The 5th NYUAD Transportation Symposium

Bios and Abstracts

• Christopher Tang

  Bio: Christopher Tang is a University Distinguished Professor and the holder of the Edward W. Carter Chair in Business Administration at the UCLA Anderson School of Management. Known as a world renowned thought leader in global supply chain management, Chris consulted with numerous global companies including Amazon, HP (California, Singapore, South Korea), IBM (New York, San Jose), Nestlé (USA), GKN (UK), Accenture, etc.; taught courses at Stanford University, UC Berkeley, Hong Kong University of Science and Technology, National University of Singapore, MIT (Zaragoza), and London Business School. Chris has published 6 books, 30 book chapters, over 100 online blogs, and over 160 research articles in various leading academic journals, and written articles for Wall Street Journal, Financial Times, Fortune, Los Angeles Times, etc. He was elected as lifetime fellow by the Institute of Operations and Management Sciences (INFORMS), the Production and Operations Management Society (POMS), and the Manufacturing and Service Operations Management Society (MSOM). He received his B.Sc. (First class honours in Mathematics) from King’s College, London, M.A (in Statistics), M.Phil (in Administrative Science), and PhD (in Management Science) from Yale University.

  Title: Managing Global Supply Chains in the Industry 4.0 Era
  Abstract: Industry 4.0 creates a “force” to make companies to rethink the way they manage their supply chain. In this presentation, I shall examine the following questions. How should companies leverage various evolving technologies (Additive Manufacturing, Advanced Robotics, Artificial Intelligence, Autonomous Vehicles, Blockchain, Drones, Internet of Things, etc.) to improve their supply chain competitiveness? What kind of value (economic, social and environmental) can these technologies bring? What are the potential pitfalls?

• Elisabetta Cherchi

  Bio: Elisabetta Cherchi is Professor at the School of Engineering, Future Mobility Group, Newcastle University and Adjunct Professor, School of Economics and Management, Beijing Jiaotong University, China. She is co-Editor in Chief of Transportation Research Part A: Policy and Practise and past Associate Editor of Transportation, as well as member of the editorial board of Transport Policy, Journal of Choice Modelling, and Transportation Letter and past member of the editorial board of Transportation Research Part B. She is the Chair of the International Association for Travel Behaviour Research (IATBR), as well as past Secretary and Treasurer of the IATBR. She is member of the ADB10 TRB committee on Travel Behavior and Value and past member of the ADB40 TRB committee on Transportation Demand Forecasting and ADB50 on Transportation Planning Applications. Her research interest is in data collection, demand modelling, behavioural background of how individuals take decision and in exploring new ways to elicit and model the complexity of individual behaviour especially for emerging problems such as understanding what drives sustainable transport behaviour and how it can be promoted.

  Title: Estimation and forecast of travel demand for innovative transport systems: challenges and new methods
  Abstract: The advent of the new technology, from the simple smartphone to the electric vehicles, autonomous and connected vehicles, smart city, the Internet of things, is transforming beyond recognition the way we interact among us and with the surrounding environment and the way we move. In this complex system, understanding, modelling and forecasting the travel demand represents one of the key research challenges of our time. Disaggregate demand models typically used in transport are estimated for a specific context and assume that the demand curve is stable (i.e., individual preferences do not vary). These models are then suitable to forecast demand in relatively stable markets based on variation of transport characteristics, but show limitations in the case of innovative transport systems. In this presentation I will discusses and provide evidences about two key challenges related to the estimation and forecast of travel demand for innovative transport systems. First, since consumers do not have preferences for products that they do not know and have not had experience with, traditional data collected methods cannot be used to study the demand for innovative transport systems, as they fail to capture underlying preferences that have not been constructed yet. I will discuss the use of Virtual Reality (VR) experiments as methods to allow respondents to acquire experience and form preferences. Second, innovations typically penetrate the new market through certain channels over time among members of a social system, an effect that cannot be properly accounted for with typical demand models. I will discuss the challenges of measuring the impact of the social system in the preference for innovations and methods to account for the diffusion process in the forecast of the demand for innovative transport systems. Examples of applications will be provided in the context of Electric Vehicles and Automated Vehicles.

• Hani Mahmassani

  Bio: Dr. Hani S. Mahmassani holds the William A. Patterson Distinguished Chair in Transportation at Northwestern University, where he is Director of the Northwestern University Transportation Center, and Professor in Civil and Environmental Engineering, with joint appointments in Industrial Engineering and Management Sciences, and Managerial Economics and Decision Sciences in the Kellogg School of Management. Prior to Northwestern, he served on the faculties of the University of Maryland and the University of Texas at Austin. He has over 35 years of professional, academic and research experience in the areas of intelligent transportation systems, freight and logistics systems, multimodal systems modeling and optimization, pedestrian and crowd dynamics and management, traffic science, demand forecasting and travel behavior, and real-time operation of transportation and distribution systems. He has served as principal investigator on over 150 funded research projects sponsored by international, national, state, and metropolitan agencies and private industry. He is past editor-in-chief and current associate editor of Transportation Science, senior editor of IEEE Transactions on Intelligent Transportation Systems, and founding associate editor of Transportation Research C: Emerging Technologies. He is a past president of the Transportation Science Section of the Institute for Operations Research and the Management Sciences, and a past President of the International Association for Travel Behavior Research. He serves on the Board of Advisors to the Panama Canal Authority, and the Prince of Monaco’s Smart Cities Advisory Council. He was the recipient of a Distinguished Alumnus Award of the Faculty of Engineering and Architecture of the American University of Beirut in 2006, the Intelligent Transportation Systems Outstanding Application Award of the Institute of Electrical and Electronics Engineers in 2010, and the Transportation Research Board’s Thomas Deen Distinguished Lectureship in 2016. He was elected Emeritus member of the Transportation Research Board (of the National Academies) committees on Telecommunications and Travel Behavior, Transportation Network Modeling Committee, and Traveler Behavior and Values. Mahmassani received his PhD from the Massachusetts Institute of Technology in transportation systems and MS in transportation engineering from Purdue University.

  Title: Predictive Flow Management in Connected Autonomous Vehicle Systems: Machine Learning Application
  Abstract: Most flow management strategies from the Intelligent Transportation Systems (ITS) era rely on data from sensors at fixed locations, and provide control information (eg. speed limits, detour information) via installed devices, also at fixed locations, that display the same information to all vehicles at the same time. Connected vehicle systems hold the promise of providing individual-level information via real-time vehicle trajectories, enabling complete characterization of prevailing spatio-temporal flow conditions. They also allow provision of differentiated control information to vehicles at any location. Furthermore automated capabilities increase the controllability of the system via individual speed control. The potential of connected and automated vehicle (CAV) information is illustrated using predictive speed harmonization, which is an active traffic management strategy used for delaying onset of flow breakdown and mitigating congestion by changing speed limits throughout a roadway segment. The system utilizes detailed vehicle trajectories broadcasted by CAVs, and machine learning techniques to predict the location of traffic congestion. Two types of CAV control strategies are developed: centralized and decentralized. The centralized system relies on a traffic management center to collect data from CAVs within a road segment of interest, predict traffic congestion, and broadcast updated speed limits to CAVs in order to mitigate congestion. The decentralized system relies on individual CAVs to collect data through communicating with each other, predict traffic congestion using vehicle-specific models, and update their speed limits to mitigate congestion. Case studies of multiple operational scenarios show that both systems can reduce the severity and lengths of traffic shockwaves, improve the overall traffic stability, increase overall speed, and reduce travel time.

• Jorge Laval

  Bio: Jorge Laval is an Associate Professor at the School of Civil and Environmental Engineering, Georgia Institute of Technology since 2006. After obtaining his B.S. in Civil and Industrial Engineering from Universidad Catolica de Chile in 1995, Dr. Laval worked as a transportation engineer for 5 years at the Chilean Ministry of Public Works in Santiago, Chile. He received his Ph.D. in Civil Engineering from the University of California, Berkeley in 2004. Prior to joining Georgia Tech, Dr. Laval held two consecutive one-year postdoctoral positions at the Institute of Transportation Studies at UC Berkeley, and at the French National Institute for Safety and Transportation Research (INRETS/ENTPE). Professor Laval's main research thrust is in the area of traffic flow theory, modeling and simulation, focusing in understanding congestion in urban networks and how to manage it. He has made important contributions towards understanding the capacity of freeways, the connection between driver behavior and stop-and-go waves, freeway ramp-metering strategies, dynamic traffic assignment and congestion pricing.

  Title: Machine Learning Methods Beware: There is Nothing to Learn about Congested Urban Networks
  Abstract: The recent rise of artificial intelligence and machine learning methods offers new alternatives to tackle the elusive problem of controlling congested urban networks. The talk discusses the case of controlling large signalized networks and show that i) near-optimal policies can be obtained using random search or supervised learning, but not with deep reinforcement learning (DRL) methods, and ii) DRL methods cannot learn under congestion. We conjecture that these results are a consequence of the “congested property of urban networks”, whereby the network flow tends to be independent of the signal control policy under congestion; i.e. there is nothing to learn under congestion. We identify four traffic states for urban networks, each with unique features that can be exploited for better control. Our findings imply that it is advisable for current DRL methods in the literature to discard any congested data when training, and that doing this will improve their performance under all traffic conditions. They also suggest that future control methods based on machine learning techniques will have to be adapted to cope with the macroscopic properties of urban networks, which are beginning to be understood.

• Michel Bierlaire

  Bio: Born in 1967, Michel Bierlaire holds a PhD in Mathematical Sciences from the Facultés Universitaires Notre-Dame de la Paix, Namur, Belgium (University of Namur). Between 1995 and 1998, he was research associate and project manager at the Intelligent Transportation Systems Program of the Massachusetts Institute of Technology (Cambridge, Ma, USA). Between 1998 and 2006, he was a junior faculty in the Operations Research group ROSO within the Institute of Mathematics at EPFL. In 2006, he was appointed associate professor in the School of Architecture, Civil and Environmental Engineering at EPFL, where he became the director of the Transport and Mobility laboratory. Since 2009, he is the director of TraCE, the Transportation Center. From 2009 to 2017, he was the director of Doctoral Program in Civil and Environmental Engineering at EPFL. In 2012, he was appointed full professor at EPFL. Since September 2017, he is the head of the Civil Engineering Institute at EPFL. His main expertise is in the design, development and applications of models and algorithms for the design, analysis and management of transportation systems. Namely, he has been active in demand modeling (discrete choice models, estimation of origin-destination matrices), operations research (scheduling, assignment, etc.) and Dynamic Traffic Management Systems. As of October 2019, he has published 123 papers in international journals, 4 books, 39 book chapters, 184 articles in conference proceedings, 163 technical reports, and has given 191 scientific seminars. His ISI h-index is 31. His Google Scholar h-index is 61. He is the founder, organizer and lecturer of the EPFL Advanced Continuing Education Course "Discrete Choice Analysis: Predicting Demand and Market Shares". He is the founder of hEART: the European Association for Research in Transportation. He is the Editor-in-Chief of the EURO Journal on Transportation and Logistics. He is an Associate Editor of Operations Research. He is the editor of two special issues for the journal Transportation Research Part C. He has been member of the Editorial Advisory Board (EAB) of Transportation Research Part B since 1995, of Transportation Research Part C since January 1, 2006.

  Title: Scheduling of daily activities: an optimization approach" by J. Pougala, T. Hillel and M. Bierlaire
  Abstract: Modern mobility systems require a detailed representation of travel demand. Activity-based models provide a powerful tool to derive travel demand from daily activity patterns. In this presentation, we propose a modeling approach built on first principles. We assume that a traveler is scheduling her day in order to maximize her utility. Therefore, she has to solve a mixed integer optimization problem. Some decision variables are discrete, such as binary variables capturing the participation in activities (or not), and some are continuous, such as the duration of activities. We propose a detailed specification of the optimization problem, and illustrate it on concrete examples.

• Ming Hu

  Bio: Ming Hu is a professor of operations management at the Rotman School, University of Toronto. He was named as one of Poets & Quants Best 40 Under 40 business school professors in 2018. His research has been featured in mainstream media including the Financial Times. Most recently, his research has focused on operations management in the context of the sharing economy, social buying, crowdfunding, crowdsourcing, and two-sided markets, with the goal to exploit operational decisions to the benefit of the society. He recently edited a book titled Sharing Economy: Making Supply Meet Demand on operations management in the age of the sharing economy. He is the recipient of Wickham Skinner Early-Career Research Accomplishments Award by the POM Society (2016) and Best Operations Management Paper in Management Science Award by INFORMS (2017). He currently serves as the editor-in-chief of Naval Research Logistics, editor of a special issue of Manufacturing & Service Operations Management on sharing economy and innovative marketplaces, department editor of Service Science, associate editor of Operations Research and Manufacturing & Service Operations Management, and senior editor of Production and Operations Management. Starting November 2019, he will be serving as the Chair of Revenue Management and Pricing Section at INFORMS.

  Title: A Neoclassical View on Ride-Hailing
  Abstract: We take a neoclassical perspective by drawing inspiration from the classical models in queueing, revenue management, and stochastic inventory theory. We aim at building connections between those traditional models and the new application of ride-hailing markets. First, we adopt queueing theory to capture the spatial movements of vehicles in a centralized ride-hailing system. We show that both temporal pooling and spatial pooling can resolve the so-called “wild goose chase phenomenon,” i.e., long pickup time wasted on the road. Second, we extend the one-sided pricing problem studied in revenue management to a two-sided pricing problem faced by a ride-hailing platform. We show the latter problem is fundamentally different from the traditional unconstrained supply chain setting (i.e., the one-sided pricing) and the two-sided pricing in the economics literature. Third, we extend the classical inventory rationing problem to a dynamic matching problem in which supply (e.g., drivers) and demand (e.g., riders) of various types (e.g., at different locations) arrive in random quantities. Under some conditions, the optimal matching policy satisfies a structural property of “priority and thresholds,” which is a generalization of priority structures seen in the balanced and deterministic transportation problems, and the threshold-type policies seen in the inventory management (such as base-stock levels) and quantity-based revenue management (such as protection levels).

• Zhu Joe

  Bio: Joe Zhu is Professor of Operations Analytics in the Foisie Business School, Worcester Polytechnic Institute. He is an internationally recognized expert in methods of performance evaluation and benchmarking using Data Envelopment Analysis (DEA). With more than 29,000 Google Scholar citations, he is recognized as one of the top authors in DEA with respect to research productivity, h-index, and g-index. In 2017, he is ranked No. 3 among the most productive and influential authors in 40 years of European Journal of Operational Research. His research has been supported by KPMG Foundation, National Institute of Health, and Department of Veterans Affairs. He has extensive editorial experience, as an Area Editor of OMEGA, Editorial Board Member of European Journal of Operational Research, and Computers and Operations Research, Associate Editor of Journal of the Operational Research Society, and INFOR, Series Associate Editor of Springer's International Series in Operations Research and Management Science, and others. He has published over 140 peer-reviewed articles in Operations Research, European Journal of Operational Research, Journal of the Operational Research Society, IIE Transactions, Management Science, Sloan Management Review, Annals of Operations Research, OMEGA, International Journal of Production Economics, Naval Research Logistics, and others. He is a Japan Society for Promotion of Science (JSPS) fellow, a William Evans Visiting Fellow (University of Otago, New Zealand), Feng Tay Chair Professor (National Yunlin University of Technology and Science, Taiwan), and Chang Jiang Scholar Chair Professor awarded by the Ministry of Education of China

  Title: Big Data Applications in Intelligent Transportation Systems
  Abstract: The evolution of availability of a large amount of data in the Intelligent Transportation System (ITS) results in that Big data becomes a research focus in the field. Big data has increasingly received attention in both the academic and industrial fields of ITS. Big data in ITS has wide range applications including but not limited to signal recognition, traffic flow, travel time planning, travel route planning. This survey aims to provide a bibliography and a comprehensive review of the application of big data in the context of ITS. We review more than 600 papers over the past two decades from both application and methodology perspective. This study provides a deep insight into applications of Big data models in ITS, revealing different areas of those applications and integrating models and applications. The result of the study identifies research gaps and direction for the future.