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Dynamic Congestion Pricing and Highway Space Inventory Control System

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Dynamic Congestion Pricing and Highway Space Inventory Control System DYNAMIC TRAVEL DEMAND MANAGEMENT STRATEGIES: Dynamic Congestion Pricing and Highway Space Inventory Control System by Praveen Kumar Edara Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Civil Engineering Dušan Teodorovi ć, Chair Antonio Trani Konstantinos Triantis Paul Schonfeld John Collura September 2005 Falls Church, Virginia Keywords: Demand Management, Congestion Pricing, Dynamic Programming, Fuzzy Sets, Revenue Management, Artificial Neural Networks Copyright 2005, Praveen Edara DYNAMIC TRAVEL DEMAND MANAGEMENT STRATEGIES: Dynamic Congestion Pricing and Highway Space Inventory Control System Praveen Kumar Edara ABSTRACT The number of trips on highways and urban networks has significantly increased in the recent decades in many cities across the world. At the same time, the road network capacities have not kept up with this increase in travel demand. Urban road networks in many countries are severely congested, resulting in increased travel times, increased number of stops, unexpected delays, greater travel costs, inconvenience to drivers and passengers, increased air pollution and noise level, and increased number of traffic accidents. Expanding traffic network capacities by building more roads is extremely costly as well as environmentally damaging. More efficient usage of the existing supply is vital in order to sustain the growing travel demand. Travel Demand Management (TDM) techniques involving various strategies that increase the travel choices to the consumers have been proposed by the researchers, planners, and transportation professionals. TDM helps create a well balanced, less automobile dependent transportation system. In the past, several TDM strategies have been proposed and implemented in several cities around the world. All these TDM strategies, with very few exceptions, are static in nature. For example, in the case of congestion pricing, the toll schedules are previously set and are implemented on a daily basis. The amount of toll does not vary dynamically, with time of day and level of traffic on the highway (though the peak period tolls are different from the off-peak tolls, they are still static in the sense that the tolls don’t vary continuously with time and level of traffic). The advent of Electronic Payment Systems (EPS), a branch of the Intelligent Transportation Systems (ITS), has made it possible for the planners and researchers to conceive of dynamic TDM strategies. Recently, few congestion pricing projects are beginning to adopt dynamic tolls that vary continuously with the time of day based on the level of traffic (e.g. I-15 value pricing in California). Dynamic TDM is a relatively new and unexplored topic and the future research attempts to provide answers to the following questions: 1) How to propose and model a Dynamic TDM strategy, 2) What are the advantages of Dynamic TDM strategies as compared to their Static counterparts, 3) What are the benefits and costs of implementing such strategies, 4) What are the travel impacts of implementing Dynamic TDM strategies, and 5) How equitable are the Dynamic TDM strategies as compared to their Static counterparts. This dissertation attempts to address question 1 in detail and deal with the remaining questions to the extent possible, as questions 2, 3, 4, and 5, can be best answered only after some real life implementation of the proposed Dynamic TDM strategies. Two novel Dynamic TDM strategies are proposed and modeled in this dissertation – a) Dynamic Congestion Pricing and b) Dynamic Highway Space Inventory Control System. In the first part, dynamic congestion pricing, a real-time road pricing system in the case of a two-link parallel network is proposed and modeled. The system that is based on a combination of Dynamic Programming and Neural Networks makes “on-line” decisions about road toll values. In the first phase of the proposed model, the best road toll sequences during certain time period are calculated off-line for many different patterns of vehicle arrivals. These toll sequences are computed using Dynamic Programming approach. In the second phase, learning from vehicle arrival patterns and the corresponding optimal toll sequences, neural network is trained. The results obtained during on-line tests are close to the best solution obtained off-line assuming that the arrival pattern is known. Highway Space Inventory Control System (HSICS), a relatively new demand management concept, is proposed and modeled in the second half of this dissertation. The basic idea of HSICS is that all road users have to make reservations in advance to enter the highway. The system allows highway operators to make real-time decisions whether to accept or reject travellers' requests to use the highway system in order to achieve certain system-wide objectives. The proposed HSICS model consists of two modules – Highway Allocation System (HAS) and the Highway Reservation System (HRS). The HAS is an off-line module and determines the maximum number of trips from each user class (categorized based on time of departure, vehicle type, vehicle occupancy, and trip distance) to be accepted by the system given a pre-defined demand. It develops the optimal highway allocations for different traffic scenarios. The “traffic scenarios-optimal allocations” data obtained in this way enables the development of HRS. The HRS module operates in the on-line mode to determine whether a request to make a trip between certain origin-destination pair in certain time interval is accepted or rejected. ACKNOWLEDGEMENTS I was highly fortunate to work with great group of professors and students at Virginia Tech since Fall 2002. I am greatly indebted to my advisor Dr. Dusan Teodorovic for his ideas and support through out my M.S. and Ph.D. studies. I cannot remember a single instance in the past three years when I knocked on his door and he was not available for discussion about my research. He was always accessible and highly motivating. I am very thankful to Dr. Antonio Trani without whom I would not have joined Virginia Tech. His support through my graduate studies was very instrumental for my success at Virginia Tech. I express my gratitude to Dr. Konstantinos Triantis for all the support he had extended to me in the past few years. His research meetings were always very thought provoking and intellectually stimulating. His comments and suggestions on an earlier draft have greatly helped in improving the quality of this version of the dissertation. I would like to thank Dr. John Collura for his ideas on my research that helped me improve my dissertation. He was very encouraging and kind to me during my stay at Virginia Tech. I am very grateful to Dr. Paul Schonfeld for his valuable comments and suggestions on my dissertation proposal that helped me write a better dissertation. I also want to thank him for his efforts to attend my preliminary exam, and defense even though he was situated in Maryland. v I am also highly indebted to Dr. Joe Bared of the Federal Highway Administration for providing financial support through out my Ph.D. studies. He was very kind and understanding towards me, and always reminded me that school duties should be my first priority. My stay at Virginia Tech would not have been so much fun without my friends Junhyuk Kim, Phaneendra Piratla, Sai Krovvidi, Vishal Kothari, Prajwal Manalwar, Chintan Sheth, and many more. Thank you all for the memorable times. Finally, I am grateful to my parents for their unconditional love and sacrifices they have made for my education; and to my brother and sister-in-law for their love, understanding, encouragement, and support. vi TABLE OF CONTENTS ACKNOWLEDGEMENTS................................................................................................ v LIST OF FIGURES ........................................................................................................... ix LIST OF TABLES.............................................................................................................. x 1. INTRODUCTION .......................................................................................................... 1 1.1. Research Motivation ................................................................................................ 6 1.2. Research Goal .......................................................................................................... 8 1.3. Organization of Dissertation.................................................................................... 9 2. LITERATURE REVIEW ............................................................................................. 11 2.1. Congestion Pricing................................................................................................. 11 2.2. Fuzzy Set Theory and Route Choice ..................................................................... 13 2.3. Artificial Neural Networks .................................................................................... 16 2.3.1. Introduction..................................................................................................... 16 2.3.2. Characteristics of neural networks.................................................................. 17 2.3.3. Training of a neural network........................................................................... 19 2.3.4. Sample calculations .......................................................................................
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