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From Biases to Opportunities: Leveraging Location-Based-Service (LBS) Data for Advancing Smart Mobility


SPEAKER Prof. Cynthia Chen

Department of Civil & Environmental Engineering and

Department of Industrial & Systems Engineering

University of Washington (UW)

Member of Washington State Academy of Sciences ​ DATE AND TIME 11 DEC 2025, 7 pm -8 pm


Venue

CPD 3.04 (Central Podium Levels - Three), Run Run Shaw Tower, The University of Hong Kong ORGANISED BY Institute of Transport Studies, The University of Hong Kong ​

REGISTRATION LINK

ABSTRACT Location-Based Service (LBS) data, generated from the mobile devices that now accompany people everywhere, holds immense promise for advancing smart mobility. With its ability to provide continuous, large-scale insights into travel patterns, LBS data can transform how we collect information, develop models, and design policies for more efficient, equitable, and sustainable mobility systems. Yet this potential is constrained by persistent challenges—particularly the lack of transparency among researchers, transportation professionals, and LBS vendors, as well as data quality biases that limit reliability.


This talk first highlights the opportunities of LBS data to enable smart mobility planning, from dynamic demand forecasting to creating adaptive infrastructure capacity. I then examine key biases and quality issues in LBS data and their impact on critical mobility metrics used for planning. Addressing these challenges requires collective action: fusing small-scale (Household Travel Survey) and large-scale (LBS) data to create privacy-aware mobility digital twins; applying anomaly and changepoint detection to trace the evolution of behavioral and network-level changes; and uncovering hidden capacities in infrastructure systems. Finally, I outline pathways for collaboration across the research, practitioner, and vendor communities to establish benchmark datasets, trip inference standards, and privacy safeguards—all essential to unleashing the full potential of LBS data in driving the future of smart mobility. BIOS Cynthia Chen is a professor in the Departments of Civil & Environmental Engineering and Industrial & Systems Engineering at the University of Washington (UW) and a member of the Washington State Academy of Sciences. An internationally recognized leader in transportation science, she directs the THINK (Transportation–Human Interaction and Network Knowledge) Lab at UW. Her research tackles some of the most pressing challenges in mobility and resilience: uncovering biases in big data, developing innovative methods to fuse large-scale and small-scale data sources, modeling mobility behaviors of individuals and cascading processes in networks, and designing interventions that promote healthier, more resilient communities through routine-aware personalized recommendations and place-based peer-to-peer sharing.


Prof. Chen’s scholarship is widely published in top journals across transportation systems engineering, travel behavior, land use planning, and interdisciplinary venues such as PNAS and Nature Cities. Her work has been supported by numerous federal, state, and local agencies. Currently, she serves as Associate Director of the USDOT-funded National Center for Understanding Future Travel Behavior and Demand (led by UT Austin) and as an Associate Editor for Transportation Science.


Through the THINK Lab, Prof. Chen continues to push the boundaries of how we understand human mobility, networks, and resilience in the face of social and environmental change. More about her work can be found at https://sites.uw.edu/thinklab.


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Title: Strategic planning for public transportation electrification: large-scale electric bus network transition planning via deep reinforcement learning

Speaker: Ms. Luyun Zhao (Department of Urban Planning and Design)

Date: Oct 14, 2025 (Tuesday)

Time: 1:00 pm - 2:00 pm

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

ITS Student Committee will provide light refreshments and drinks for registered participants.


Abstract: Urban bus electrification is gaining global interest, playing a crucial role in reducing emissions. This study defines and addresses the electric bus network transition problem (EBNTP), jointly optimizing battery electric bus (BEB) fleet transitions and charging facility planning over a multi-period horizon. Existing research often neglects this interdependent long-term planning and lacks scalable solutions for large systems. This study proposes a deep reinforcement learning (DRL) approach, formulating EBNTP as a Markov Decision Process modeling sequential planning decisions, and introduces the DRL-HetGNN method, integrating heterogeneous graph neural networks (HetGNN) to capture network effects and enhance efficiency in large-scale applications. Using Hong Kong's franchised bus system as a case study, DRL-HetGNN demonstrates superior performance and generalizability compared to benchmark methods. Scenario analyses explore budget allocations, independent operators, BEB subsidies, and price fluctuations, while examining policy-incentive mechanisms to accelerate electrification. The findings will support policymakers in planning sustainable public transportation systems.

 

Bios: Luyun Zhao is a PhD candidate at the Department of Urban Planning and Design, Faculty of Architecture, HKU. Her research interest lies in public transit, shared mobility, transportation electrification, and transportation modeling. She has more than five years of theoretical and practical experience in the transportation industry. Prior to HKU, she worked at Tencent Smart Transportation, DiDi Chuxing Public Transportation Department and Two-Wheeler Department, Bytedance, and the World Bank. Luyun received her Bachelor of Engineering and Bachelor of Economics degrees from Peking University and her Master of City Planning degree from the University of Pennsylvania.


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Strategic Coordination and Integration in Ride-Hailing Platforms


Speaker:

Prof. Fang He

Department of Industrial Engineering

Tsinghua University

Date:    Sep 30, 2025 (Tuesday)

Time:   11:00 am – 12:15 pm

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


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Abstract

This seminar synthesizes two complementary game-theoretic investigations that together illuminate how ride-hailing ecosystems can be steered toward greater efficiency and welfare. The first study frames idle drivers’ information-enabled relocation as a multi-stage leader–follower game on arbitrary service networks. The platform (leader) designs relocation subsidies; drivers (followers) relocate and compete for revenue. Existence and uniqueness results show that when volume imbalances are pronounced or commission rates are low, drivers’ self-interested moves already align with system objectives, making relocation-specific subsidies superfluous. In residual cases, spatial–temporal targeted incentives can double platform profit and markedly boost trip completions, with gains amplified on sparse networks and when driver behavior retains moderate stochasticity. The second study examines the rise of third-party integrated platforms (IPs) that match orders across heterogeneous service providers (SPs) under a revenue-sharing commission. A three-stage mixed-integer game reveals that integration outcomes hinge on four interactive forces: relative SP size, demand stimulation, profit redistribution, and commission design. Excessive consolidation dampens competition, discourages large SPs, and allows the IP to over-extract surplus—threatening industry welfare when worker supply is ample. A ceiling-commission policy emerges as an effective regulatory safeguard.


Bio

Dr. Fang He is Deputy Head for Research and Tenured Associate Professor in the Department of Industrial Engineering at Tsinghua University. He serves as the Executive Vice Dean of the Tsinghua University–COSCO Shipping Green & Intelligent Supply Chain Institute. His research focuses on network modeling and optimization, large-scale combinatorial optimization and deep reinforcement learning, producing more than 60 journal papers, including publications in TS (6), Transportation Research Part Series (40+), and POM (3). He serves as the Associate Editor of Transportation Science and the Editor of Transportation Research Part B. Dr. He is also a recipient of a national-level Young Talent Program in China, and his scholarly work has been implemented by China COSCO Shipping Group, AutoNavi Ride-Hailing, the Beijing 2022 Winter Olympics, and the Wuhan Power Grid, among other high-impact sectors. He has been recognized as an Elsevier Highly Cited Chinese Scholar for six consecutive years (2019–2024), with a single paper exceeding 600 citations. Dr. He received his B.S. degree in Civil Engineering from Tsinghua University in 2010 and his Ph.D. degree from the University of Florida in 2014.

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