A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest
Vulnerable road users (VRUs) are usually more susceptible to fatal injuries. Accurate and rapid assessment of VRU injury severity at the accident scene can provide timely support for decision-making in emergency response. However, evaluating VRU injury severity at the accident scene usually requires...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-04-01
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| Series: | Electronic Research Archive |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023173?viewType=HTML |
| _version_ | 1797808502347923456 |
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| author | Ye Yu Zhiyuan Liu |
| author_facet | Ye Yu Zhiyuan Liu |
| author_sort | Ye Yu |
| collection | DOAJ |
| description | Vulnerable road users (VRUs) are usually more susceptible to fatal injuries. Accurate and rapid assessment of VRU injury severity at the accident scene can provide timely support for decision-making in emergency response. However, evaluating VRU injury severity at the accident scene usually requires medical knowledge and medical devices. Few studies have explored the possibility of using on-site positional relationship to assess injury severity, which could provide a new perspective for on-site transportation professionals to assess accident severity. This study proposes a data-driven on-site injury severity assessment model for car-to-electric-bicycle accidents based on the relationship between the final resting positions of the car, electric bicycle and cyclist at the accident scene. Random forest is employed to learn the accident features from the at-scene positional relationship among accident participants, by which injury severity of the cyclist is assessed. Conditional permutation importance, which can account for correlation among predictor variables, is adopted to reflect the importance of predictor variables more accurately. The proposed model is demonstrated using simulated car-to-electric-bicycle collision data. The results show that the proposed model has good performance in terms of overall accuracy and is balanced in recognizing both fatal and non-fatal accidents. Model performance under partial information confirms that the position information of the electric bicycle is more important than the position information of the cyclist in assessing injury severity. |
| first_indexed | 2024-03-13T06:38:30Z |
| format | Article |
| id | doaj.art-cbd71fe5cffe4b1b981cd55718dda406 |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-03-13T06:38:30Z |
| publishDate | 2023-04-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-cbd71fe5cffe4b1b981cd55718dda4062023-06-09T01:09:36ZengAIMS PressElectronic Research Archive2688-15942023-04-013163417343410.3934/era.2023173A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forestYe Yu0Zhiyuan Liu11. Department of Public Security Management, Jiangsu Police Institute, Nanjing, Jiangsu, China2. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, ChinaVulnerable road users (VRUs) are usually more susceptible to fatal injuries. Accurate and rapid assessment of VRU injury severity at the accident scene can provide timely support for decision-making in emergency response. However, evaluating VRU injury severity at the accident scene usually requires medical knowledge and medical devices. Few studies have explored the possibility of using on-site positional relationship to assess injury severity, which could provide a new perspective for on-site transportation professionals to assess accident severity. This study proposes a data-driven on-site injury severity assessment model for car-to-electric-bicycle accidents based on the relationship between the final resting positions of the car, electric bicycle and cyclist at the accident scene. Random forest is employed to learn the accident features from the at-scene positional relationship among accident participants, by which injury severity of the cyclist is assessed. Conditional permutation importance, which can account for correlation among predictor variables, is adopted to reflect the importance of predictor variables more accurately. The proposed model is demonstrated using simulated car-to-electric-bicycle collision data. The results show that the proposed model has good performance in terms of overall accuracy and is balanced in recognizing both fatal and non-fatal accidents. Model performance under partial information confirms that the position information of the electric bicycle is more important than the position information of the cyclist in assessing injury severity.https://www.aimspress.com/article/doi/10.3934/era.2023173?viewType=HTMLtraffic safetydata-drivenassessmentrandom forestvulnerable road user |
| spellingShingle | Ye Yu Zhiyuan Liu A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest Electronic Research Archive traffic safety data-driven assessment random forest vulnerable road user |
| title | A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest |
| title_full | A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest |
| title_fullStr | A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest |
| title_full_unstemmed | A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest |
| title_short | A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest |
| title_sort | data driven on site injury severity assessment model for car to electric bicycle collisions based on positional relationship and random forest |
| topic | traffic safety data-driven assessment random forest vulnerable road user |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023173?viewType=HTML |
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