Identification of patterns and predictive modeling of traffic accidents.

Objective

To develop predictive models for the prediction of traffic accidents.

Research questions

What are the characteristics of the locations where exists a higher number of traffic accidents?

Where and at what time of day is expected to exist a higher number of traffic accidents?

Literature review

(Almamlook et al., 2019; Dereli & Erdogan, 2017; Dogru & Subasi, 2018; Fancello et al., 2018; Hammad et al., 2019; Ihueze & Onwurah, 2018; La Torre et al., 2019; Le et al., 2020; Mohammed et al., 2019; Retallack & Ostendorf, 2019; Taamneh, Alkheder, et al., 2017; Taamneh, Taamneh, et al., 2017; Yuan et al., 2018)

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• Dereli, M. A., & Erdogan, S. (2017). A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods. Transportation Research Part A: Policy and Practice, 103, 106–117. https://doi.org/10.1016/j.tra.2017.05.031
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• Retallack, A. E., & Ostendorf, B. (2019). Current Understanding of the Effects of Congestion on Traffic Accidents. https://doi.org/10.3390/ijerph16183400
• Taamneh, M., Alkheder, S., & Taamneh, S. (2017). Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates. Journal of Transportation Safety and Security, 9(2), 146–166. https://doi.org/10.1080/19439962.2016.1152338
• Taamneh, M., Taamneh, S., & Alkheder, S. (2017). Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks. International Journal of Injury Control and Safety Promotion, 24(3), 388–395. https://doi.org/10.1080/17457300.2016.1224902
• Yuan, Z., Zhou, X., & Yang, T. (2018). Hetero-ConvLSTM: A deep learning approach to traffic accident prediction on heterogeneous Spatio-temporal data. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 984–992. https://doi.org/10.1145/3219819.3219922