Crash severity prediction and interpretation for road determinants based on a hybrid method

Author:

Cheng Chuangang123ORCID,Chen Shuyan123ORCID,Ma Yongfeng123ORCID,Qiao Fengxiang4ORCID,Xie Zhuopeng123ORCID

Affiliation:

1. School of Transportation, Southeast University, Nanjing, China

2. Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing, China

3. Jiangsu Province Collaborative Innovation Center of Modem Urban Traffic Technologies, Southeast University, Nanjing, China

4. Innovative Transportation Research Institute, Texas Southern University, Houston, Texas, USA

Funder

National Natural Science Foundation of China

Transport Science & Technology Project of Jiangsu Province

Key R &D Program of Ningxia Hui Autonomous Region

Publisher

Informa UK Limited

Reference45 articles.

1. AASHTO. (2016). Manual for Assessing Safety Hardware (MASH). Manual for Assessing Safety Hardware (trb.org).

2. Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

3. Alberson D. Bligh R. Bullard D. Lord D. Miaou S. & Van Schalkwyk I. (2007). Overview of the development of a TxDOT in-service performance evaluation process for roadside safety features. 0-4366-s.pdf (utexas.edu).

4. The impact of higher speed limits on the frequency and severity of freeway crashes: Accounting for temporal shifts and unobserved heterogeneity

5. The effect of natural and built environmental characteristics on pedestrian-vehicle crash severity in Ghana

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