BACKGROUND
The Distinguished Transport Lecture Series (DTLS) was launched in 2009. Each year, we invite internationally renowned and distinguished scholars to visit Hong Kong to deliver public lectures in the transport field, with the aim of promoting excellence in transport research and development in Hong Kong, as well as providing a platform for discussions and the exchange of views between international experts, scholars, local researchers, and professionals.
Distinguished Transport Lecture by Prof. Cynthia Chen, 11 DEC 2025 (THU) 19:00 – 20:00 (HKT)
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
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DATE AND TIME
11 DEC 2025, 7 pm -8 pm
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ORGANISED BY
Institute of Transport Studies, The University of Hong Kong ​
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REGISTRATION LINK
​https://forms.office.com/r/CsQCxTUg7H
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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.
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SPEAKER’S BIO
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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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Distinguished Transport Lecture by Prof. Quddus, 02 SEP 2025 (TUE) 19:00 – 20:00 (HKT)
From Reaction to Prediction: Re-imagining Traffic Safety Through AI and Intelligent Transport Systems​
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SPEAKER
Prof. Quddus
Chair Professor of Intelligent Transport Systems (ITS)
Department of Civil and Environmental Engineering
Imperial College London
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DATE AND TIME
02 SEP 2025, 7 pm -8 pm
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ORGANISED BY
Institute of Transport Studies, The University of Hong Kong ​
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REGISTRATION LINK
https://forms.office.com/r/P3jXb7bhap​
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ABSTRACT
This talk presents a paradigm shift in traffic safety analysis, moving from reactive approaches based on historical collision data to proactive conflict detection and predictive collision modelling. In the reactive phase, advanced statistical methods are used to analyse collision patterns, identify high-risk locations and evaluate the effectiveness of safety interventions, laying the groundwork for evidence-based traffic safety evaluation programmes. The proactive phase leverages vehicle-based conflict data to anticipate potential collisions before they occur. To improve interpretability and accuracy, a context-aware conflict prediction algorithm is introduced, combining traffic covariates with vehicle sensor data using a hierarchical Bayesian threshold-excess model grounded in Extreme Value Theory (EVT). This approach improves model performance and enables efficient real-time deployment. In the predictive phase, vision-based video analytics are used to detect and explain pre-crash behaviours, informing the development of more transparent and trustworthy Advanced Driver Assistance Systems (ADAS). Together, these phases represent a fundamental shift—from reacting to crashes after they occur, to preventing them through intelligent, explainable and data-driven systems.
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SPEAKER’S BIO
Professor Quddus is Chair Professor of Intelligent Transport Systems (ITS) in the Department of Civil and Environmental Engineering at Imperial College London. He is internationally recognised for his pioneering research in transport safety and simulation, autonomous and connected vehicles, big data analytics, and map-matching technologies. His seminal work on AI-based map-matching has become highly influential, widely cited by researchers, and adopted by the ITS industry, automotive manufacturers, and National Highways. Notably, his award-winning risk-mapping algorithms have been implemented to inform strategic safety policies and procedures across the UK’s 4,300-mile strategic road network. Over the past two decades, Professor Quddus has led and co-led research projects totalling more than £12.2 million, funded by UKRI, the European Commission, government bodies, and industry partners. He has directed large-scale research programmes, including a mini-CDT, and served on international research committees such as the Transportation Research Board (Washington, DC), while also contributing as an editor of leading peer-reviewed journals, including Transportation Research Part C: Emerging Technologies. To date, he has supervised over 40 doctoral and postdoctoral researchers and authored more than 140 peer-reviewed journal publications.
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