Optimization of Highway Safety and Operation by Using Crash Prediction Models with Accident Modification Factors

Abstract

The linear programming model that is presented optimizes the crash and delay costs of highway networks by selecting safety and operational improvements with implementation costs within a constrained budget. The costs that are minimized in this study are those of crashes plus the costs of delay times. This optimization model works with crash prediction models that estimate the expected number of crashes by using base models and accident modification factors (AMFs). In these models the base model predicts the expected number of crashes for a base highway or intersection. The AMFs modify the prediction on the basis of the specific characteristics of each highway segment or intersection. Several improvement alternatives for different highways and intersections within a network would mean many improvement combinations, each with a different cost for implementation, which would cause certain crashes and delay. The proposed model uses linear optimization to find the combination of improvements with the lowest total of crash and delay costs within an acceptable range of implementation costs. The optimization model is presented in conjunction with crash prediction models borrowed from the Interactive Highway Safety Design Model software. The optimization model could be modified to work with other types of crash prediction models. A test case study containing a network of three highways and 13 intersections is presented. The corresponding optimization problem is solved with the CPLEX software. The variations of crash and delay cost savings versus improvement costs are studied, and the results are discussed.