Nonlinear impact analysis of built environment on urban road traffic safety risk
With the rapid development of economy, the increasing number of motor vehicles and the total road mileage, which leads to the increasingly prominent traffic safety problems. In order to explore the quantitative relationship between the built environment and the risk of urban road traffic safety, thi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2023.2268121 |
| _version_ | 1797447523713941504 |
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| author | Zhang Yaofang Chen Jian Qiu Zhixuan |
| author_facet | Zhang Yaofang Chen Jian Qiu Zhixuan |
| author_sort | Zhang Yaofang |
| collection | DOAJ |
| description | With the rapid development of economy, the increasing number of motor vehicles and the total road mileage, which leads to the increasingly prominent traffic safety problems. In order to explore the quantitative relationship between the built environment and the risk of urban road traffic safety, this paper reconstructs the built environment system based on the ‘5D' element model of the built environment combined with the factors influencing traffic safety risks, and describes the built environment from multiple aspects such as density, diversity &traffic design etc, and then build the gradient lift decision tree model to explore the importance and dependency of variables. The empirical analysis selects a district in Chongqing as the research unit, and the results show that: the RMSE the model was 0.0036, the MAPE was 1.9%, and the determination coefficient R2 was 0.84. GBDT algorithm results shows: the cumulative importance of population density, road facilities, intersection density, secondary road and branch road density, average intersection distance, land use mix, and economic density reaches 77.87%. Some variables show obvious nonlinearity and threshold effect. |
| first_indexed | 2024-03-09T13:57:14Z |
| format | Article |
| id | doaj.art-9bf1eec029584de79db8fb738c3574a5 |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-03-09T13:57:14Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-9bf1eec029584de79db8fb738c3574a52023-11-30T12:45:31ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832023-12-0111110.1080/21642583.2023.2268121Nonlinear impact analysis of built environment on urban road traffic safety riskZhang Yaofang0Chen Jian1Qiu Zhixuan2School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing, People’s Republic of ChinaSchool of Traffic & Transportation, Chongqing Jiaotong University, Chongqing, People’s Republic of ChinaSchool of Traffic & Transportation, Chongqing Jiaotong University, Chongqing, People’s Republic of ChinaWith the rapid development of economy, the increasing number of motor vehicles and the total road mileage, which leads to the increasingly prominent traffic safety problems. In order to explore the quantitative relationship between the built environment and the risk of urban road traffic safety, this paper reconstructs the built environment system based on the ‘5D' element model of the built environment combined with the factors influencing traffic safety risks, and describes the built environment from multiple aspects such as density, diversity &traffic design etc, and then build the gradient lift decision tree model to explore the importance and dependency of variables. The empirical analysis selects a district in Chongqing as the research unit, and the results show that: the RMSE the model was 0.0036, the MAPE was 1.9%, and the determination coefficient R2 was 0.84. GBDT algorithm results shows: the cumulative importance of population density, road facilities, intersection density, secondary road and branch road density, average intersection distance, land use mix, and economic density reaches 77.87%. Some variables show obvious nonlinearity and threshold effect.https://www.tandfonline.com/doi/10.1080/21642583.2023.2268121Built environmenttraffic safety risksboosting algorithmGBDT algorithm |
| spellingShingle | Zhang Yaofang Chen Jian Qiu Zhixuan Nonlinear impact analysis of built environment on urban road traffic safety risk Systems Science & Control Engineering Built environment traffic safety risks boosting algorithm GBDT algorithm |
| title | Nonlinear impact analysis of built environment on urban road traffic safety risk |
| title_full | Nonlinear impact analysis of built environment on urban road traffic safety risk |
| title_fullStr | Nonlinear impact analysis of built environment on urban road traffic safety risk |
| title_full_unstemmed | Nonlinear impact analysis of built environment on urban road traffic safety risk |
| title_short | Nonlinear impact analysis of built environment on urban road traffic safety risk |
| title_sort | nonlinear impact analysis of built environment on urban road traffic safety risk |
| topic | Built environment traffic safety risks boosting algorithm GBDT algorithm |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2023.2268121 |
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