Hanyong Xu

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Nice to meet you!

I am a Ph.D. candidate in Urban Science at the Department of Urban Studies and Planning (DUSP) at MIT, advised by Professor Jinhua Zhao. I am a member of the JTL Urban Mobility Lab (JTL) and affiliated with the Data + Feminism Lab. I seek to leverage the power of data science, algorithms, and visualization to lead to better urban planning, public policy making, and business growth. My current research focuses on:

  • responsible data science and AI in urban science;
  • platform economy and urban form.

Prior to joining DUSP, I accumulated three years of professional experience as both a data analyst and a GIS specialist at Meituan and CityDNA Technology, orchestrating data science and web-based solutions for decision-makers in urban planning and e-commerce. I hold a Master of Urban Spatial Analytics from the University of Pennsylvania Stuart Weitzman School of Design and an Honors Bachelor of Arts with a double major in Architectural Design and Economics from the University of Toronto.

news

Jul 11, 2026 Our paper Longitudinal evaluations of the coverage and pricing differences between transportation network companies and traditional taxis: A case study of New York city is published on journal Transportation Research Part A: Policy and Practice.
Jul 1, 2026 Our paper Large language models for travel behavior prediction is published on journal Transportation Research Interdisciplinary Perspectives.
Feb 2, 2026 Presented “Unveiling and Mitigating Disparities in the Ride-Hailing Industry” at the EAAMO Bridges Urban Data and Equitable Cities working group.
Jan 14, 2026 Presented two papers, “Longitudinal Evaluations of the Coverage and Pricing Differences between Transportation Network Companies and Traditional Taxis: A Case Study of New York City” and “Modeling Latent Demand and Reducing Prediction Disparities of Ride-hailing: A Fair Quantile Regression Method”, at Transportation Research Board (TRB) Annual Meeting 2026 in Washington DC, US. [Online Program with Paper and Presentation]
Apr 28, 2025 Paper “Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study.” was presented at The International Workshop on Spatio-Temporal Data Mining from the Web WebST’25, held in conjunction with the ACM on Web Conference WWW2025 in Sydney, Australia, and received the Best Paper Award.

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