22 JUL 2024 (Mon) 16:00-17:00 | Seminar by Prof. Yanfeng Ouyang

Expected Bipartite Matching Distance in An L^p Space: Approximate Closed-form Formulas and Applications to On-Demand Shared Mobility Services

Speaker:

Prof. Yanfeng Ouyang

Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, USA

Date:    July 22, 2024 (Monday)

Time:   4:00pm – 5:00pm

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

Abstract

In this talk, we discuss how strategic performance evaluation and resource planning of shared mobility services can benefit from new closed-form formulas that estimate the expected distances from a random bipartite matching problem in a D-dimensional L^p space. Asymptotic approximations of the formulas are also developed for some special cases. These formulas provide a theoretical foundation for taxi matching functions in the literature, and also reveal conditions under which the matching function will be most suitable. These formulas can also be easily incorporated into optimization models to select taxi operation strategies; e.g., whether newly arriving customers shall be instantly matched or pooled into a batch for matching. Agent-based simulations are also conducted to verify the predicted performance of the demand pooling strategy for two types of e-hailing taxi systems.

About the Speaker

Yanfeng Ouyang is George Krambles Professor, Paul Kent Faculty Scholar, and Donald Willett Faculty Scholar at the University of Illinois, Urbana-Champaign (UIUC). He is also Associate Director for Mobility of the Illinois Center for Transportation.  His work mainly focuses on planning, operations, and control of complex transportation and logistics systems. He currently serves (or previously served) as a Department/Area/Associate/Board Editor of IISE Transactions, Networks and Spatial Economics, Transportation Science, Transportation Research Part B, Transportation Research Part C, and Transportmetrica B. He is also Chair of TRB’s AEP40 Committee on Transportation Network Modeling. His work has been recognized by a Merit Award for Technical Study from the American Planning Association, a Walter L. Huber Research Prize from the American Society of Civil Engineers, a High Impact Project Award from the Illinois Department of Transportation, a Faculty Early Career Development (CAREER) Award from the U.S. National Science Foundation, among others.

5 JUL 2024 (FRI) 16:30-17:30 | Artificial Intelligence, Decision Making, and Fairness: Risks and Opportunities by Dr. Abdullah Konak

Artificial Intelligence, Decision Making, and Fairness: Risks and Opportunities

Speaker:

Dr. Abdullah Konak

Pennsylvania State University, Berks.

Date:    July 5, 2024 (Friday)

Time:   16:30 – 17:30

Venue:  Room 8-28, 8/F, Haking Wong Building, The University of Hong Kong

Abstract

With the proliferation of machine learning applications across industries such as transportation, human resources, finance, surveillance, and healthcare, concerns about the fairness and equity of artificial intelligence (AI) have intensified. Recent incidents highlighting biased AI predictions have underscored the urgent need to ensure fairness in these systems. In this presentation, we will first review different sources and types of biases that can affect AI applications and approaches to remedying the effects of biases. We will later introduce Multi-Objective Ensemble Learning for Fairness (MELF). This novel approach combines ensemble learning and multi-objective decision-making to train machine learning models that achieve a balance between predictive performance and fairness metrics. MELF is adaptable across various datasets and machine learning algorithms and can be integrated with other fairness-aware training techniques. Computational experiments with various algorithms demonstrate that MELF can enhance fairness without compromising predictive accuracy.

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 and ASEE.

GRAND CHALLENGES SEMINAR by Dr. Yu (Marco) NIE (18 JUN 2024 (TUE) | 11:00-12:00)

Does Dockless Bikesharing Create a Competition for Losers?

Speaker:

Dr. Yu (Marco) NIE

School of Civil and Environmental Engineering, Northwestern University

Date:    June 18, 2024 (Tuesday)

Time:   11:00 am – 12:00 nn

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

Abstract

We model the oligopoly competition in a dockless bike-sharing (DLB) market as a dynamic game. Each DLB operator is first committed to an action tied to a specific objective, such as maximizing profit. Then, the operators play a lower-level game to reach a subgame perfect Nash equilibrium, by making tactical decisions (e.g., pricing and fleet sizing). We define a Nash equilibrium under either weak or strong preference to characterize the likely outcomes of the dynamic game and formulate the demand-supply equilibrium of a DLB market that accounts for key operational features and mode choice. Using the oligopoly game model calibrated with empirical data, we show that, if an operator seeks to maximize its market share with a budget constraint, all other operators must either respond in kind or be driven out of the market. When all operators compete for market dominance, even a slight efficiency edge gained by one operator can significantly shift the outcome, which signals high volatility. Moreover, even if all operators agree to focus on making money rather than ruinously seeking dominance, profitability still plunges quickly with the number of operators. Taken together, the results explain why an unregulated DLB market is often oversupplied and prone to collapse under competition. We also show this market failure may be prevented by a fleet cap regulation, which sets an upper limit on each operator's fleet size.

About the Speaker

Dr. Yu (Marco) Nie is currently a Professor of Civil and Environmental Engineering at Northwestern University. He received his B.S. in Structural Engineering from Tsinghua University, his M.S. from National University of Singapore and his Ph.D. from the University of California, Davis.  Dr. Nie’s research covers a variety of topics in the areas of transportation systems analysis, transportation economics, and sustainable transportation. Dr. Nie served as a member of the TRB committees on Transportation Network Modeling and Traffic flow Theory and Characteristics. He is currently an Area Editor for Transportation Science, an Associate Editor for Transportation Research Part B and Service Science. Dr. Nie’s research has been supported by National Science Foundation, Transportation Research Board, US Department of Transportation, US Department of Energy, and Illinois Department of Transportation.

Hosts:

DEPARTMENT OF CIVIL ENGINEERING

GRAND CHALLENGES SEMINAR SERIES 2023-24

JOINTLY ORGANIZED WITH

INSTITUTE OF TRANSPORT STUDIES, HKU