Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, CN-201804, China; Department of Architecture and Civil Engineering, Chalmers University of Technology, Goteburg SE-412 96, Sweden.
Department of Architecture and Civil Engineering, Chalmers University of Technology, Goteburg SE-412 96, Sweden.
Accid Anal Prev. 2024 May;199:107528. doi: 10.1016/j.aap.2024.107528. Epub 2024 Mar 5.
Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources. Special emphasis is put on modeling the spatial heterogeneity in the effects of a factor on crash frequency in different geographical areas in a data-driven way. The GW-RF model outperforms global models (e.g. Random Forest) and conventional geographically weighted regression, demonstrating superior predictive accuracy and elucidating spatial variations. The GW-RF model reveals spatial distinctions in the effects of certain factors on crash frequency. For example, the importance of intersection density varies significantly across regions, with high significance in the southern and northeastern areas. Low-grade road density emerges as influential in specific cities. The findings highlight the significance of different factors in influencing crash frequency across zones. Road network factors, particularly intersection density, exhibit high importance universally, while socioeconomic variables demonstrate moderate effects. Interestingly, land use variables show relatively lower importance. The outcomes could help to allocate resources and implement tailored interventions to reduce the likelihood of crashes.
由于交通事故数据中存在空间相关性和空间异质性，因此对交通事故的空间分析引起了广泛关注。本研究充分利用地理加权随机森林（GW-RF）模型，探索了美国交通事故频率与道路网络属性、社会经济特征和土地利用等多种影响因素之间的局部关联，这些因素的数据来源于多个数据源。本研究特别注重以数据驱动的方式建模因素对不同地理区域交通事故频率的影响中的空间异质性。GW-RF 模型优于全局模型（例如随机森林）和传统的地理加权回归模型，表现出更高的预测准确性，并阐明了空间变化。GW-RF 模型揭示了某些因素对交通事故频率的影响存在空间差异。例如，交叉口密度的重要性在不同地区差异显著，在南部和东北部地区具有较高的重要性。低等级道路密度在某些城市中具有影响力。研究结果强调了不同因素在不同区域影响交通事故频率的重要性。道路网络因素，特别是交叉口密度，普遍具有较高的重要性，而社会经济变量则表现出中等程度的影响。有趣的是，土地利用变量的重要性相对较低。研究结果可以帮助分配资源并实施有针对性的干预措施，以降低事故发生的可能性。
