Traffic Risk Assessment Based on Warning Data
To address the issues of insufficient danger excavation and long data collection period in traditional traffic risk assessment methods, this paper proposes a risk assessment method based on driver’s improper driving behavior and abnormal vehicle state warning data. Meanwhile, this paper analyses the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/1191239 |
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| _version_ | 1832561785774800896 |
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| author | Tao Wang Binbin Chen Yuzhi Chen Shejun Deng Jun Chen |
| author_facet | Tao Wang Binbin Chen Yuzhi Chen Shejun Deng Jun Chen |
| author_sort | Tao Wang |
| collection | DOAJ |
| description | To address the issues of insufficient danger excavation and long data collection period in traditional traffic risk assessment methods, this paper proposes a risk assessment method based on driver’s improper driving behavior and abnormal vehicle state warning data. Meanwhile, this paper analyses the built environment’s impact on traffic risk using the spatial econometric model. Firstly, a risk assessment system with the relative incidence of driver’s improper driving behavior (eye closure, yawn, and looking away) and abnormal vehicle state (rapid acceleration, rapid deceleration, and lane departure) warnings as assessment indicators is constructed. Then, the risk responsibility weights of each warning type were determined using the entropy weight method. The risk classification thresholds were determined based on the Gaussian Mixture Model algorithm. Finally, a spatial econometric model was used to quantify the impact of built environment factors characterized by Point of Interest (POI) data on regional traffic risk, with the results of risk class classification as the dependent variable. The data of bus vehicle warnings in Zhenjiang, Jiangsu Province, are employed as an example for validation. The geographic cell of 1 km × 1 km scale is applied as the basic risk assessment unit. The results show that the optimal risk classification threshold for road traffic risk levels I and II is 1.92, the accuracy rate of class classification is 79.3%; the optimal risk classification threshold for levels II and III is 0.75, and the accuracy rate of class classification is 83.4%. The number of residential areas, Point of Interest (POI) mixing degree, and bus stops were significantly and positively correlated with transit traffic risk. The study results provide references for developing customized accident prevention measures and the appropriate setting of urban supporting facilities. |
| format | Article |
| id | doaj-art-9fbcc38785654166a038908f83177f7a |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-9fbcc38785654166a038908f83177f7a2025-02-03T01:24:10ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/1191239Traffic Risk Assessment Based on Warning DataTao Wang0Binbin Chen1Yuzhi Chen2Shejun Deng3Jun Chen4Guangxi Education Department Key Laboratory of ITSGuangxi Education Department Key Laboratory of ITSSchool of TransportationCollege of Civil Science and EngineeringSchool of TransportationTo address the issues of insufficient danger excavation and long data collection period in traditional traffic risk assessment methods, this paper proposes a risk assessment method based on driver’s improper driving behavior and abnormal vehicle state warning data. Meanwhile, this paper analyses the built environment’s impact on traffic risk using the spatial econometric model. Firstly, a risk assessment system with the relative incidence of driver’s improper driving behavior (eye closure, yawn, and looking away) and abnormal vehicle state (rapid acceleration, rapid deceleration, and lane departure) warnings as assessment indicators is constructed. Then, the risk responsibility weights of each warning type were determined using the entropy weight method. The risk classification thresholds were determined based on the Gaussian Mixture Model algorithm. Finally, a spatial econometric model was used to quantify the impact of built environment factors characterized by Point of Interest (POI) data on regional traffic risk, with the results of risk class classification as the dependent variable. The data of bus vehicle warnings in Zhenjiang, Jiangsu Province, are employed as an example for validation. The geographic cell of 1 km × 1 km scale is applied as the basic risk assessment unit. The results show that the optimal risk classification threshold for road traffic risk levels I and II is 1.92, the accuracy rate of class classification is 79.3%; the optimal risk classification threshold for levels II and III is 0.75, and the accuracy rate of class classification is 83.4%. The number of residential areas, Point of Interest (POI) mixing degree, and bus stops were significantly and positively correlated with transit traffic risk. The study results provide references for developing customized accident prevention measures and the appropriate setting of urban supporting facilities.http://dx.doi.org/10.1155/2022/1191239 |
| spellingShingle | Tao Wang Binbin Chen Yuzhi Chen Shejun Deng Jun Chen Traffic Risk Assessment Based on Warning Data Journal of Advanced Transportation |
| title | Traffic Risk Assessment Based on Warning Data |
| title_full | Traffic Risk Assessment Based on Warning Data |
| title_fullStr | Traffic Risk Assessment Based on Warning Data |
| title_full_unstemmed | Traffic Risk Assessment Based on Warning Data |
| title_short | Traffic Risk Assessment Based on Warning Data |
| title_sort | traffic risk assessment based on warning data |
| url | http://dx.doi.org/10.1155/2022/1191239 |
| work_keys_str_mv | AT taowang trafficriskassessmentbasedonwarningdata AT binbinchen trafficriskassessmentbasedonwarningdata AT yuzhichen trafficriskassessmentbasedonwarningdata AT shejundeng trafficriskassessmentbasedonwarningdata AT junchen trafficriskassessmentbasedonwarningdata |