A Review of Learning-Based Traffic Accident Prediction Models and Their Opportunities to Improve Information Security

Author:

Marcillo Pablo,Barona López Lorena Isabel,Valdivieso Caraguay Ángel Leonardo,Hernández-Álvarez Myriam

Publisher

Springer International Publishing

Reference21 articles.

1. World Health Organization: WHO—Global status report on road safety2018. WHO (2018). http://www.who.int/violence_injury_prevention/road_safety_status/2018/en/

2. Zhang, X., Huang, F., Zheng, C.: Causes analysis of the serious road traffic accidents cases. In: 2016 5th International Conference on Energy and Environmental Protection (ICEEP 2016). Atlantis Press (2016)

3. Zhou, Z., Chen, L., Zhu, C., Wang, P.: Stack resnet for short-term accident risk prediction leveraging cross-domain data. In: Chinese Automation Congress (CAC), pp. 782–787. IEEE (2019)

4. Yuan, Z., Zhou, X., Yang, T., Tamerius, J., Mantilla, R.: Predicting traffic accidents through heterogeneous urban data: a case study. In: Proceedings of the 6th International Workshop on Urban Computing (UrbComp 2017), Halifax, NS, Canada, vol. 14 (2017)

5. Ren, H., Song, Y., Wang, J., Hu, Y., Lei, J.: A deep learning approach to the citywide traffic accident risk prediction. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3346–3351. IEEE (2018)

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