa, Andhra Pradesh and Maharashtra that has its border abutting Karnataka State has an influencing factor of two percent (2%) four percent (4%) and two percent (2%) respectively.

85 Discussions & Conclusions

Table 7.1: Influence Factors Obtained from O-D Analysis States Cars Buses Truck Average Karnataka 97.0% 93.9% 86.4% 92.4% Goa 1.3% 0.5% 3.7% 1.8% Andhra Pradesh 1.1% 3.7% 6.6% 3.8% Maharashtra 0.6% 1.9% 2.7% 1.7% Rest of India 0.0% 0.0% 0.7% 0.2% Total 100.0% 100.0% 100.0% 100.0%

7.1.3 Discussions Based on Elasticity Values and R-squared values: The Elasticity values and R-squared values obtained from regression analysis for Karnataka State are as shown in Table 7.2. Table 7.2: Elasticity Values Derived based on Regression Analysis for Karnataka Mode Variable Elasticity R square T-Static P-Value Two Wheelers PCI 2.86 0.80 3.45 0.04 Cars PCI 1.48 0.97 12.27 0.00 Buses PCI 1.45 0.75 3.92 0.01 Goods NSDP 1.23 0.97 11.79 0.00 Auto Rickshaw PCI 0.84 0.7 3.38 0.02

From the Table 7.2, it can be inferred that the R-squared values are maximum for Goods Vehicles and Cars (0.97). The P-Values for the entire vehicle category varies from 0.00 to 0.04, which means that the dependent variable is very much related to the independent variable, i.e. the Socio- Economic Variables and the Vehicle Registration data.

7.1.4 Discussions Based on Growth Rates: The Growth estimates based on past traffic data collected from PWD department was found to be inadequate and there was no definite growth trend observed. Table 6.3 provides details of the Growth rates of various Methods adopted for the study.

86 Discussions & Conclusions

Table 7.3: Comparison of Growth Rates Method Mode Two Auto Car Bus Truck Wheeler Rickshaw Past Traffic on Project No trend No trend No trend No trend No trend Corridor Vehicle Registration 11.10% 10.91% 11.62% 11.22% 8.27% Growth of Karnataka State Elasticity Method 10.50% 10% 7.90% 8% 6% (2004-2011)

It can be observed from the Table 6.3, during the last 7 years, the average growth of two- wheelers, cars and that of trucks is around 11%-12%. This high growth rate of more than 10% may not sustain in future. Therefore, another rational approach i.e. elasticity method was adopted in order to derive realistic growth rates. The Growth rates obtained by elasticity method were adopted for traffic forecasting and the Optimistic growth rates were adopted as the project corridor is in and around places where there is a huge Economic development taking place. Table 7.4: Adopted Growth rates for Traffic Forecasting Projected Traffic Growth Rates Optimistic Approach Projected Traffic Growth Rate (%) Sl. No Vehicle Type 2013-2018 2018-2023 2023-2033 1 LCV 10.5% 9.8% 8.5% 2 2-Axle Truck 6.4% 6.0% 5.2% 3 3-Axle Truck 10.5% 9.8% 8.5% 4 Multi-Axle Truck 9.1% 8.6% 7.4% 5 Bus 9.0% 8.5% 7.4% 6 Car 11.4% 10.9% 9.4% 7 Two Wheeler 12.1% 11.5% 10.0% 8 Auto Rickshaw 6.8% 6.5% 5.6%

7.1.5 Discussions on Capacity Analysis:

7KH3UHVHQWWUDIILFDW1DOYDDGLLV3&8¶VZKLFKPHDQVWKHPD[LPXPFDSDFLW\KDV already been reached at this Location and the capacity reaches maximum value for the second location Hallikeri in the year 2014 ( for LOS B) hence it is suggested that 4- laning with a Paved shoulder should be taken up immediately for both the homogeneous location.

87 Discussions & Conclusions

Table 7.5: Results of Capacity Analysis

Design Service Homogenous Sections Level of Project facility Volume Service ( PCU/day) Nalavadi - Hallikeri - (Year) (Year) Maximum LOS B 15000 2014 2-Lane with1.5m Capacity Reached Earthen Shoulder LOS C 21000 2016 2020

2-Lane with1.5m LOS B 18000 2014 2017 Paved Shoulder LOS C 24000 2018 2022 4-Lane with LOS B 35000 2025 2029 1.5m Earthen Shoulder LOS C 49000 2030 - 4-Lane with LOS B 40000 2027 2032 1.5m Paved Shoulder LOS C 60000 - -

7.2 Conclusions x In the area of influence of the project road, there is no good network of National Highways. The development of the project corridor will further boost the economy of the area and is considered to be the most desirable feature for the social and economic prosperity for the people of the area. x The traffic projections for the years up to 2033 have been worked out using the Econometric method under three scenarios. The overall traffic levels under the likely and pessimistic scenario are around 15% to 25% lower compared to the optimistic scenario in the horizon year. x For the purpose of highway design and project development, we need to adopt one Scenario. The present, optimistic, economic outlook is based on the experience of only past few years. Also, with structural changes in the economy the traffic elasticities with respect to economic growth may go down more than expected. x High fuel costs may also affect traffic growth negatively. However, it is understood that Karnataka as a state has not yet reached its socioeconomic potential and would, therefore, register a higher growth impetus. Further, the project corridor connects to one of the largest iron ore reserves in India and some important cities like Karwar and Hubli.

88 Discussions & Conclusions

x Although it is difficult to translate macroeconomic indicators at a microeconomic regional level, we believe the growth of this region would be faster than the general growth rate of the state. In view of the above discussion Optimistic Growth Rates has been adopted for Traffic Forecasting. x It is observed that with the constraints of availability of proper data and fluctuating developing economy, the task of Traffic Demand Forecasting could be quite tricky, subjective and approximate.

7.3 Scope for Further Study The Present Study could not accomplish some of the works that can be done as an extension of this project work due to time constraints. Further studies that can be conducted are: x Toll Revenue Estimation if the Project Corridor is upgraded from two-lane to four-lane under Public Private Partnership (PPP) basis. x Planning of Road sLGHDPHQLWLHVDQG7UXFNOD\E\H¶VRQWKH3URMHFW&RUULGRU x Junction Improvement can be done on the basis of Turning Movement Surveys. x Other Time Series analysis can be done if data on past traffic is available for quite a number of years. x In this study only a few variables were used to model the demand, other variables such as mix of Urban/Rural population, Land Use, Percentage of Working Class Population etc., can be tried.

89 REFERENCES

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9. 9,5(1'5$ .80$5 ³IUHLJKW JHQHUDWLRQ DQG DWWUDFWLRQ DQDO\VLV RQ UHJLRQDO EDVLV´Traffic Engineering, Highway Research Board No 28, 1986.

10. /5.DGL\DOL 796KDVKLNDOD³5RDG7UDQVSRUW'HPDQG)RUHFDst for 2000 AD 5HYLVLWHGDQG'HPDQG)RUHFDVWIRU´Journal of the Indian Road Congress October ± December 2009, Paper No. 557.

11. -DKDU56DUNDUDQG'U%KDUJDE0DLWUD³&ULWLFDOFRQVLGHUDWLRQRI7UDYHO'HPDQG Forecasting on National Highways: A Case Stud\´ Journal of the Indian Road Congress :Volume 62 No.3 2001

12. 9LMD\ .XPDU ³7UDIILF &KDUDFWHULVWLFV $QG 'HPDQG $ORQJ 1RUWK DQG 6RXWK Corridors- $&DVH6WXG\´M.E. Thesis, Bangalore University, Bangalore March 2001. 13. 7KDPL]K $UDVDQ 9 5HQJDUDMX 95 ³,QWHr- city Travel Demand analysis- A &DVH6WXG\´Traffic Engineering, Highway Research Board. No.48, 1983.

90 14. Dr. S.K Khanna & Dr. C.E.G. Justo, Highway Engineering, Nem Chand & Bros, 2001, pp 179-183. 15. Indian Road Congress: 102- ³7UDIILF 6WXGLHV IRU 3ODQQLng Bypass around WRZQV´

16. *KDUHLE$+³'LIIHUHQW7UDYHO3DWWHUQV´Journal of Transportation Engineering, ASCE, 122(1), 67-76, 1966.

17. 'U 5 6DWLVK .XPDU 3UDYHHQ 9 ³$ 6WXG\ RQ 5RDG )UHLJKW &KDUDFWHULVWLFV- 7KLUXYDQDQWKDSXUDP 'LVWULFW´ - A Case Study. 10th National Conference on Technological trends, Nov -2009.

18. Sikka, R.P, Raghavachari, S., Chandrasekar, B.P, and Srinivas Nagu, P., ³$QDO\VLV RI 7ULS OHQJWK )UHTXHQFLHV RI +LJKZD\ )UHLJKW 7UDIILF LQ +\GHUDEDG 5HJLRQ³Journal of Indian Road Congress, Vol.52-2,Oct-1991

19. 6DQJKDSUL\D3KXOH´5HJLRQDO)UHLJKW7UDQVSRUW'HPDQG0RGHOLQJ´0E Thesis Bangalore university, Bangalore, Aug-2000.

20. -DVRQ ' /HPS ³$QWLFLSDWLQJ :HOIDUH ,PSDFWV YLD 7UDYHO 'HPDQG )RUHFDVWLQJ 0RGHOV´Proceedings of the 88th Annual Meeting of the Transportation Research Board, January- 2009. Washington D.C. 21. 6XEUDPDQLDP*RSDODNULVKQDQ³3UHGLFWLRQRI6KRUW-Term Traffic Volume for the $SSOLFDWLRQVLQ,QWHOOLJHQW7UDQVSRUWDWLRQ6\VWHP´Thesis, University of Regina, Saskatchewan, August- 2000. 22. L.H. Immers -( 6WUDGD ³7UDIILF 'HPDQG 0RGHOOLQJ´ .DWKROLHNH 8QLYHUVLWHLW Leuven- May -1998. 23. Vassilos $3URILOOLGLV*HRUJH1%RW]RULV³(FRQRPHWULF0RGHOVIRUWKHIRUHFDVW RISDVVHQJHUGHPDQGLQ*UHHFH´Journal of Statistics and Management Systems. Vol. 9 (2006), No.1, pp. 37-54..

24. $7HFKQR(FRQRPLF )HDVLELOLW\5HSRUWRQµ6WXGLHVIRUGHYHORSPHQWRI$JUDWR /XFNQRZ$FFHVV&RQWUROOHG([SUHVVZD\¶7UDIILF5HSRUW)HHGEDFN,QIUD$SULO± 2013.

25. Todd Litman, Victoria Transport Policy Institute, Transportation Elasticities, How Prices and Other Factors Affect Travel Behavior, 2 February August 2010

26. Indian Road Congress: 9-³7UDIILF&HQVXVRQ1RQ8UEDQ5RDGV´

91 27. Indian Road Congress: SP: 19-2001, Manual for Survey, Estimation and Preparation of Road Projects.

28. Indian Road Congress: 64- ³*XLGHOLQHV IRU &DSDFLW\ RI 5RDGV LQ 5XUDO $UHDV´

29. Rajasthan State Road Development and Construction Corporation ± ³3UHSDUDWLRQ of DPR for development of Salumber - Keer Ki Choki 5RDG´± January 2011.

30. Public Works Department, Rajasthan, Consultancy Services for the preparation of Feasibility Report for two laning with paved shoulder of Silkar-Bikaner section Km357 to Km 557 of NH- 11. July ± 2010.

31. Tom V. Mathew, Traffic Engineering, Chapter1-5, IIT Bombay, September- 2011.

32. Rajasthan State Road Development and Construction Corporation Limited, Detailed Project Report for the “Development of Road the section from Beawar ± Merta road, Lambiya ± Jaitaran & Mangliwas ± 5DV5RDG´PDCOR Limited. May -2010. 33. 0-%XUWRQµ,QWURGXFWLRQWR7UDQVSRUWDWLRQ3ODQQLQJ¶+XWFKLQVRQRI/RQGRQ 34. -DLSXU5RDG$XWKRULW\³&RQVXOWDQF\6HUYLFHVIRUWKHSUHSDUDWLRQRI)HDVLELOLW\- cum- Prelimnary Project Report (PPR) for Jaipur Ring Road form Ajmer Road to Agra road Section in the state of Rajasthan, UPHAM International Corporation. Sep-2010.

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38. Advantage Karnataka: Global Investors Meet, A report on Sector Profile: Automobile. Government of Karnataka ± 2010.

39. Reserve Bank of India, Handbook of Statistics on the Indian Economy, Government of India ±Sep 2012.

92 40. &HQVXVRI,QGLD³3RSXODWLRQ3URMHFWLRQIRU,Qdia and States 2001-´- Report of the Technical Group On Population Projections Constituted By The National Commission On Population May 2006.

41. 5RJHU)RXTXHW³7UHQGVLQ ,QFRPHDQG3ULFH(ODVWLFLWLHVRI7UDQVSRUW'HPDQG´ Basque Centre for Climate Change – February 2012.

42. Amado Crotte $OYDUDGR³(VWLPDWLRQRI7UDQVSRUW5HODWHGGHPDQG(ODVWLFLWLHVLQ 0H[LFR &LW\´ $Q $SSOLFDWLRQ WR 5RDG 8VHU &KDUJLQJ Centre for Transport Studies Imperial College London, October ± 2008.

43. .DXVKLN 'HE 0DVVVLPR )LOLSSLQL ³3XEOic bus transport demand elasticites in ,QGLD´-XO\± 2010.

44. 0LQLVWU\ RI 5RDG 7UDQVSRUW DQG +LJKZD\V ³5RDG 'HYHORSPHQW 3ODQ 9LVLRQ ´Indian Road Congress ± 2001.

45. Feasibility Report for Up-gradation of NH-65, Jodhpur to Pali Road on DBFOT Basis, Public Works Department, Rajasthan ± 2010.

46. 0LQLVWU\ RI 5RDG 7UDQVSRUW DQG +LJKZD\V ³0DQXDO RI 6SHFLILFDWLRQ  6WDQGDUGVIRU)RXU/DQQLQJRI+LJKZD\V´Indian Road Congress-2010.

93 ANNEXURES Classified Traffic Volume Count Survey Sheet for Passenger and Goods Vehicles 23 24 LCV Truck Car Bus Toll Vehicles Exempted Drawn Animal Rick. Cycle Cycle Slow ModesSlow Vehicles Exempted Toll Trailer Bus Tractor + Tractor EME HCM / HCM Mini Bus School Bus Govt.Bus Pvt.Bus Axles) MAV (> 6 Axles) Fast Passenger Vehicles Passenger Fast Car/Jeep/ /Van Car/Jeep/ MAV (4 to 6 (4 to MAV Classified Traffic Volume Count Survey (Goods Vehicles) (Goods Survey Count Volume Traffic Classified Truck 3 Axle Board) Classified Traffic Volume Count Survey Sheet for Passenger Vehicles Passenger for Sheet Survey Count Volume Traffic Classified Taxi (Yellow ______Date Enumerator Day Weather Sheet No: Truck 2 Axle Goods Vehicles Goods LCV-6Tyre Auto Rick.Auto Trip Vans LCV- 4Tyre 12 3 4 5 6 7 8 9 Two Two 10 11 12 13 14 15 16 17 18 19 20 21 22 25 26 Wheelers Mini LCV Time Total Hourly Hourly Hourly LOCATION ______LOCATION 08:00 - 08:00 08:15 - 08:15 08:30 - 08:30 08:45 - 08:45 09:00 Total DIRECTION ______DIRECTION 08:00 - - 08:00 08:15 - 08:15 08:30 - 08:30 08:45 - 08:45 09:00 LOCATION ______LOCATION DateDIRECTIONTime Enumerator Day Weather Sheet No:

94 77 71 55 51 46 32 38 40 68 100 137 165 152 223 295 274 190 185 259 196 288 147 176 104 3369 Total Vehicles Bus LCV Truck Car Drawn Animal Cycle Cycle Rickshaw Cycle Trailer Tractor + + Tractor Tractor EME HCM/ Monday 4-Feb-13 6 Axles) MAV ( > ( MAV MAV 3 Axle 3 Axle Truck Date : Date Day 2 Axle 2 Axle Truck Summary Sheet LCV 4 tyre 6 tyre Mini Mini LCV Traffic Volume Count Summary Sheet (Sample) Sheet (Sample) Summary Count Volume Traffic Bus Private Private Bus Bus Govt Govt Bus Bus School School Bus Mini Mini Van Car / / Car Jeep / / Jeep FAST PASSENGER VEHICLES PASSENGER FAST VEHICLES COMMERCIAL FAST MODES SLOW VEHICLES EXEMPTED TOLL Trip VanTrip Taxi 159+500 Hubli - HospetHubli Auto Rickshaw 1 2 34567891011121314151617181920212223242526 Two Wheeler TIME Daily TotalDaily 577 30 51 314 823 10 8 287 105 250 52 126 241 278 72 0 2 35 24 70 1 1 11 1 0 0 09:00 - 10:00 - 09:00 11:00 - 10:00 34 12:00 - 11:00 43 13:00 - 12:00 41 14:00 - 13:00 1 44 15:00 - 14:00 4 60 16:00 - 15:00 0 45 0 17:00 - 16:00 2 38 0 18:00 - 17:00 3 25 0 19:00 - 18:00 4 8 43 4 20.00 - 1419:00 2 47 5 21:00 - 20:00 1 31 8 62 55 4 22:00 - 21:00 1 8 14 0 23:00 - 2322:00 0 1 42 20 0 4 24:00 - 3323:00 55 2 11 76 3 01:00 - 1400:00 0 0 0 6 74 7 0 02:00 - 1101:00 2 2 3 2 55 5 03:00 - 3002:00 0 0 14 2 1 49 3 04:00 - 2703:00 14 0 0 4 13 0 80 0 05:00 - 04:00 23 16 0 0 4 1 10 2 1 06:00 - 1405:00 4 17 1 4 51 1 97 0 07:00 - 06:00 26 0 0 1 3 1 50 6 1 08:00 - 07:00 27 5 0 10 0 2 3 1 0 1 15 23 0 0 6 9 12 2 2 0 27 17 28 1 1 0 9 19 0 31 0 20 0 4 0 16 6 3 0 14 7 25 0 0 7 2 1 10 7 3 1 0 23 0 5 0 8 14 6 2 3 0 6 9 9 0 14 0 3 8 9 5 5 14 0 4 15 0 0 4 0 8 8 6 4 18 10 0 3 9 0 8 0 33 2 22 4 5 16 10 1 0 7 0 1 6 9 8 5 0 10 13 15 2 0 0 0 6 23 8 1 9 17 9 0 2 0 3 5 0 16 2 8 0 2 0 9 2 1 5 7 0 0 19 7 0 11 0 1 4 14 8 0 0 9 18 0 0 1 9 0 14 5 10 0 3 0 2 0 0 2 1 11 1 1 7 9 3 0 0 5 0 11 0 9 2 0 2 7 5 0 1 0 2 3 2 8 3 15 2 0 0 1 8 5 5 3 21 0 0 6 2 2 2 1 0 12 1 0 1 0 1 3 0 1 1 10 17 6 3 0 0 0 5 0 2 3 5 7 1 6 3 0 3 1 2 0 17 6 3 6 0 0 0 0 5 5 1 17 7 2 7 5 2 4 11 0 4 5 1 0 0 4 3 9 0 4 6 4 4 3 0 15 0 0 6 0 4 4 4 0 0 0 0 0 6 5 0 6 3 0 16 2 0 9 0 0 3 0 0 3 1 1 8 0 2 0 0 12 0 1 0 0 6 0 0 0 2 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 2 0 0 0 1 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 08:00 - 09:00 - 08:00 19 5 0 7 19 0 2 5 0 13 4 3 8 9 0 0 0 0 1 5 0 0 0 0 0 0 Direction Location

95 Fuel Sales Data Collected from Fuel Stations along the Project Corridor (Sample) IESEL PETROL IESEL PETROL 61808 1961866672 119268 26807 25536110385 161807 106344 26681 29620 23723 131716 169089 30924 31962 171055 33083 75839 2237763077 102271 27723 21520100813 156229 117615 27181 30932 28330 130352 153930 30537 32428 173140 34619 120662 27081 124324 28867 148628 32774 85942 20416 115506 26001 176412 29598 86367 21809 114054 27070 164079 31558 91383 22433 121617 29257 145871 32904 87589 22348 128525 26292 149404 30903 126952 29789 143136 31376 175451 36044 126022 5052146049 5455 131483 5780 127142 5048 100720 8578 100978 7169 125812 10250 101980 9593 131344 4284 120580 5259 100629 7178 102147 9956 124084 7330124083 6734 154079 5929 155000 6775 151257 6329 117987 10577 117454 10684 119875 14191 119470 1281 130684 5457 103489 7535 95923 9074 88216 4421 132915 4774 122864 8838 103789 10742 126547 4762 180916 5580 113740 10039 103057 10305 145552 4223 178215 3469 118837 8685 97899 10393 Fuel sales data (in liters) obtained from BP outlet at Km 141+000 Km at outlet BP from obtained liters) sales (in Fuel data Pump Pertol Sai Dealer Sri : FUEL DATAFUEL FOR CALCULATING SEASONAL THE CORRECTION FACTORS (2007-2008) (2008-2009) (2009-2010) (2010-2011) (2011-2012) 71940 3579 155498 3798 131884 5205 111903 8802 81817 8557 3776 212 157681 2827 155286 5430 106480 8837 86201 9933 95208 4419 102392 3344 149616 6218 110118 10233 77020 8909 Fuel sales data (in liters) obtained from Indian Oil outlet at Km 250+000 Km at outlet Oil Indian from obtained liters) sales (in Fuel data (2007-2008) Fuels Dealer Ajay : (2008-2009) (2009-2010) (2010-2011) (2011-2012) NAME November October December September January February August July April May June June July May August September November October December January April March February NAME PERIOD FUEL DATAFUEL FOR CALCULATING SEASONAL THE CORRECTION FACTORS PERIOD March Dealer : RSN Oil Kulkoti 92437 6570 82460 9847 60207 13223 74390 14388 108265 7308 94617 9923 73315 13312 104920 17455 69000 3000 48000 12000 60000 12000 71000 13000 60000 6000104000 400045000 119000 3000 13000 118000 14000 178000 87000 16000 129000 9000 15000 151000 17000 227000 136000 25000 20000 167000 21000 111489 8092 134340 10126 139559 12858 190712 15429 53000 7000 52000 8000 85000 23000 97000 23000 54000 6000 61000 11000 73000 11000 104000 16000 52000 8000 73000 11000 104000 16000 88000 20000 147141 10220 174504 12203 221015 14957 155601 14835 50000 10000 72000 12000 80000 16000 124000 20000 172330 10154 161537 12519 180508 17368 224022 8509 72000 12000 88000 8000 101000 19000 92000 16000 53000 700050000 10000 81000 81000 15000 15000 104000 16000 80000 16000 96000 24000 104000 28000 87000 9000 104000 16000 99000 21000 148000 20000 sales data(in liters)obtained from Indian Oil outlet at Km 181+ Km at outlet Oil Indian from liters)obtained data(in sales FUEL DATAFUEL FOR CALCULATING SEASONAL THE CORRECTION FACTORS DATAFUEL FOR CALCULATING SEASONAL THE CORRECTION FACTORS (2007-2008) (2008-2009) (2009-2010) (2010-2011) (2011-2012) (2007-2008) (2008-2009) (2009-2010) (2010-2011) (2011-2012) 111745 8320 103559 10422 138215 13025 103545 17342 125752 19327 101750 7590113734 65620 8848 6728 138879 147394 10646 13839 150655 96025 12448 22264 88748 117467 15949 19913 93313 15493 70302 6064 91753 9480 103916 13759 173247 31718 113160 21015 107490 7882 145569 9458 140489 11509 98538 13909 94083 14963 16950 2300 74324 7732 80630 11748 87583 19427 102330 14807 DIESEL PETROL DIESEL PETROL DIESEL PETROL DIESEL PETROLDIESEL PETROL DIESEL PETROL DIESEL PETROLDIESEL PETROL DIESEL PETROL D DIESEL PETROL DIESEL PETROL DIESEL PETROL DIESEL PETROLDIESEL PETROL DIESEL PETROL DIESEL PETROLDIESEL PETROL DIESEL PETROL D 101292 8428 146747 11111 181941 12588 128010 17744 120598 16780 Fuel sales data (in liters) obtained from Indian Oil outlet at Km 169+000 Km at outlet Oil Indian from obtained liters) sales (in Fuel data Amadla Dealer S.K. : Fuel 000 December November January October February September August July April May June June July August September October NAME PERIOD May April November January March February December AverageNAME 62417 7083 82000 12000 102417 16750 122417 20250 Average 56975 2737 128912 4459 145650 5410 113250 8567 101081 10216 Average 89038 7061.714 116509 8993 132558 11961 120858 17506 126362 16076 Average 89791 23899 121227 28076 162091 32202 March PERIOD

96 Format for O-D Survey (Passenger Vehicles)

ORIGIN & DESTINATION SURVEY (PASSENGER VEHICLES)

Name of Road : Direction: Location at Km: Survey Duration : 24 hrs 1. Car / jeep / Van Vehicle Type of Vehicle 2. Taxi Particulars 3. Trip Van Place:

District: Origin State: O-D Place:

District: Destination State:

Trip Length

Frequency

ZĞƚƵƌŶǁŝƚŚŝŶ Ϯϰ,ŽƵƌƐ

Dailyϭ͘^ŝŶŐůĞ ϳ͘ZĞƚƵƌŶ

Alternate Days Ϯ͘^ŝŶŐůĞ ϴ͘ZĞƚƵƌŶ Trip Frequency Weekly ϯ͘^ŝŶŐůĞ ϵ͘ZĞƚƵƌŶ

Monthly Twice ϰ͘^ŝŶŐůĞ ϭϬ͘ZĞƚƵƌŶ Trip Characteristics Monthly ϱ͘^ŝŶŐůĞ ϭϭ͘ZĞƚƵƌŶ

Occationally ϲ͘^ŝŶŐůĞ ϭϮ͘ZĞƚƵƌŶ If Day Return trip is "daily" Mention the no of return trips in 24 hrs. (for Frequency code No.7) 1. Work 2. Business 3. School / College/Education Purpose 4. Marriage / Functions / Social 5. Tourism 6. Hospital / Health 7. Other

97 Format for O-D Survey (Goods Vehicles)

ORIGIN & DESTINATION SURVEY (GOODS VEHICLES)

Name of Road : Road No : Direction: Location at Km: Surve y Duration 24 hours

1. LCV 2. 2-Axle Vehicle Particulars Type of Vehicle 3. 3-Axle 4. Multi Axle vehicle 5. Oversized Vehicles Place: Origin District: State: O-D Place: Destination District: State: Trip Length: Frequency

ZĞƚƵƌŶǁŝƚŚŝŶϮϰ,ŽƵƌƐ

Daily 1.Single 7. Return

Alternate Days 2.Single 8. Return Trip Frequency Weekly 3.Single 9. Return

Monthly Twice ϰ͘^ŝŶŐůĞ4.Single ϭϬ͘ZĞƚƵƌŶ10. Return

Monthly 5.Singleϱ͘^ŝŶŐůĞ ϭϭ͘ZĞƚƵƌŶ11. Return Occationally 6.Single 12. Return If Day Return trip is "daily", Mention the no of return trips in 24 hrs. (for Frequency N7) 1. Vegitables, Fruits, Meat, Milk Products (Perishable Products) 2. Rice, wheat, Pulses, Bajra, Jowar (Food Crops) Trip Characteristics & Other 3. Cotton,jute, Sugarcane, Tea, Tobaco (Cash Crops) Information 4.Electrical & Electronics, Vehicles, Medicines (Manifacturing Products ) 5.Cosumer Products (Daily Usable Items , soap, shampoo, parchun etc) 6. Chemical Products 7. Petrolium Products Commodity Type 8. Textile Product 9. Building Materials 10. Parcel & Paper Products 11. Wood & Forest Products 12. Machine & Machine Products 13. Metal, Mineral & Ores 14. Rubber & plastic Products 15. Miscellanious (Animals, Scrap, Glass, Bottles etc) 16. Empty

98 Zone List

Zone Lis t Zone N o Zone N ame District 1 Dharwad Dharwad 2 Navalur, Satturu, NavanagarSurrounding Dharwad 3 Hebbali, Shivahalli, Govankoppaareas of Dharwad Project 4 Hebsur,Ingalhalli, Shishvinhalli, Manekvad, KusugalCorridor Dharwad 5 Navalgund, shelwadi, Tadhal, Nargund Dharwad 6 Hubli Dharwad 7 Bhandiwad, Mantur, Shiraguppi Dharwad 8 Nalavadi Dharwad 9 Bhadrapur Dharwad 10 Annegeri Dharwad 11 Dundoor, Shagoti Gadag 12 Hulkoti, Binkadkatti Gadag Along the 13 Gadag Gadag Project 14 Adavi Sompur Gadag Corridor 15 Lakkundi, Harlapur Gadag 16 Bannikoppa, Talakal, Bhanapur Koppal 17 Halagere, Dadegal Koppal 18 Koppal Koppal 19 Basapur, Ginigera Koppal 20 Huligi, Munirabad Koppal 21 Hospet, Bellary 22 Mahadevaitagi, Koppal 23 Timmapur, Yarehanchinal, Binnal Gadag 24 Chikkenakoppa, Mandalagiri, Bhatapanahalli Koppal 25 Naregal , Ron Gadag 26 Thadakoda, Garag, Budaraghatti, Uppina Dharwad 27 Honnapur, Kalakeri, Holiyal Dharwad 28 Kalghatgi, Mishrikot, Honnalli, Ramanhal Dharwad 29 Kundgol, Saunshi, Gudgeri Dharwad Surrounding 30 Adargunchi, Katnur, Belagali, Varur, Chabbi, Aralikatti, Dharwad areas of 31 Lakshmeshwar, Magdi, Mulgund, Shirunj, SoraturProject Gadag 32 Nagarahalli, Gudenakatti, YaraguppiCorridor Gadag 33 Kurtakoti Gadag 34 Dambal Gadag 35 Mundargi, Kalikeri, Bidanahal, MundavadaBidaralli Gadag 36 Kavalur Koppal Gangawati, Budaghumpa, Ginigera, Komahalli, Erur, Kesaki, Challuru, Navali, Karatgi, 37 Juratgi, Siddapur, Liganur, Koppal 38 Kushtagi, Yelbarga, Rest of Koppal district Koppal 39 Bellary, Torangallu Bellary 40 Raichur District 41 Bagalkot District 42 Belgaum District 43 Karwar Port 44 Yellapur, Ankola, Rest of Uttar Kannad District Rest of Karnataka 45 Haveri District

46 Shimoga, Udupi, Chikmangalur, Dakshin Kannad, Kodagu, Mysore, Mandya, Chamrajnagar 47 Davangere, Chitradurga, Tumkur, Bangalore, Kolar, 48 Gulbarga, Bijapur, Bidapur 49 Andhra Pradesh 50 Maharashtra 51 Goa 52 Kerala 53 Tamil Nadu Rest of India 54 Orissa, Chattisgarh, Jharkhand, West Bengal, Bihar Gujarat, Rajasthan, Madhya Pradesh, Uttar Pradesh,, Delhi, Haryana, Punjab, Jammu & 55 Kashmir, Himachal Pradesh, Uttaranchal 56 East India: Assam, Sikkim, Arunachal Pradesh, Meghalaya, Nagaland, Mizoram, Manipur

99 Distance Matrix 0 825 2515 2850 0 1740 1422 30 1081 2845 0 1235 1810 2660 0 46 1535 1070 2862 680 1860 1900 3280 0 750 1540 1100 2865 0 790 930 1715 1100 2900 0 550 1336 1300 1655 530 2700 0 711 663 1087 630 1025 1150 2367 0 215 525 480 1030 735 1275 1021 2514 0 555 550 920 520 500 399 1457 1427 2930 0 154 685 710 1045 600 350 480 1600 1560 3022 0 375 265 430 520 650 254 720 665 1540 1160 2828 0 160 460 425 498 645 700 140 650 845 1670 1190 2940 0 31 183 490 450 520 670 670 110 680 850 1685 1170 2920 1 50 42520 265 498 154 430 685 555 90 460 375 0 10 140 254 600 520 480 663 550 50 845 665 480 399 735 630 1300 930 680 70 645 520 710 550 215 70 700 650 1045 920 525 711 80 650 720 350 500 1030 1087 1336 790 175 205 175 550 440 375 550 485 115 850 850 1575 995 2760 0 145 295 270 200 575 445 245 410 570 256 890 725 1425 1085 2650 0 205 350 465 440 315 525 390 165 210 680 460 870 575 1231 1180 2597 0 157 220 310 385 360 215 366 240 306 365 760 400 711 515 1300 1285 2720 0 130 155 85 205 305 280 185 460 330 250 365 630 310 805 650 1385 1160 2750 0 65 61 140 152 270 330 305 180 420 290 255 345 700 350 760 580 1335 1219 2735 0 80 80 130 215 135 210 270 245 105 425 300 310 425 665 275 765 650 1435 1175 2810 0 15 84 95 135 220 145 200 260 235 95 410 290 315 430 650 265 755 685 1445 1170 2815 0 20 25 92 90 145 230 115 180 250 225 110 425 305 320 440 640 272 775 675 1425 1145 2800 0 45 56 66 125 95 175 255 120 125 210 188 80 430 330 350 470 610 235 770 705 1490 1140 2750 0 30 71 85 95 151 125 202 281 121 130 190 167 80 440 350 350 500 610 211 795 750 1515 1130 2750 0 1140 1145 1170 1175 1219 1160 1285 1180 1085 995 1170 1190 1160 1560 1427 1021 1150 530 1100 1900 1810 1740 5 1490 1425 1445 1435 1335 1385 1300 1231 1425 1575 1685 1670 1540 1600 1457 1275 1025 1655 1715 1860 1235 0 35 31 62 56 72 143 120 195 275 150 150 200 175 60 400 310 345 485 620 230 745 730 1510 1135 2800 0 41 23 55 92 100 110 170 145 232 301 135 107 170 145 70 460 332 365 515 590 190 790 730 1540 1097 2765 0 12 30 12 45 85 90 100 160 135 225 295 135 120 180 156 70 455 340 365 510 600 200 800 735 1535 1105 2760 0 40 32 75 55 75 112 120 130 190 160 240 330 155 110 140 115 70 430 360 385 535 590 180 760 760 1560 1100 2780 0 50 60 50 90 80 90 120 140 150 205 175 264 345 155 70 155 145 111 490 375 400 550 550 145 795 775 1570 1060 2790 0 25 61 67 56 97 75 85 125 145 156 215 155 265 313 135 62 190 175 121 500 385 375 540 542 167 850 785 1560 1051 2770 0 105 115 110 90 92 78 77 55 50 65 65 110 52 166 210 70 155 246 225 125 485 365 295 415 600 256 820 700 1430 1110 2815 0 40 138 135 120 90 105 75 85 50 35 50 35 85 65 130 225 110 185 260 230 130 460 335 300 430 650 285 805 670 1440 1125 2815 5 35 140 135 120 85 100 75 80 50 31 50 40 85 60 130 220 111 183 255 230 130 460 335 300 430 650 280 800 670 1440 1125 2810 0 0 13 10 50 155 150 135 103 114 90 95 65 33 38 25 82 56 135 215 125 200 270 245 130 445 315 300 425 650 290 790 650 1430 1100 2790 0 65 70 68 115 200 190 175 145 160 125 140 105 80 65 65 50 65 65 190 150 245 311 290 150 390 260 290 395 700 335 734 580 1415 1220 2780 0 10 65 68 65 96 186 190 175 145 155 125 130 105 75 65 60 45 65 70 185 150 238 305 280 150 425 260 290 390 710 333 735 590 1375 1215 2780 0 20 27 46 51 48 80 177 175 155 127 140 105 115 90 57 48 43 36 55 90 175 135 235 290 267 140 410 275 275 380 685 316 750 615 1390 1200 2767 0 25 45 50 62 65 60 80 185 190 170 140 155 120 130 105 75 65 60 41 31 105 165 114 250 310 285 155 430 300 275 365 660 315 800 650 1380 1180 2855 0 30 15 30 40 30 36 33 65 155 158 140 112 125 90 100 75 42 37 33 50 60 102 192 138 218 278 255 130 458 290 280 397 675 285 770 625 1405 1210 2800 0 10 40 20 35 45 15 25 25 55 145 150 130 100 115 87 91 65 33 25 25 60 60 110 200 130 210 270 245 130 450 315 290 405 665 275 790 635 1415 1200 2845 0 40 48 78 60 73 83 40 25 28 40 109 112 95 65 75 50 55 25 20 30 40 98 80 145 235 110 170 230 205 105 440 315 322 440 620 235 785 670 1455 1165 2830 0 10 45 53 85 65 80 90 45 30 35 45 110 105 90 75 70 45 50 20 25 35 45 103 85 150 240 110 165 225 200 100 445 310 330 445 625 230 790 680 1465 1150 2835 0 6 13 50 59 88 73 90 100 50 36 39 39 97 100 85 55 65 45 50 15 25 40 50 110 80 160 245 105 163 225 200 100 440 330 333 450 610 225 785 680 1475 1150 2840 0 10 15 21 59 67 97 80 97 107 58 45 47 50 89 91 75 48 58 37 34 8 38 50 60 118 90 165 255 120 155 215 190 105 440 330 343 460 635 237 835 690 1483 1143 2847 90 15 20 25 63 72 100 85 100 111 60 48 50 55 85 85 70 43 55 40 31 10 42 55 65 122 95 170 260 110 150 210 185 110 445 330 340 465 630 232 830 690 1470 1135 2850 0 11 17 23 27 33 71 80 110 95 110 120 70 55 60 55 75 75 62 35 45 45 25 13 50 60 70 130 100 180 265 110 140 200 175 105 450 345 325 475 620 222 820 700 1480 1125 2840 60 17 23 30 33 39 65 85 115 100 115 125 75 60 65 65 80 70 56 30 40 40 20 20 55 65 75 135 105 195 273 116 135 195 170 102 467 350 330 480 615 217 815 710 1495 1120 2846 60 12 23 29 35 38 45 83 91 121 105 121 130 82 68 71 73 64 68 50 25 35 35 10 25 62 75 85 140 115 190 275 115 130 185 165 95 460 360 345 485 610 211 810 722 1500 1115 2841 8 14 20 30 32 41 45 52 90 98 128 113 128 138 89 75 78 80 66 62 45 26 27 41 10 32 70 80 90 148 120 200 286 120 123 180 158 90 455 350 350 491 600 205 805 750 1515 1112 2846 0 0 20 25 31 36 45 50 60 63 70 107 111 145 130 146 155 105 90 95 85 40 40 30 25 15 60 30 50 86 100 110 165 140 220 300 140 100 165 145 80 440 340 360 510 580 180 800 737 1530 1090 0 40 30 25 30 20 30 30 40 45 50 89 90 130 111 127 136 88 74 77 50 60 80 65 44 45 60 32 30 67 80 90 145 100 210 280 90 120 200 175 110 490 375 320 490 580 220 850 730 1500 1100 281 0 30 25 20 12 15 17 30 30 40 43 50 88 90 125 110 126 135 87 73 76 75 55 60 50 35 30 46 20 30 66 78 90 145 112 210 280 110 110 185 160 95 460 360 340 490 580 200 835 720 1515 1105 2 0 20 40 15 35 32 35 37 48 50 60 63 70 107 115 145 130 147 156 107 90 95 82 35 35 49 41 30 65 45 50 90 100 110 165 130 215 305 125 100 185 160 95 455 360 355 510 570 180 830 736 15 0 15 30 50 15 35 40 50 50 60 62 70 76 82 132 129 160 142 160 165 120 95 109 110 38 25 35 40 30 70 40 63 100 110 120 180 150 230 315 140 85 170 145 95 460 355 375 520 560 170 815 7 0 8 15 40 60 18 35 42 50 55 65 70 77 81 90 127 130 165 145 160 172 125 110 120 125 20 20 35 40 31 75 45 67 104 115 125 180 145 235 320 140 80 170 145 95 460 350 370 525 550 160 80 8 95 95 95 95 110 80 90 95 102 105 110 105 100 100 105 130 130 155 140 150 150 130 130 130 125 121 111 70 70 70 60 80 80 110 95 105 180 185 215 315 200 175 183 160 80 85 100 110 120 100 123 130 135 140 150 155 163 165 170 210 218 250 235 238 245 200 183 185 155 62 70 110 120 107 150 130 125 180 200 210 270 205 310 350 145 67 63 50 30 30 50 32 25 20 13 10 8 15 20 25 65 75 105 90 105 105 65 50 50 55 85 90 75 45 55 31 30 35 3540 49 40 50 41 65 35 30 44 45 25 50 26 56 25 62 30 70 35 75 43 85 48 90 55 95 75 130 65 140 100 170 112 155 140 175 127 175 145 135 145 120 103 120 85 110 61 90 50 90 67 60 40 31 3075 30 7045 30 65 40 45 46 45 15 60 20 27 60 32 35 41 30 40 35 10 45 40 10 55 45 20 58 40 25 65 37 31 70 45 34 75 45 50 115 50 50 125 87 55 155 140 90 91 155 120 100 160 105 130 114 125 115 100 125 130 105 140 90 92 95 75 56 80 75 50 85 78 32 77 97 12 75 90 80 75 55 30 12 41 23 35 90 82 70 50 50 70 52 45 39 33 25 21 13 10 20 38 35 55 60 40 66 64 80 75 85 89 97 110 109 145 155 185 177 186 200 155 140 138 105 15 15 60 50 40 30 40 30 20 20 25 35 60 80 40 62 68 70 75 85 91 100 105 112 150 158 190 175 190 190 150 135 135 115 25 35 35 35 20 30 20 18 15 15 25 40 81 76 63 43 45 63 45 38 33 27 20 15 6 77 70 60 40 40 60 41 35 30 23 15 10 70 62 50 30 30 50 32 29 23 17 9 42 4050 32 5055 12 35 5065 25 15 37 60 25 30 17 48 31 20 30 8 14 36 30 20 45 6 12 30 23 6 17 11 350 355 360 360 375 340 350 360 350 345 330 330 330 310 315 315 290 300 275 260 260 315 335 335 365 385 375 360 340 332 310 350 330 305 290 300 290 330 240 390 445 440 4 370 375 355 340 320 360 350 345 330 325 340 343 333 330 322 290 280 275 275 290 290 300 300 300 295 375 400 385 365 365 345 350 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760 735 730 730 750 705 675 685 650 580 650 515 575 725 850 8 525 520 510 490 490 510 491 485 480 475 465 460 450 445 440 405 397 365 380 390 395 425 430 430 415 540 550 535 510 515 485 500 470 440 430 425 345 365 365 210 410 550 6 550 560 570 580 580 580 600 610 615 620 630 635 610 625 620 665 675 660 685 710 700 650 650 650 600 542 550 590 600 590 620 610 610 640 650 665 700 630 760 680 570 485 6 140 140 125 110 90 140 120 115 116 110 110 120 105 110 110 130 138 114 135 150 150 125 111 110 70 135 155 155 135 135 150 121 120 115 145 135 152 85 220 205 320 315 305 280 280 300 286 275 273 265 260 255 245 240 235 200 192 165 175 185 190 215 220 225 210 313 345 330 295 301 275 281 255 230 220 215 140 155 157 145 150235 130 230 112 215 100 210 140 210 120 220 115 200 105 190 100 195 95 180 170 90 165 80 160 150 85 145 80 110 102 60 105 60 90 31 70 55 65 65 135 130 65 130 56 166 265 60 264 65 240 225 52 232 155 195 175 202 160 175 135 145 145 135 120 130 125 61 95 130 90 95 80 65 125 120110 107 95 87 90 88 73 105 89 74 82 90 75 75 70 68 60 60 58 55 50 48 45 45 40 36 15 30 30 25 62 25 46 36 65 65 65 51 68 70 13 160 160172 147 165 126 156 127 135 146 136 128 155 121 138 115 130 110 125 100 120 97 111 107 90 100 80 90 73 83 35 45 30 40 45 50 20 27 10 104 100 90 66 67 86 70 62 55 50 42 38 25 25 20 33 42 75 57 75 80 33 31 35 50 125 120 112 85 92 62 71 45 125 110 82 75 50 85 80 73 65 55 55 50 39 45 40 55 65 80 80 96 115 50 35 40 130 129 115 90 90 111 98 91 85 80 72 67 59 53 48 10 165 160145 145 142 125 130 130 110 145 111 128 130 121 113 115 105 110 100 100 95 97 85 88 80 85 73 78 65 40 60 30 20 15 25 120 109 95 76 77 95 78 71 65 60 50 47 39 35 28 25 33 60 48 65 68 10 5 125 120180 110 180 90 165 145 90 145 110 165 90 148 140 85 135 75 130 122 70 118 65 110 103 60 98 50 60 45 50 40 41 25 36 33 45 60 50 43 82 60 85 65 85 25 110 40 215 205 35 190 65 160 156 170 150 143 130 151 100 125 110 92 72 84 95 80 66 25 15 127 132 107 88 89 107 90 83 65 71 63 59 50 45 40 115 110 100 78 80 100 80 75 65 60 55 50 40 35 30 25 37 65 48 65 65 38 50 50 65 145 140 120 90 100 56 85 56 20 1100 1070 1081 1105 1100 1090 1112 1115 1120 1125 1135 1143 1150 1150 1165 1200 1210 1180 1200 1215 1220 1100 1125 1125 1110 1051 1060 1100 1105 1097 1135 113 1540 1535 1530 1515 1500 1530 1515 1500 1495 1480 1470 1483 1475 1465 1455 1415 1405 1380 1390 1375 1415 1430 1440 1440 1430 1560 1570 1560 1535 1540 1510 151 2 7 6 8 3 5 10 4 9 48 49 46 47 43 44 45 55 41 42 40 30 31 32 23 51 52 53 54 33 26 34 25 50 28 29 36 37 38 39 27 16 22 15 20 21 14 13 12 10 11 35 17 18 19 24 O/D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

100 Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ ϭ Ϯ ϭ Ϭ ϯ Ϭ Ϭ Ϭ ϱ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ ϯϰ ϭϱ ϬϬ ϬϬ ϬϬ ϬϬ ϬϬ Ϭϭ ϭϴ ϬϬ ϬϬ ϬϬ ϬϬ ϭϬ ϱ Ϭϯ Ϭϭ ϰ Ϭϭϭ ϳ Ϭ ϭϭϭ Ϭ ϯ Ϭϭ ϲ ϭ Ϭ Ϭ Ϭ ϯϰ ϯϱ ϯϲ ϯϳ ϯϴ ϯϵ ϰϬ ϰϭ ϰϮ ϰϯ ϰϰ ϰϱ ϰϲ ϰϳ ϰϴ ϰϵ ϱϬ ϱϭ ϱϮ ϱϯ ϱϰ ϱϱ ϱϲ 'ƌĂŶĚ Ϭ Ϭ Ϭ Ϭ ϭϬϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ Ϭ ϭϭϬϮ Ϭ Sample Pivot Table for Govt. Buses At Nalavadi Nalavadi At Buses for Govt. Table Pivot Sample Ϭ ϬϬϭϬϬϭϬϭϰϬϬϬϵϬϬϬϮϬϬϭϰϭϳϭϬϭϯϯϬϬϬϯ ϬϬϬϬϬϭϱϬϬϬϬϭϮϬϭϭϬϰϬϬϬϬϯϬϬϵϬϬϬϬϬϬϬϮϬϬϬϬϬϭϬϰϭϭϬϳϮϭϯϬϬϭϮϯϱϯϭϬϬϬϬϬ Ă Ŷ ƚ ϮϬ ϯϬ ϰϬ ϱϬ ϲϳϬ ϴϬϭ Ϭ ϮϬ ϭ ϭϬϭ ϯ ϳ ϭ ϭ ϴ ϳ Ϯ ϯ ϱ ϭϱϵ ϵϬ ϭϬ ϭ ϯϵϰϬϰϭ ϱ ϯ ϯ ϱϮ ϱϯ ϱϰ ϱϲ ϰϮ ϰϰ ϰϱ ϰϲ ϰϳ ϰϴ ϰϵϱϬϱϭϱϱ ϰ ϭ ϭ ϭ ϭ Ϭ ϯ ϰϯ ϭϬϭϭϭϮϭϯϭϰϭϱϭϳϭϴϭϵ ϭϱ ϯϮϯ ϵϵϮϰ Ϯϱ Ϯϲ Ϭ Ϭ ϱ Ϭ ϯϯ Ϭϯϰ ϯϲ Ϭ ϯϳϯϴ Ϭ Ϭ Ϭ ϱ ϭ ϭ Ϯ Ϯ ϭ ϭϬϱ ϭϲϮϬ ϯϱ Ϭ ϮϭϮϮ ϳ Ϭ Ϯϵ ϯϬ ϯϭ ϯϮ Ϯϳ Ϯϴ 'ƌĂŶĚdŽ ^ƵŵŽĨ ĞƐƚŝŶĂ ϭKƌŝŐŝŶ Ϯ ϯ ϰ ϱ ϲ ϳ ϴ ϵϭϬϭϭϭϮϭϯϭϰϭϱϭϲϭϳϭϴϭϵϮϬϮϭϮϮϮϯϮϰϮϱϮϲϮϳϮϴϮϵϯϬϯϭϯϮϯϯ

101 102