The paper introduces Nomad, a transfer-and-ground framework for generating human mobility trajectories without target-city trajectory data. It learns relative transitions from source cities using POI attributes and then grounds these transitions onto a target city’s POI map via a behavior graph and exploration–return walk. Experiments across ten cities show Nomad improves trajectory fidelity and downstream utility by roughly 15% and 3% respectively over adaptation baselines.
By Yidi Wang, Yunhe Zhang, Bangchao Deng, Dingqi Yang, Pengyang Wang
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:2606. 09086v1 Announce Type: new Abstract: Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations.
By Jie Zhao, Xianqi Dai, Jie Feng, Huandong Wang, Yong Li
arXiv:2606. 09115v1 Announce Type: new Abstract: Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback.
By Lena Krieger, Xuan Zhao, Zhuo Cao, Qin Wang, Hanno Scharr, Ira Assent
arXiv:2609.15883v1 Announce Type: cross
Abstract: Offline reinforcement learning aims to learn a policy solely from fixed datasets, which often contain multimodal action distributions. Flow policies...
By Jaehun Shon, Jinha Choi, Jongwook Jeon, Jongmin Lee
arXiv:2510. 14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis.
By Ji Cao, Yu Wang, Tongya Zheng, Jie Song, Qinghong Guo, Zujie Ren, Canghong Jin, Gang Chen, Mingli Song
arXiv:2608. 19778v1 Announce Type: cross Abstract: Pedestrian simulators need a behaviour rule for every agent, but privacy usually limits the data for setting one to aggregate statistics, namely zone-level device counts and origin-to-destination (OD) flows, with no individual trajectories.
By Tatsuya Amano, Hirozumi Yamaguchi
arXiv:2606. 05327v1 Announce Type: new Abstract: Flow matching (FM) has emerged as a powerful framework for learning dynamic transport maps between two empirical distributions.
By Raghav Kansal, David Crair, Nghia Nguyen, Scott Pope, Bradley Parry
arXiv:2607. 22655v1 Announce Type: new Abstract: Estimating origin-destination (OD) flows under disruptive events is important for disaster response and urban resilience.
By Jie Zhao, Jie Feng, Can Rong, Zhihan Hou, Peng Lu, Yong Li
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:2609.16528v1 Announce Type: new
Abstract: Building accurate decision-support tools for next-generation air traffic control requires robust trajectory prediction models. We present a flow-matchi...
By Mathurin Petit, Emir Torun, Louis Brusset, Jordan Kam, Alexandre M. Bayen
arXiv:2511. 06229v3 Announce Type: replace Abstract: This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations.
By Donggyu Min, Seongjin Choi, Dong-Kyu Kim