A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/4013185 |
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| author | Chen Zhang Jie He Yinhai Wang Xintong Yan Changjian Zhang Yikai Chen Ziyang Liu Bojian Zhou |
| author_facet | Chen Zhang Jie He Yinhai Wang Xintong Yan Changjian Zhang Yikai Chen Ziyang Liu Bojian Zhou |
| author_sort | Chen Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-663022bc8d824450b25c29559b87faf0 |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-663022bc8d824450b25c29559b87faf02025-08-20T02:23:49ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/40131854013185A Crash Severity Prediction Method Based on Improved Neural Network and Factor AnalysisChen Zhang0Jie He1Yinhai Wang2Xintong Yan3Changjian Zhang4Yikai Chen5Ziyang Liu6Bojian Zhou7School of Transportation, Southeast University, Sipailou 2#, Nanjing 210018, Jiangsu, ChinaSchool of Transportation, Southeast University, Sipailou 2#, Nanjing 210018, Jiangsu, ChinaSmart Transportation Applications and Research Laboratory, University of Washington, Seattle, WA 98195-2700, ChinaSchool of Transportation, Southeast University, Sipailou 2#, Nanjing 210018, Jiangsu, ChinaSchool of Transportation, Southeast University, Sipailou 2#, Nanjing 210018, Jiangsu, ChinaSchool of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Transportation, Southeast University, Sipailou 2#, Nanjing 210018, Jiangsu, ChinaSchool of Transportation, Southeast University, Sipailou 2#, Nanjing 210018, Jiangsu, ChinaCrash 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.http://dx.doi.org/10.1155/2020/4013185 |
| spellingShingle | Chen Zhang Jie He Yinhai Wang Xintong Yan Changjian Zhang Yikai Chen Ziyang Liu Bojian Zhou A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis Discrete Dynamics in Nature and Society |
| title | A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis |
| title_full | A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis |
| title_fullStr | A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis |
| title_full_unstemmed | A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis |
| title_short | A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis |
| title_sort | crash severity prediction method based on improved neural network and factor analysis |
| url | http://dx.doi.org/10.1155/2020/4013185 |
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