Analyzing the Leading Causes of Traffic Fatalities Using XGBoost and Grid-Based Analysis: A City Management Perspective
Traffic accidents have been one of the most important global public problems. It has caused a severe loss of human lives and property every year. Studying the influential factors of accidents can help find the reasons behind. This can facilitate the design of effective measures and policies to reduc...
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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.148059-148072 |
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| creator | Ma, Jun Ding, Yuexiong Cheng, Jack C. P. Tan, Yi Gan, Vincent J. L. Zhang, Jingcheng |
| description | Traffic accidents have been one of the most important global public problems. It has caused a severe loss of human lives and property every year. Studying the influential factors of accidents can help find the reasons behind. This can facilitate the design of effective measures and policies to reduce the traffic fatality rate and improve road safety. However, most of the existing research either adopted methods based on linear assumption or neglected to further evaluate the spatial relationships. In this paper, we proposed a methodology framework based on XGBoost and grid analysis to spatially analyze the leading factors on traffic fatality in Los Angeles County. Characteristics of the collision, time and location, and environmental factors are considered. Results show that the proposed method has the best modeling performance compared with other commonly seen machine learning algorithms. Eight factors are found to have the leading impact on traffic fatality. Spatial relationships between the eight factors and the fatality rates within the Los Angeles County are further studied using the grid-based analysis in GIS. Specific suggestions on how to reduce the fatality rate and improve road safety are provided accordingly. |
| doi_str_mv | 10.1109/ACCESS.2019.2946401 |
| format | Article |
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Results show that the proposed method has the best modeling performance compared with other commonly seen machine learning algorithms. Eight factors are found to have the leading impact on traffic fatality. Spatial relationships between the eight factors and the fatality rates within the Los Angeles County are further studied using the grid-based analysis in GIS. 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| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Accidents Algorithms factors analysis Fatalities Geographic information systems GIS grid-based analysis Machine learning Machine learning algorithms Mathematical model non-linear machine learning Road safety Support vector machines Traffic accidents Traffic accidents & safety traffic fatality Traffic management Traffic safety XGBoost |
| title | Analyzing the Leading Causes of Traffic Fatalities Using XGBoost and Grid-Based Analysis: A City Management Perspective |
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