arXiv Machine Learning

Public transit gains and spatially uneven travel demand changes after NYC congestion pricing

arXiv:2606. 17530v1 Announce Type: cross Abstract: New York City implemented the nation's first cordon-based congestion pricing program in January 2025, providing an opportunity to evaluate how system-wide urban mobility responds to large-scale pricing interventions.

arXiv Machine Learning
Sep 10

Inferring Urban Mobility Interactions from Aggregated Dynamics

The paper demonstrates that real‑time urban mobility patterns can be reconstructed from aggregated area‑level counts, without tracking individuals. Using a physics‑informed, uncertainty‑aware framework, the authors infer future origin‑destination flows across twelve datasets from the U.S. and China, achieving accuracy comparable to models that use historical OD matrices. Probabilistic modeling corrects underestimation of sparse corridors, and architectures that preserve spatial heterogeneity before reconstructing pairwise interactions yield more faithful interaction estimates.

By Yi Wang, Jing Li, Jinliang Deng, Zhenghong Wang, Yizhi Zhang, Fan Zhang, Ivor W. Tsang, Yu Liu
arXiv Machine Learning
Aug 31

Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting

The paper introduces TransMod, a unified framework for forecasting urban mobility demand across multiple transportation modes. It creates a shared zone-level spatial representation to align systems with different spatial granularities, reducing structural mismatch and distributional shift. TransMod then learns transferable spatio-temporal patterns from data-rich source modes and adapts them to data-scarce target modes, improving forecasting performance when target data is limited.

By Yixuan Zhao, Man Luo
arXiv Machine Learning
Aug 11

TS-Mob: Social and Geographical-Aware Time Series Foundation-Model Framework for Human Mobility Prediction

arXiv:2507. 00945v2 Announce Type: replace Abstract: Short-term forecasting of aggregated human mobility flows supports urban planning, intelligent transportation systems, and emergency response, yet existing models often require substantial mobility history and learn spatial structure implicitly through grids or graphs.

By Massimiliano Luca, Ciro Beneduce, Bruno Lepri
Hugging Face Trending Papers
Sep 2

UTP-Bench: Uncertainty-aware Travel Planning Benchmark

UTP‑Bench is a new benchmark that evaluates large language models on uncertainty‑aware travel planning, incorporating real‑world data from 504 Indian cities and empirical delay and crowd patterns. It introduces three metrics—Buffer Adequacy Score, Crowd‑Aware Timing Score, and Transport Delay Absorption Score—to measure how well generated itineraries remain robust under stochastic conditions. Experiments show that current LLMs lag behind human planners in buffering, delay‑aware scheduling, and crowd sensitivity.