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. 09539v1 Announce Type: new Abstract: Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems (ITS).
By Soban Nasir Lone, Mohamed Abouelela, Taeyoung Yu, Jiwon Kim, Constantinos Antoniou
arXiv:2603. 11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions.
By Yufeng Xin, Ethan Fan
arXiv:2606. 14956v1 Announce Type: new Abstract: Autonomous driving systems rely on precise trajectory prediction to plan safe and efficient movement.
By George Daoud, Mohamed El-Darieby
arXiv:2608. 04075v1 Announce Type: cross Abstract: Accurate traffic forecasting is essential for proactive resource management in edge computing, where service demand evolves dynamically across both space and time.
By Laha Ale, Letian Lin, Na Cao, Zheng Ma, Peng Yu
arXiv:2608.29658v1 Announce Type: cross
Abstract: Accurate traffic flow prediction is fundamental to intelligent transportation systems, playing a pivotal role in urban mobility optimization and smar...
By Limiao Zhang, Yuhui Lu, Jie Gao, Hao Jiang, Haiping Ma, Xingyi Zhang
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.
By Alessandro Scalese, Santhanakrishnan Narayanan, Constantinos Antoniou
arXiv:2609.13878v1 Announce Type: new
Abstract: Spatio-temporal traffic data are central to intelligent transportation systems, yet their heterogeneity poses significant challenges for large-scale mo...
By Zhouyang Liu, Jindong Han, Hao Wang, Xinyue Liu, Hui Gao, Dongsheng Li, Hao Liu
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap.
The paper introduces a deep architecture that jointly optimizes cost functions and a route-ranking model to accommodate diverse user preferences in route planning. It first generates a complete set of Pareto‑optimal routes using a multi‑objective Dijkstra algorithm, then employs a neural network that emulates shortest‑path search and ranking in an end‑to‑end differentiable framework. A novel loss function treats route preference as a constrained optimization problem, allowing a single objective to be optimized while other attributes remain constrained, and experiments on real‑world data show significant improvements over existing methods.
By Rui Zhao, Chao Chen, Longfei Xu, Chenguang Ji, Hengbin Cui, Kaikui Liu, Xiaolong Li
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
arXiv:2606. 21072v2 Announce Type: replace-cross Abstract: Traffic prediction is a core task in intelligent transportation systems and urban-scale decision making.
By Yongfeng Su, Hongwen Li, Zijian Zhang, Ziquan Fang, Lu Chen, Christian S. Jensen, Hong Gao, Yinjun Han