CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting - researchr publication related

researchr

You are not signed in
Sign in
Sign up

Zhengyang Zhou, Jiahao Shi, Hongbo Zhang, Qiongyu Chen, Xu Wang, Hongyang Chen, Yang Wang. CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting. In Luz Angelica Caudillo-Mata, Silvio Lattanzi, Andrés Muñoz Medina, Leman Akoglu, Aristides Gionis, Sergei Vassilvitskii, editors, Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024. pages 985-993, ACM, 2024. [doi]

The following publications are possibly variants of this publication:

Fast Spatiotemporal Learning Framework for Traffic Flow ForecastingCanyang Guo, Chi-Hua Chen, Feng-Jang Hwang, Ching-Chun Chang, Chin-Chen Chang 0001. tits, 24(8):8606-8616, August 2023. [doi]

Multi-view spatiotemporal learning for traffic forecastingCanyang Guo, Chi-Hua Chen, Feng-Jang Hwang, Ching-Chun Chang, Chin-Chen Chang 0001. isci, 657:119868, February 2024. [doi]

LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal DataBang An 0002, Xun Zhou, Amin Vahedian, W. Nick Street, Jinping Guan, Jun Luo 0007. icdm 2024: 11-20 [doi]

A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic ForecastingFuqiang Liu, Jiawei Wang, Jingbo Tian, Dingyi Zhuang, Luis Miranda-Moreno, Lijun Sun. tits, 23(10):19064-19075, 2022. [doi]

A Machine Learning Framework for Real-Time Traffic Density DetectionJing Chen, Evan Tan, Zhidong Li. ijprai, 23(7):1265-1284, 2009. [doi]

Dynamic spatial aware graph transformer for spatiotemporal traffic flow forecastingZequan Li, Jinglin Zhou, Zhizhe Lin, Teng Zhou. kbs, 297:111946, 2024. [doi]

runs on WebDSL