Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA.
College for Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA.
Sensors (Basel). 2020 Feb 17;20(4):1096. doi: 10.3390/s20041096.
In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research.
在综述的第一部分，我们观察到，在与分别涉及碰撞风险预测和最小化的预测模型和规定性模型之间存在显著差距。在这一部分，我们回顾和分类了侧重于最小化碰撞风险的优化/规定性分析模型。尽管文献的这一部分的大多数工作都与危险材料（危险物质）卡车运输问题有关，但我们表明（有些例外）许多模型也可以用于非危险物质场景。为了突出碰撞风险预测模型对规定性模型获得的累积风险的影响，我们提出了一个模拟示例，其中我们在最短路径算法中使用了四个风险指标（来自逻辑回归、泊松回归、XGBoost 和神经网络）。从我们的示例中，我们展示了两个主要的设计要点：（a）最短路径不一定会导致最低的碰撞风险，以及（b）预测性能的相似性不一定会转化为规定性模型的相似结果。基于综述和示例，我们强调了未来研究的几个方向。
