Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor proximity or road-network topology. This paper presents a spatio-temporal prediction framework, developed to incorporate knowledge in various forms.
arXiv:2607. 24885v1 Announce Type: cross Abstract: Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility.
By Jinpeng Chen, Ziyu Yu, Tao Wang, Jun Ma, Hongbo Gao, Senzhang Wang, Zufeng Zhang, Kaimin Wei
Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch. Deep learning models, including graph convolutional networks, recurrent networks, and Transformers, achieve strong results on standard benchmarks, but their architectures are designed by hand, requiring significant expert effort and producing models that often generalize poorly across cities and datasets.
arXiv:2606. 09872v1 Announce Type: cross Abstract: Traffic forecasting is a fundamental component of intelligent transportation systems, yet remains challenging in real-world settings due to irregular sensor distributions and the high computational cost of modeling large-scale spatiotemporal dependencies.
By Jichao Li, Xuanming Shi
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:2607. 09741v1 Announce Type: cross Abstract: Accurate trajectory prediction in autonomous driving hinges on modeling dynamic and context-dependent interactions among traffic agents.
By Chengyue Wang, Bin Rao, Haicheng Liao, Bonan Wang, Chengzhong Xu, Zhenning Li