Citation

Hu Q, Cai M, Mohabbati-Kalejahi N, Mehdizadeh A, Alamdar Yazdi MA, Vinel A, Rigdon SE, Davis KC, Megahed FM. Sensors (Basel) 2020; 20(4): e1096.

Affiliation

Farmer School of Business, Miami University, Oxford, OH 45056, USA.

Copyright

(Copyright © 2020, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s20041096

PMID

32079346

Abstract

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 k-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.

Language: en

Keywords

crash risk modeling; hazardous materials; highway safety; operations research; prescriptive analytics; shortest path problem; trucking; vehicle routing problem