Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model
Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/3771640 |
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| _version_ | 1832566265755992064 |
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| author | Shubo Wu Quan Yuan Zhongwei Yan Qing Xu |
| author_facet | Shubo Wu Quan Yuan Zhongwei Yan Qing Xu |
| author_sort | Shubo Wu |
| collection | DOAJ |
| description | Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors. |
| format | Article |
| id | doaj-art-2e3028ffefc94c7ba088b7f165e46b73 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-2e3028ffefc94c7ba088b7f165e46b732025-02-03T01:04:32ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/37716403771640Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) ModelShubo Wu0Quan Yuan1Zhongwei Yan2Qing Xu3Merchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaVehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors.http://dx.doi.org/10.1155/2021/3771640 |
| spellingShingle | Shubo Wu Quan Yuan Zhongwei Yan Qing Xu Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model Journal of Advanced Transportation |
| title | Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model |
| title_full | Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model |
| title_fullStr | Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model |
| title_full_unstemmed | Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model |
| title_short | Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model |
| title_sort | analyzing accident injury severity via an extreme gradient boosting xgboost model |
| url | http://dx.doi.org/10.1155/2021/3771640 |
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