Department of Civil and Environmental Engineering, University of Massachusetts Lowell, 1 University Ave, Lowell, MA 01854, United States.
Center for Transportation Safety, Texas A&M Transportation Institute, 3135 TAMU, College Station, TX 77843, United States.
Accid Anal Prev. 2021 Sep;159:106261. doi: 10.1016/j.aap.2021.106261. Epub 2021 Jun 25.
Understanding and quantifying the effects of risk factors on crash frequency is of great importance for developing cost-effective safety countermeasures. In this paper, the effects of key crash contributing factors on total crashes and crashes of different collision types are analyzed separately and compared. A novel Machine Learning (ML) method, Light Gradient Boosting Machine (LightGBM), is introduced to model a Texas dataset consisting of vehicle crashes occurred from 2015 to 2017. Compared with other commonly used ML methods such as eXtreme Gradient Boosting (XGBoost), LightGBM performs significantly better in terms of mean absolute error (MAE) and root mean squared error (RMSE). In addition, the SHapley Additive explanation (SHAP) approach is employed to interpret the LightGBM outputs. Significant risk factors are identified, including speed limits, area type, number of lanes, roadway functional class, shoulder width and shoulder type. With the SHAP method, the importance, total effects, and main and interaction effects of risk factors are quantified. The results suggest that the importance of risk factors vary across collision types. Speed limit is a more important risk factor than right/left shoulder width, lane width, and median width for Rear-End (RE) crashes, while the opposite relationship is found for Run-Off-Road (ROR) crashes. Also, it is found that narrow lanes (8ft to 11ft) increase the risk for all types of crashes (i.e., Total, ROR, and RE) in this study. For road segments with 5 or 6 lanes in both directions combined, a lane width greater than or equal to 12ft may help reduce the risk of all types of crashes. These results have important implications for developing accurate crash modification factors and cost-effective safety countermeasures.
理解和量化危险因素对碰撞频率的影响对于制定具有成本效益的安全对策非常重要。本文分别分析和比较了关键碰撞因素对总碰撞和不同碰撞类型碰撞的影响。引入了一种新的机器学习（ML）方法——Light Gradient Boosting Machine（LightGBM），用于对 2015 年至 2017 年发生的车辆碰撞的德克萨斯州数据集进行建模。与其他常用的 ML 方法（如 eXtreme Gradient Boosting（XGBoost））相比，LightGBM 在平均绝对误差（MAE）和均方根误差（RMSE）方面表现更好。此外，采用 SHapley Additive explanation（SHAP）方法对 LightGBM 的输出进行解释。确定了显著的危险因素，包括限速、区域类型、车道数、道路功能等级、路肩宽度和路肩类型。通过 SHAP 方法，量化了危险因素的重要性、总效应、主效应和交互效应。结果表明，危险因素的重要性因碰撞类型而异。限速对追尾（RE）碰撞比右侧/左侧路肩宽度、车道宽度和中央分隔带宽度更为重要，而对于驶出路外（ROR）碰撞则相反。此外，研究还发现，狭窄的车道（8 英尺至 11 英尺）增加了所有类型碰撞（即总碰撞、ROR 和 RE）的风险。对于双向车道数为 5 或 6 条的路段，车道宽度大于或等于 12 英尺可能有助于降低所有类型碰撞的风险。这些结果对于制定准确的碰撞修正因子和具有成本效益的安全对策具有重要意义。
