Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States.
Accid Anal Prev. 2011 Jan;43(1):461-70. doi: 10.1016/j.aap.2010.10.002. Epub 2010 Nov 2.
A recently developed machine learning technique, multivariate adaptive regression splines (MARS), is introduced in this study to predict vehicles' angle crashes. MARS has a promising prediction power, and does not suffer from interpretation complexity. Negative Binomial (NB) and MARS models were fitted and compared using extensive data collected on unsignalized intersections in Florida. Two models were estimated for angle crash frequency at 3- and 4-legged unsignalized intersections. Treating crash frequency as a continuous response variable for fitting a MARS model was also examined by considering the natural logarithm of the crash frequency. Finally, combining MARS with another machine learning technique (random forest) was explored and discussed. The fitted NB angle crash models showed several significant factors that contribute to angle crash occurrence at unsignalized intersections such as, traffic volume on the major road, the upstream distance to the nearest signalized intersection, the distance between successive unsignalized intersections, median type on the major approach, percentage of trucks on the major approach, size of the intersection and the geographic location within the state. Based on the mean square prediction error (MSPE) assessment criterion, MARS outperformed the corresponding NB models. Also, using MARS for predicting continuous response variables yielded more favorable results than predicting discrete response variables. The generated MARS models showed the most promising results after screening the covariates using random forest. Based on the results of this study, MARS is recommended as an efficient technique for predicting crashes at unsignalized intersections (angle crashes in this study).
本研究引入了一种新的机器学习技术——多元自适应回归样条（MARS），用于预测车辆的角度碰撞。MARS 具有很好的预测能力，并且不会受到解释复杂性的影响。使用在佛罗里达州无信号交叉口收集的大量数据，对负二项式（NB）和 MARS 模型进行了拟合和比较。在 3 腿和 4 腿无信号交叉口，分别对角度碰撞频率进行了两个模型的估计。通过考虑碰撞频率的自然对数，将碰撞频率视为连续响应变量来拟合 MARS 模型也进行了检验。最后，探讨和讨论了将 MARS 与另一种机器学习技术（随机森林）相结合的方法。拟合的 NB 角度碰撞模型显示了几个显著的因素，这些因素导致无信号交叉口的角度碰撞发生，如主要道路上的交通量、到最近的信号交叉口的上游距离、连续无信号交叉口之间的距离、主要入口处的中央隔离带类型、主要入口处的卡车比例、交叉口的大小以及州内的地理位置。基于均方预测误差（MSPE）评估标准，MARS 优于相应的 NB 模型。此外，使用 MARS 预测连续响应变量比预测离散响应变量产生更有利的结果。使用随机森林筛选协变量后，生成的 MARS 模型显示出最有前途的结果。基于这项研究的结果，建议将 MARS 作为一种预测无信号交叉口（本研究中的角度碰撞）碰撞的有效技术。
