Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data
Currently, research on road traffic safety is mostly focused on traffic safety evaluations based on statistical indices for accidents. There is still a need for in-depth investigation on preaccident identification of safety risks. In this study, the correlations between high-incidence locations for...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/3024101 |
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| _version_ | 1832566432914735104 |
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| author | Xiaoyu Cai Cailin Lei Bo Peng Xiaoyong Tang Zhigang Gao |
| author_facet | Xiaoyu Cai Cailin Lei Bo Peng Xiaoyong Tang Zhigang Gao |
| author_sort | Xiaoyu Cai |
| collection | DOAJ |
| description | Currently, research on road traffic safety is mostly focused on traffic safety evaluations based on statistical indices for accidents. There is still a need for in-depth investigation on preaccident identification of safety risks. In this study, the correlations between high-incidence locations for aberrant driving behaviors and locations of road traffic accidents are analyzed based on vehicle OBD data. A road traffic safety risk estimation index system with road traffic safety entropy (RTSE) as the primary index and rapid acceleration frequency, rapid deceleration frequency, rapid turning frequency, speeding frequency, and high-speed neutral coasting frequency as secondary indices is established. A calculation method of RTSE is proposed based on an improved entropy weight method. This method involves three aspects, namely, optimization of the base of the logarithm, processing of zero-value secondary indices, and piecewise calculation of the weight of each index. Additionally, a safety risk level determination method based on two-step clustering (density and k-means clustering) is also proposed, which prevents isolated data points from affecting safety risk classification. A risk classification threshold calculation method is formulated based on k-mean clustering. The results show that high-incidence locations for aberrant driving behaviors are consistent with the locations of traffic accidents. The proposed methods are validated through a case study on four roads in Chongqing with a total length of approximately 38 km. The results show that the road traffic safety trends characterized by road safety entropy and traffic accidents are consistent. |
| format | Article |
| id | doaj-art-b5edf9bf06aa4ca3ad57643ee390096b |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-b5edf9bf06aa4ca3ad57643ee390096b2025-02-03T01:04:17ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/30241013024101Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic DataXiaoyu Cai0Cailin Lei1Bo Peng2Xiaoyong Tang3Zhigang Gao4Chongqing Jiaotong University, College of Traffic and Transportation, Chongqing 400074, ChinaChongqing Jiaotong University, College of Traffic and Transportation, Chongqing 400074, ChinaChongqing Jiaotong University, College of Traffic and Transportation, Chongqing 400074, ChinaUrban Transportation Big Data Engineering Technology Research Center of Chongqing, Chongqing 400020, ChinaUrban Transportation Big Data Engineering Technology Research Center of Chongqing, Chongqing 400020, ChinaCurrently, research on road traffic safety is mostly focused on traffic safety evaluations based on statistical indices for accidents. There is still a need for in-depth investigation on preaccident identification of safety risks. In this study, the correlations between high-incidence locations for aberrant driving behaviors and locations of road traffic accidents are analyzed based on vehicle OBD data. A road traffic safety risk estimation index system with road traffic safety entropy (RTSE) as the primary index and rapid acceleration frequency, rapid deceleration frequency, rapid turning frequency, speeding frequency, and high-speed neutral coasting frequency as secondary indices is established. A calculation method of RTSE is proposed based on an improved entropy weight method. This method involves three aspects, namely, optimization of the base of the logarithm, processing of zero-value secondary indices, and piecewise calculation of the weight of each index. Additionally, a safety risk level determination method based on two-step clustering (density and k-means clustering) is also proposed, which prevents isolated data points from affecting safety risk classification. A risk classification threshold calculation method is formulated based on k-mean clustering. The results show that high-incidence locations for aberrant driving behaviors are consistent with the locations of traffic accidents. The proposed methods are validated through a case study on four roads in Chongqing with a total length of approximately 38 km. The results show that the road traffic safety trends characterized by road safety entropy and traffic accidents are consistent.http://dx.doi.org/10.1155/2020/3024101 |
| spellingShingle | Xiaoyu Cai Cailin Lei Bo Peng Xiaoyong Tang Zhigang Gao Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data Journal of Advanced Transportation |
| title | Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data |
| title_full | Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data |
| title_fullStr | Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data |
| title_full_unstemmed | Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data |
| title_short | Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data |
| title_sort | road traffic safety risk estimation method based on vehicle onboard diagnostic data |
| url | http://dx.doi.org/10.1155/2020/3024101 |
| work_keys_str_mv | AT xiaoyucai roadtrafficsafetyriskestimationmethodbasedonvehicleonboarddiagnosticdata AT cailinlei roadtrafficsafetyriskestimationmethodbasedonvehicleonboarddiagnosticdata AT bopeng roadtrafficsafetyriskestimationmethodbasedonvehicleonboarddiagnosticdata AT xiaoyongtang roadtrafficsafetyriskestimationmethodbasedonvehicleonboarddiagnosticdata AT zhiganggao roadtrafficsafetyriskestimationmethodbasedonvehicleonboarddiagnosticdata |