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
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:2607. 26467v1 Announce Type: new Abstract: Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch.
By Truong Giang Vu, Li Yang, Richard W. Pazzi
arXiv:2607. 23467v1 Announce Type: new Abstract: We study an integrated pickup-and-delivery problem on sparse, non-Euclidean networks that jointly optimizes cyclic routing, cargo flow allocation, and cross-cycle service.
By Haomiao Sun, Fang He, Congyuan Ji, Xindi Tang
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:2509. 07123v2 Announce Type: replace-cross Abstract: Generalized extreme value models capture dependence among choice alternatives in discrete choice modeling, but require this dependence to be predefined, symmetric, and shared uniformly across individuals.
By Yuqi Zhou, Zhanhong Cheng, Dingyi Zhuang, Lingqian Hu, Yuheng Bu, Shenhao Wang