The paper introduces a spatio‑temporal traffic forecasting framework that fuses Graph Neural Networks with embeddings derived from a general‑purpose knowledge graph such as Wikidata. By creating semantic subgraphs around traffic sensors, the approach captures relationships like nearby points of interest, administrative hierarchies, and functional roles of locations, which are then integrated as additional adjacency matrices for the GNN. Experiments demonstrate that this external knowledge improves prediction accuracy beyond what street‑network data alone can achieve, while also offering a path toward better interpretability.
The paper introduces a spatio‑temporal traffic forecasting framework that fuses Graph Neural Networks with semantic knowledge from general-purpose knowledge graphs such as Wikidata. By generating embeddings that capture relationships like nearby points of interest, administrative hierarchies, and functional roles of locations, the framework creates additional adjacency matrices that enrich the sensor graph beyond physical connectivity. Experiments with established forecasting methods demonstrate that this external knowledge improves prediction accuracy and offers a path toward better interpretability.
By Mattis thor Straten, Yannick Wolker, Steffen Strohm, Prathvish Mithare, Ralf Krestel, Matthias Renz
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
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
Traffic flow forecasting is essential to intelligent transportation systems. Large-scale traffic forecasting requires jointly modeling local spatial dependencies and cross-region context.Spatial depen...
arXiv:2608. 08646v1 Announce Type: new Abstract: Trajectory-User Linking (TUL) aims to identify the owner of an anonymous trajectory from a set of candidate users, providing a basis for user mobility analysis and personalized location-aware services.
By Zhifeng Chu, Bin Wang
arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.
By Mohammad Hashemi, Hossein Amiri, Andreas Zufle
arXiv:2507. 00028v2 Announce Type: replace Abstract: The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns.
By Lihuan Li, Hao Xue, Shuang Ao, Yang Song, Flora Salim
arXiv:2606. 00572v1 Announce Type: new Abstract: Passenger count data from public transit systems reveals urban mobility patterns and is essential for planning, operation, and optimisation.
By Oluwaleke Yusuf, Adil Rasheed, Frank Lindseth
arXiv:2607. 17694v1 Announce Type: new Abstract: Urban transportation systems generate heterogeneous data, yet these data do not automatically become actionable management intelligence.
By Junbiao Pang, Muhammad Ayub Sabir, Fatima Ashraf
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
The paper introduces RSTGCN, a Railway-centric Spatio-Temporal Graph Convolutional Network that predicts average arrival delays for all incoming trains at a specific station during a given time period. It incorporates train frequency-aware spatial attention and other architectural innovations to improve predictive accuracy. Experiments on a newly released dataset covering 4,735 Indian Railway stations show RSTGCN outperforms state‑of‑the‑art baselines by 18% in MAE, 14% in MAPE, and 1–8% in RMSE.
By Koyena Chowdhury, Paramita Koley, Abhijnan Chakraborty, Saptarshi Ghosh