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A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis.

Authors :
Zhang, Chen
He, Jie
Wang, Yinhai
Yan, Xintong
Zhang, Changjian
Chen, Yikai
Liu, Ziyang
Zhou, Bojian
Source :
Discrete Dynamics in Nature & Society. 6/30/2020, p1-13. 13p.
Publication Year :
2020

Abstract

Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10260226
Database :
Academic Search Index
Journal :
Discrete Dynamics in Nature & Society
Publication Type :
Academic Journal
Accession number :
144317337
Full Text :
https://doi.org/10.1155/2020/4013185