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Optimizing Bike Sharing Systems: Dynamic Prediction Using Machine Learning and Statistical Techniques and Rebalancing Optimizing Bike Sharing Systems: Dynamic Prediction Using Machine Learning and Statistical Techniques and Rebalancing Mohammed Hamad Almannaa Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil Engineering Hesham A. Rakha, Chair Mohammed M. Elhenawy Feng Guo Ralph Buehler Montasir M. Abbas April 22nd, 2019 Blacksburg, VA Keywords: Dynamic Linear and Incremental Learning Models, Bike Sharing System, Bike Count Prediction, Machine Learning and Statistical algorithms and models, Portable Bike Stations Copyright © 2019, Mohammed Hamad Almannaa i Optimizing Bike Sharing Systems: Dynamic Prediction Using Machine Learning and Statistical Techniques and Rebalancing Mohammed Hamad Almannaa ABSTRACT The large increase in on-road vehicles over the years has resulted in cities facing challenges in providing high-quality transportation services. Traffic jams are a clear sign that cities are overwhelmed, and that current transportation networks and systems cannot accommodate the current demand without a change in policy, infrastructure, transportation modes, and commuter mode choice. In response to this problem, cities in a number of countries have started putting a threshold on the number of vehicles on the road by deploying a partial or complete ban on cars in the city center. For example, in Oslo, leaders have decided to completely ban privately-owned cars from its center by the end of 2019, making it the first European city to totally ban cars in the city center. Instead, public transit and cycling will be supported and encouraged in the banned-car zone, and hundreds of parking spaces in the city will be replaced by bike lanes. As a government effort to support bicycling and offer alternative transportation modes, bike- sharing systems (BSSs) have been introduced in over 50 countries. BSSs aim to encourage people to travel via bike by distributing bicycles at stations located across an area of service. Residents and visitors can borrow a bike from any station and then return it to any station near their destination. Bicycles are considered an affordable, easy-to-use, and, healthy transportation mode, and BSSs show significant transportation, environmental, and health benefits. As the use of BSSs have grown, imbalances in the system have become an issue and an obstacle for further growth. Imbalance occurs when bikers cannot drop off or pick-up a bike because the bike station is either full or empty. This problem has been investigated extensively by many researchers and policy makers, and several solutions have been proposed. There are three major ways to address the rebalancing issue: static, dynamic and incentivized. The incentivized approaches make use of the users in the balancing efforts, in which the operating company incentives them to change their destination in favor of keeping the system balanced. The other two approaches: static and dynamic, deal with the movement of bikes between stations either during or at the end of the day to overcome station imbalances. They both assume the location and number of bike stations are fixed and only the bikes can be moved. This is a realistic assumption given that current BSSs have only fixed stations. However, cities are dynamic and their geographical and economic growth affects the distribution of trips and thus constantly changing BSS user behavior. In addition, work-related bike trips cause certain stations to face a high-demand level during weekdays, while these same stations are at a low-demand level on weekends, and thus may be of little use. Moreover, fixed stations fail to accommodate big events such as football games, holidays, or sudden weather changes. This dissertation proposes a new generation of BSSs in which we assume some of the bike stations can be portable. This approach takes advantage of both types of BSSs: dock-based and dock-less. Towards this goal, a BSS optimization framework was developed at both the tactical and operational level. Specifically, the framework consists of two levels: predicting bike counts at stations using fast, online, and incremental learning approaches and then balancing the system ii using portable stations. The goal is to propose a framework to solve the dynamic bike sharing repositioning problem, aiming at minimizing the unmet demand, leading to increased user satisfaction and reducing repositioning/rebalancing operations. This dissertation contributes to the field in five ways. First, a multi-objective supervised clustering algorithm was developed to identify the similarity of bike-usage with respect to time events. Second, a dynamic, easy-to-interpret, rapid approach to predict bike counts at stations in a BSS was developed. Third, a univariate inventory model using a Markov chain process that provides an optimal range of bike levels at stations was created. Fourth, an investigation of the advantages of portable bike stations, using an agent-based simulation approach as a proof-of-concept was developed. Fifth, mathematical and heuristic approaches were proposed to balance bike stations. iii Optimizing Bike Sharing Systems: Dynamic Prediction Using Machine Learning and Statistical Techniques and Rebalancing Mohammed Hamad Almannaa GENERAL AUDIENCE ABSTRACT Large urban areas are often associated with traffic congestion, high carbon mono/dioxide emissions (CO/CO2), fuel waste, and associated decreases in productivity. The estimated loss attributed to missed productivity and wasted fuel increased from $87.2 to $115 between 2007 and 2009. Driving in congested areas also results in long trip times. For instance, in 1993, drivers experienced trips that were 1.2 min/km longer in congested conditions. As a result, commuters are encouraged to leave their cars at home and use public transportation modes instead. However, public transportation modes fails to deliver commuters to their exact destination. Users have to walk some distance, which is commonly called the “last mile”. Bike sharing systems (BSSs) have started to fill this gap, offering a flexible and convenient transportation mode for commuters, around the clock. This is in addition to individual financial savings, health benefits, and reduction in congestion and emissions. Resent reports have shown BSSs multiplying over 50 countries. This notable expansion of BSSs also brings daily logistical challenges due to the imbalanced demand, causing some stations to run empty while others become full. Rebalancing the bike inventory in a BSS is crucial to ensure customer satisfaction and the whole system’s effectiveness. Most of the operating costs are also associated with rebalancing. The current rebalancing approaches assume stations are fixed and thus don’t take into account that the demand changes from weekday to weekend as well as from peak to non-peak hours, making some stations useless during specific days of the week and times of day. Furthermore, cities change continually with regard to demographics or structures and thus the distribution of trips also changes continually, leading to re-installation of stations to accommodate the dynamic change, which is both impractical and costly. In this dissertation, we propose a new generation of BSS in which we assume some stations are portable, meaning they can move during the day. They can be either stand-alone or an extension of existing stations with the goal of accommodating the dynamic changes in the distribution of trips during the day. To implement our new BSSs, we developed a BSS optimization framework. This framework consists of two components: predicting the bike counts at stations using fast approaches and then balancing the system using portable stations. The goal is to propose a framework to solve the dynamic bike sharing repositioning problem, aiming at minimizing the unmet demand, leading to increased user satisfaction and reducing repositioning/rebalancing operations. This dissertation contributes to the field in five ways. First, a novel algorithm was developed to identify the similarity of bike-usage with respect to time events. Second, easy-to-interpret and rapid approaches to predict bike counts at stations in a BSS were developed. Third, an inventory model using statistical techniques that provide an optimal range of bike levels at stations was created. Fourth, an investigation of the advantages of portable bike stations was developed. Fifth, mathematical approach was proposed to balance bike stations. iv DEDICATION To my parents (Hamad and Haya), my wife (Asmaa), and my two little daughters (Maysaan and Kayaa v ACKNOWLEDGEMENTS All praise is due to Allah, we thank Him, seek His guidance and forgiveness. I’m extremely thankful to my family: my parents (Hamad and Haya), my wife (Asmaa), my brothers (Majed, Zyad, and Othman), and my lovely sisters (Eman and Huda) for their love, affection, and emotional support throughout my undergraduate and graduate career. I would like to express my sincere gratitude to my advisory committee for their outstanding guidance, continuous support, never-ending patience, and friendly advice during my PhD journey: Dr. Hesham Rakha, Dr. Mohammed Elhenawy, Dr. Feng Guo, Dr. Ralph Buhler, and Dr. Montasir Abbas. I would like to give special thanks to my adviser, Dr. Hesham Rakha, for his continuous help, constructive guidance, and great encouragement. My sincere appreciation also goes to Dr. Mohammed Elhenawy for his mentoring during my PhD research. I have learned greatly from his knowledge in artificial intelligence and machine learning fields. My thanks also go to Dr. Feng Guo for his invaluable and immense knowledge in statistical modeling that I have benefited from during my journey. I must acknowledge the great input from Dr. Ralph Buehler in the planning side of this research. My sincere thanks also go to my sponsor, King Saud University in Riyadh, Saudi Arabia, for funding me since I arrived in the United States of America in 2012 until writing this dissertation. During the past years, I have been blessed with a supportive and cheerful group of colleagues in the Center for Sustainable Mobility at the Virginia Tech Transportation Institute; in particular: Dr.
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