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Using AVs to Replace Buses & Complement Urban Rail Systems









SPEAKER Professor Kara Kockelman Dewitt Greer Centennial Professor of Transportation Engineering Department of Civil, Architectural and Environmental Engineering The University of Texas at Austin ​ DATE AND TIME September 29 (Friday) 10:00 – 11:00 am (Hong Kong Time) REGISTRATION Please register by using this link: https://forms.office.com/r/xiT9c9E9xL Confirmation emails with ZOOM link will be sent to participants before the lecture. This Distinguished Transport Lecture will be held on ZOOM only. ORGANISED BY Institute of Transport Studies, The University of Hong Kong ​ ABSTRACT Motivated by shared autonomous vehicles’ (SAVs’) many potential services (like door-to-door [D2D] service, first-mile last-mile [FMLM] service, and bus replacements), this work explores traveler choices for different US settings, simulates fleets of SAVs in concert with other fixed-route transit, and analyzes results for travelers, their networks, and their regions. The presentation first microsimulates how SAVs can provide FMLM service to (and from) 5 commuter rail stations in central Austin neighborhoods. With train headways of 15 minutes, simulations predict dramatic increases in train use (by roughly a factor of 10, at those stations). Variations in train headways and SAV fleet sizes illustrate how D2D travel remains the key predictor of mode choice. Four-seat SAVs perform similarly to 6-seat SAVs, but cost less to provide. A dynamic ride-sharing (DRS) (vehicle-to-passenger assignment) algorithm tightly coordinated with train arrivals delivers 87% of travelers to their stations in time to catch the next train, while uncoordinated assignments deliver just 57% in time. Second, this work simulates 10-seat SAVs providing fixed-route transit service alongside private automobiles. System costs for each traveler type along a 6.4-kilometer (4-mile) corridor are computed across different SAV-use rates. The work prices out walking, waiting, riding, and driving times for all travelers in the corridor, along with vehicle ownership, parking, and operating costs. Results suggest that such self-driving mini-buses or 10-seat SAVs lower total costs per passenger-kilometer traveled when SAV mode split exceeds 30 percent, even though walking and waiting are valued at a relatively high cost. Third, a POLARIS-based mesoscopic simulation integrates three SAV service types across the 20-county Chicago region. When SAVs with DRS serve only D2D trips, at just $0.50 per passenger-mile, with 1 SAV for every 40 residents, they attract 15% of trips (and private vehicle ownership falls from 0.66 to 0.37 cars per capita), with a 15-minute average travel time and 4.6-mile average person-trip distance. Adding FMLM service (to about 54,000 train and bus stops) increases the region’s transit split: from 5.4% to 6.3% of travelers, with the same SAV fleet serving 12% more person-trip requests per day and driving 4.2% more SAV-miles. Most FMLM person-trip distances are under 2 miles, with rail-station connections dominating (rather than those to bus stations). Overall, many metro regions of the globe and their transit systems seem ready to benefit from SAV services. SPEAKER’S BIO Prof. Kara Kockelman is a registered professional engineer and holds a PhD, MS, and BS in civil engineering, a master’s in city planning, and a minor in economics from the University of California at Berkeley. She has been a professor of transportation engineering at the University of Texas at Austin for 25 years, and is the recipient of an NSF CAREER Award, Google Research Award, MIT Technology Review Top 100 Innovators Award, Vulog’s Top 20 of 2020 Influential Women in Mobility, and various ASCE, NARSC, TRF, and WTS awards. She recently served as President of the North American Regional Science Association and sits on the Eno Center for Transportation’s Advisory Board, as well as 3 TRB Committees. She has authored over 200 journal articles (and two books), and her primary research interests include planning for shared and autonomous vehicle systems, the statistical modeling of urban systems, energy and climate issues, the economic impacts of transport policy, and crash occurrence and consequences. Pre-prints of these articles (and book contents) can be found at www.caee.utexas.edu/prof/kockelman. She hopes you will join the zero-cost, zero-carbon Bridging Transportation Researchers conference (held in August each year), by submitting papers in spring & then registering here: www.bridgingtransport.org.



On-Demand Shared Mobility Management for Smart Cities


SPEAKER:

Prof. Xiqun CHEN

Department of Civil Engineering,

Zhejiang University, China


DATE:

August 8, 2023 (Tuesday)


TIME:

11:00 a.m. – 12:00 p.m.


VENUE:

Room 612B, 6/F Haking Wong Building, The University of Hong Kong

Abstract

On-demand ride-sourcing services (e.g., Uber, DiDi) receive praises from consumers and investors. This presentation focuses on the ride-sourcing system optimization modeling and behavioral analysis for smart cities. Under governmental regulation of ride-sourcing platforms, pricing and subsidies on passengers and drivers have become an effective incentive to coordinate supply and demand. A multi-stage game-theoretic model is formulated to reveal the coupling game among heterogeneous passengers, heterogeneous drivers, and the ride-sourcing platform in the on-demand ride services market regulated by the government. Ride-sourcing platforms offer incentive subsidies and set pricing strategies to ensure stable supply capacity for on-demand ride services. Understanding the causal effects is a prerequisite for deploying related activities, as well as the heterogeneous and stochastic responses to subsidy and pricing. Data-driven agent-based modeling and simulation for large-scale transportation networks are implemented to investigate how regulatory policies impact the ride-sourcing market, which goes beyond existing approaches by employing data-driven multi-objective deep learning to train ride-sourcing drivers' offline/online behavior. Those research initiatives of the presenter’s research team have the potential to help decision-makers and ride-sourcing platforms to optimize regulatory policies and operations management strategies in the era of shared mobility.

About the Speaker

Dr. Xiqun (Michael) Chen is Tenured Professor of Zhejiang University, Director of Institute of Intelligent Transportation Systems, Vice Dean of Zhejiang University-UIUC Institute, and Deputy Director of Zhejiang Provincial Engineering Research Center for Intelligent Transportation. Prof. Chen’s research interests include shared mobility on demand, simulation-based optimization, transportation big data analytics, and intelligent transportation systems. He received the National Excellent Young Scholars Award of National Natural Science Foundation of China and Distinguished Young Scholars Award of Zhejiang Provincial Natural Science Foundation, and was an awardee of Young Elite Scientists Sponsorship Program by China Association for Science and Technology. Currently, he serves as the Chairman of Transportation Management and Control of World Transport Convention, Transportation Consultant for World Bank, Board Member of ASCE Greater China Section, Board Member of Society of Management Science and Engineering of China, Associate Editor of IEEE Transactions on Intelligent Vehicles, Editorial Advisory Board Member of Transportation Research Part C: Emerging Technologies, and Senior Associate Editor of Digital Transportation and Safety. Prof. Chen has published 1 book, 3 book chapters, over 110 peer-review international journal papers on Nature Sustainability, Cell Press journal Patterns, Cell Press partner journal The Innovation, Management Science, Manufacturing & Service Operations Management, Transportation Science, Transportation Research Part B, etc. In 2022, he was ranked in the list of World’s Top 2% Scientists by Stanford University. He received the Science and Technology Innovation Youth Award of China Communications and Transportation Association, Science and Technology Award of China Intelligent Transportation Systems Association, Best Ph.D. Dissertation Award of IEEE Intelligent Transportation Systems Society, and Best Paper Awards at seven international conferences.

Hosts:

DEPARTMENT OF CIVIL ENGINEERING

JOINTLY ORGANIZED WITH

HONG KONG SOCIETY FOR TRANSPORTATION STUDIES

And INSTITUTE OF TRANSPORT STUDIES, HKU

New Evolutionary Algorithms to Solve the Competitive Maximal Covering Location Problem


SPEAKER:

Prof. Abdullah Konak

Distinguished Professor of Information Sciences and Technology The Pennsylvania State University, Berks, USA


Date: July 28, 2023 (Friday)

Time: 5:00 pm – 6:00 pm

Venue: Room 612B, 6/F Haking Wong Building, The University of Hong Kong


Abstract:

This presentation introduces two evolutionary algorithms called the Game-Theoretic Genetic Algorithm (GTGA) and Regret-Based Nash Equilibrium Sorting Genetic Algorithm (RNESGA) for analyzing combinatorial optimization game theory problems where it is computationally infeasible to enumerate all decision options of the players involved in the game. Although evolutionary algorithms are widely used to solve combinatorial optimization programs, their applications to game theory have been limited to specific types of games. The GTGA and RNESGA can solve different types of game theory problems using multiple populations and alternating fitness evaluation methods. We will demonstrate how these algorithms can be applied to solve various versions of the Competitive Maximal Covering Location Problem as well as other game theory problems such as Cournot's Model, Pricing Games, Numerical game, Hotelling Game, etc. Computational experiments demonstrate their performance in terms of converging equilibria in Nash and Stackelberg games.


About the Speaker:

Dr. Abdullah Konak is a Distinguished Professor of Information Sciences and Technology at the Pennsylvania State University, Berks. Dr. Konak also teaches graduate courses in the Master of Science in Cybersecurity Analytics and Operations program at the College of Information Sciences and Technology, Penn State World Campus. Dr. Konak’s primary research focuses on modeling, analyzing, and optimizing complex systems using computational intelligence combined with probability, statistics, data sciences, and operations research. His research also involves active learning, entrepreneurship education, and the innovation mindset. Dr. Konak published numerous academic papers on a broad range of topics, including network design, system reliability, sustainability, cybersecurity, facilities design, green logistics, production management, and predictive analytics. Dr. Konak held visiting positions at Lehigh University and Cornell University, as well as at the Chinese University of Hong Kong, where he taught engineering innovation for over a decade. He has been a principal investigator in sponsored projects from the National Science Foundation, the National Security Agency, the U.S. Department of Labor, and Venture Well. He is a member of INFORMS, IISE, and ASEE.


Hosts:

DEPARTMENT OF CIVIL ENGINEERING

JOINTLY ORGANIZED WITH

HONG KONG SOCIETY FOR TRANSPORTATION STUDIES

AND

INSTITUTE OF TRANSPORT STUDIES, HKU


© 2023 by Institute of Transport Studies. The University of Hong Kong.
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