Lecture 1.
Title:The Growing Nexus between Computational Data Science and
Transportation Science: The Excitement and the Challenges
Lecturer:Professor Chandra Bhat，The University of Texas at Austin
Time:2019.7.1 9:00-10:00
Location:A949
Lecture 2.
Time:Modeling Empty Miles in Ride-sourcing Systems
Lecturer: University of Michigan, Ann Arbor
Time:2019.7.1 10:00-11:00
Location：A949
Lecture 3.
Time:Last train scheduling for maximizing passenger destination
reachability in urban rail transit networks
Lecturer: Professor Yanghai
Time:2019.7.1 11:00-12:00
Location: A949
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Lecture 1.
Abstract This keynote presentation will focus on a new data science landscape in which a whole host of smart equipment can act as sensors — legacy roadway systems, smart phones and GPS systems, and smart cars themselves. The key issue is how to deal with such voluminous and diverse amounts of incoming data per unit of time, and translate them into usable information for near-real time operations purposes or for longer-term planning purposes. This is a challenge, given the low latency and data reliability required to translate data into actionable intelligence, especially for such safety applications as collision avoidance. In addition, computational data science to translate data into information requires the ability to deal with data that may be from multiple sources, highly noisy, heterogeneous, and high-dimensional with complex interdependencies. On the last of these, the joint modeling of data with mixed types of dependent variables (including ordered-response or ordinal variables, unordered-response or nominal variables, count variables, and continuous variables) is a tricky problem. The presentation will discuss the exciting possibilities, some enquiry and computational data science pathways forward in terms of methods, and the research challenges in the emerging landscape of data science applications for the transportation field. This will include a discussion of the activities being undertaken as part of the U.S.DOT-funded Tier 1 Center at UT-Austin on “Data-Supported Transportation Planning and Operations” (D-STOP).*D-STOP is the Data-Supported Transportation Operations and Planning Center at the University of Texas at Austin.
About the presenter：Dr. Chandra R. Bhat is a world-renowned expert in the area of transportation and urban policy design, with far reaching implications for public health, energy dependence, greenhouse gas emissions, and societal quality of life. Methodologically, he has been a pioneer in the formulation and use of statistical and econometric methods to analyze human choice behavior. His current research includes the social and environmental aspects of transportation, planning implications of connected and automated smart transportation systems (CASTS), and data science and predictive analytics. He is a recipient of many awards, including the 2017 Council of University Transportation Center (CUTC) Lifetime Achievement Award in Transportation Research and Education, the 2015 ASCE Frank Masters Award, and the 2013 German Humboldt Award. He was listed in 2017 as one of the top ten transportation thought leaders in academia by the Eno Foundation. He is also a top-cited transportation engineering researcher (web of science h-index of 51 and google scholar h-index of 83), and was listed in the most cited researchers in civil engineering by ShanghaiRanking's global ranking of academic subjects 2016 by Elsevier. He is the Editor-in-Chief of Transportation Research – Part B and a visiting professor in the Department of Civil and Environmental Engineering at Hong Kong Polytechnic University.
Lecture 2.
Abstract Ride-sourcing services like Uber, Lyft and Didi Chuxing are playing an increasingly important role in meeting mobility needs in many metropolitan areas. Other than delivering passengers from their origins to destinations, ride-sourcing vehicles generate massive vacant or empty trips from the end of one customer trip to the start of the next. These vacant trips contribute additional traffic demand and may worsen the traffic conditions in urban networks. Capturing the congestion effects of these vacant trips poses a great challenge to the modeling practice of transportation planning agencies. With ride-sourcing services, vehicular trips are the outcome of the interactions between service providers and passengers, a missing ingredient in the current traffic assignment methodology. In this paper, we enhance the methodology by explicitly modeling those vacant trips, which include cruising for searching for customers and deadheading for picking up them. Due to the similarity between taxi and ride-sourcing services, we firstly extend previous taxi network models to construct a base model, which assumes intra-zone matching between customers and idle ride-sourcing vehicles and thus only considers cruising vacant trips. Considering spatial matching among multiple zones commonly practiced by ride-sourcing platforms, we further enhance the base model by encapsulating inter-zone matching and considering both the cruising and deadheading vacant trips. The extended model describes the equilibrium state that results from the interactions between background regular traffic, and occupied, idle and deadheading ride-sourcing vehicles. A solution algorithm is proposed to solve the enhanced model effectively. Numerical examples are presented to demonstrate the model and solution algorithm. Although this study focuses on ride-sourcing services, the proposed modeling framework can be adapted to model other types of shared-use mobility services.
About the presenter：Dr. Yafeng Yin is a Professor at Department of Civil and Environmental Engineering, and Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor. He works in the area of transportation systems analysis and modeling, and has published more than 100 refereed papers in leading academic journals. Dr. Yin is the Editor-in-Chief of Transportation Research Part C: Emerging Technologies, Department Editor of Service Science, and Associate Editor of Transportation Science. He also serves on the International Advisory Committee of the International Symposium of Transportation and Traffic Theory (ISTTT). Dr. Yin received his Ph.D. from the University of Tokyo, Japan in 2002, his master’s and bachelor’s degrees from Tsinghua University, Beijing, China in 1996 and 1994 respectively. Prior to his current appointment at the University of Michigan, he was a faculty member at University of Florida between 2005 and 2016. He worked as a postdoctoral
researcher and then assistant research engineer at University of California at Berkeley between 2002 and 2005. Between 1996 and 1999, he was a lecturer at Tsinghua University. Dr. Yin has received recognition from different institutions. He was one of the five recipients of the 2012 Doctoral Mentoring Award from University of Florida in recognition of his outstanding graduate student advising and mentoring. One of his papers won the 2016 Stella Dafermos Best Paper Award and the Ryuichi Kitamura Paper Award from Transportation Research Board.
Lecture 3.
Abstract:We report our latest research on the last train scheduling problem for maximizing passenger destination reachability. Because urban rail transit systems usually do not operate for the whole day, the last train service offers the last daily chance for late-night passengers to utilize URT services to reach their target destination stations. We show how the problem with both the last train timetabling and passenger assignment can be formulated as a mixed-integer linear programming (MILP) problem, and solved via existing commercial software. We also report some results of our case study with Beijing URT network
About the presenter:Prof. Hai Yang is currently a Chair Professor at The Hong Kong University of Science and Technology. He is internationally known as an active scholar in the field of transportation, with more than 240 papers published in SCI/SSCI indexed journals and a SCI H-index citation rate of 53. Most of his publications appeared in leading international journals, such as Transportation Research, Transportation Science and Operations Research. Prof. Yang received a number of national and international awards, including National Natural Science Award bestowed by the State Council of PR China (2011). He was appointed as Chang Jiang Chair Professor of the Ministry of Education of PR China; Prof. Yang served as the Editor-in-Chief of Transportation Research Part B: Methodological from 2013 to 2018 and is now a distinguished editorial board member of this journal.
