Probability of Roadside Accidents for Curved Sections on Highways
To predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic i...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2020/9656434 |
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doaj-d6aa386de723434fbc5f46cf8a1694bf2020-11-25T03:08:23ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/96564349656434Probability of Roadside Accidents for Curved Sections on HighwaysGuozhu Cheng0Rui Cheng1Yulong Pei2Liang Xu3School of Traffic and Transportation, Northeast Forestry University, 150040 Harbin, ChinaSchool of Traffic and Transportation, Northeast Forestry University, 150040 Harbin, ChinaSchool of Traffic and Transportation, Northeast Forestry University, 150040 Harbin, ChinaSchool of Civil Engineering, Changchun Institute of Technology, 130012 Changchun, ChinaTo predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors and explore the influence of different combinations of significant risk factors on roadside accidents according to the generated decision rules, so as to propose specific improved countermeasures as the reference for the revision of the Design Specification for Highway Alignment (JTG D20-2017) of China. Considering the effects of related interactions among different risk factors on roadside accidents, path analysis was applied to investigate the importance of the significant risk factors. The results showed that the significant risk factors were in decreasing order of importance, vehicle speed, horizontal curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope. The first five important factors were chosen as predictors of the probability of roadside accidents in the Bayesian network analysis to establish the probability prediction model of roadside accidents. Eventually, the thresholds of the various factors for roadside accident blackspot identification were given according to probabilistic prediction results.http://dx.doi.org/10.1155/2020/9656434 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guozhu Cheng Rui Cheng Yulong Pei Liang Xu |
spellingShingle |
Guozhu Cheng Rui Cheng Yulong Pei Liang Xu Probability of Roadside Accidents for Curved Sections on Highways Mathematical Problems in Engineering |
author_facet |
Guozhu Cheng Rui Cheng Yulong Pei Liang Xu |
author_sort |
Guozhu Cheng |
title |
Probability of Roadside Accidents for Curved Sections on Highways |
title_short |
Probability of Roadside Accidents for Curved Sections on Highways |
title_full |
Probability of Roadside Accidents for Curved Sections on Highways |
title_fullStr |
Probability of Roadside Accidents for Curved Sections on Highways |
title_full_unstemmed |
Probability of Roadside Accidents for Curved Sections on Highways |
title_sort |
probability of roadside accidents for curved sections on highways |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2020-01-01 |
description |
To predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors and explore the influence of different combinations of significant risk factors on roadside accidents according to the generated decision rules, so as to propose specific improved countermeasures as the reference for the revision of the Design Specification for Highway Alignment (JTG D20-2017) of China. Considering the effects of related interactions among different risk factors on roadside accidents, path analysis was applied to investigate the importance of the significant risk factors. The results showed that the significant risk factors were in decreasing order of importance, vehicle speed, horizontal curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope. The first five important factors were chosen as predictors of the probability of roadside accidents in the Bayesian network analysis to establish the probability prediction model of roadside accidents. Eventually, the thresholds of the various factors for roadside accident blackspot identification were given according to probabilistic prediction results. |
url |
http://dx.doi.org/10.1155/2020/9656434 |
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