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:2505.11239v4 Announce Type: replace
Abstract: Understanding human mobility through Point-of-Interest (POI) trajectory modeling is increasingly important for applications such as urban planning,...
By Wilson Wongso, Hao Xue, Flora D. Salim
TSMini is a trajectory similarity learning model that introduces a sub-view modeling mechanism and a k‑nearest‑neighbor‑based loss to capture multi‑granularity trajectory patterns and relative similarity ranks. These innovations allow TSMini to approximate conventional trajectory similarity measures with high accuracy. Experiments demonstrate that TSMini outperforms state‑of‑the‑art models by an average of 15% on widely used similarity metrics.
By Yanchuan Chang, Dingyang Lyu, Xu Cai, Christian S. Jensen, Jianzhong Qi
WALT introduces a method to align latent trajectories with pretrained driving world models, creating a compact generative trajectory space that preserves action-relevant semantics without altering the original model. The approach uses a dual-branch autoencoder to map raw waypoints into this latent space and transfers visual world knowledge into trajectory representations. Experiments on NAVSIM benchmarks show modest performance gains and a 30.5% reduction in planner FLOPs, indicating that maintaining world representations while extracting action-relevant information can improve trajectory planning efficiency.
By Mingkai Jia, Jiaxin Guo, Zhijian Shu, Jiawei Xu, Mingxiao Li, Jintao Cheng, Ping Tan, Wei Yin
MoRAX is a lightweight framework that augments geospatial foundation model embeddings with functional structure derived from human mobility data. By incorporating mobility flows, MoRAX preserves the coverage and consistency of existing geospatial models while adding information about functional connectivity among urban regions, enabling zero‑shot deployment in unseen cities. Experiments across four cities in two countries show that the MoRAX teacher model outperforms baseline geospatial models on eight socioeconomic and environmental prediction tasks, and the student model—without direct mobility input—approaches the teacher’s performance.
By Ya Wen, Jixuan Cai, Yulun Zhou, Alec Kirkley
arXiv:2606. 12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation.
By Siyu Li, Toan Tran, Lingyi Zhao, Khurram Shafique, Li Xiong