Zengxiang Lei
Social Sciences · Purdue University West Lafayette
Publications
39
Citations
293
Est. group size
~5
Recurring co-author estimate
Active years
11
Publishing since 2016
Zengxiang Lei's research focuses on understanding and modeling human travel behavior using large-scale data sources such as mobile phone records, smartphone GPS traces, and transit ridership logs. Work spans transportation planning topics like microtransit and ridesharing demand, disaster-related evacuation decision-making (e.g., hurricanes combined with COVID-19), and metro/transit network resilience, with some recent exploration into machine learning methods applied to these problems. This research would suit students interested in data-driven transportation systems, urban mobility analysis, or disaster/emergency travel behavior modeling.
Publication output has grown over the last decade, rising from a couple of papers per year around 2017-2021 to a more active and consistent pace of roughly 5-7 papers annually since 2022.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Stable GFlowNets with Probabilistic Guarantees
arXiv (Cornell University) · 2026
- Stable GFlowNets with Probabilistic Guarantees
arXiv (Cornell University) · 2026
- Anticipatory demand planning for microtransit with strategic use of latent capacity at fixed stops
Transportation Research Part E Logistics and Transportation Review · 2026
- Simulation Inputs for the METS-R Simulator
Open MIND · 2025
- Comparison of home detection algorithms using smartphone GPS data
EPJ Data Science · 2024
- Assessing metro network vulnerability with turn-back operations: A Monte Carlo method
Physica A Statistical Mechanics and its Applications · 2024
- Household evacuation decision making during simultaneous events: Hurricane Ida and the COVID-19 pandemic
International Journal of Disaster Risk Reduction · 2024
- Modeling hurricane evacuation/return under compound risks—Evidence from Hurricane Ida
International Journal of Disaster Risk Reduction · 2024
- Comparison of home detection algorithms using smartphone GPS data
arXiv (Cornell University) · 2023
- Real-time Ridesharing for Transportation Hubs with Demand and Supply Uncertainty
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2022
- Understanding multiple days’ metro travel demand at aggregate level
IET Intelligent Transport Systems · 2018
- Understanding the Structure of Taxi Travel Demand via Stable Principle Component Pursuit: A Case Study in Xiamen, China
Transportation Research Board 97th Annual MeetingTransportation Research Board · 2018
- Using ALPR Data to Analyze the Impact of TDM Policy on Vehicle Users’ Travel Behaviors
Transportation Research Board 97th Annual MeetingTransportation Research Board · 2018
- Using Matrix Decomposition Method to Understand the Structure of Metro Travel Demand
Transportation Research Board 97th Annual MeetingTransportation Research Board · 2018
- A Comparison of College Students’ Travel Patterns in Different Campuses Using Mobile Phone Positioning Data: A Case Study of Tongji University, China
CICTP 2017 · 2018
- arXiv (Cornell University)×5
- Transportation Research Part C Emerging Technologies×3
- Transportation Research Board 97th Annual MeetingTransportation Research Board×3
- IET Intelligent Transport Systems×2
- International Journal of Disaster Risk Reduction×2
- Satish V. Ukkusuri
Social Sciences · Purdue University West Lafayette
- Jiawei Xue
Social Sciences · Purdue University West Lafayette
- Xiaowei Chen
Social Sciences · Purdue University West Lafayette
- Mark R. McCord
Social Sciences · The Ohio State University
- Shiping Shao
Social Sciences · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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