A deep spatiotemporal network for forecasting the risk of traffic accidents in low-risk regions
Author:
Funder
Project of Science and Technology Plan of Fujian Province
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07971-2.pdf
Reference22 articles.
1. Yuan Z, Zhou X, Yang T (2018) Hetero-convlstm: a deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 984–992
2. Huang C, Zhang C, Dai P, Bo L (2019) Deep dynamic fusion network for traffic accident forecasting. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 2673–2681
3. Huang C, Zhang C, Zhao J, Wu X, Yin D, Chawla N (2019) Mist: a multiview and multimodal spatial-temporal learning framework for citywide abnormal event forecasting. In: The world wide web conference, pp 717–728
4. Lv Y, Tang S, Zhao H (2009) Real-time highway traffic accident prediction based on the k-nearest neighbor method. In: 2009 international conference on measuring technology and mechatronics automation, vol 3. pp 547–550
5. Lin L, Wang Q, Sadek AW (2015) A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction. Transp Res Part C Emerg Technol 55:444–459
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