Identification of High Crash Road Segment using Genetic Algorithm and Dynamic Segmentation

Abstract

<span>This paper presents an evolutionary algorithm for recognizing high and low crash road segments using <span>Genetic Algorithm as a dynamic segmentation method. Social and economic costs as well as physical and <span>mental injuries make the governments perceiving to road safety indexes in order to diminish the <span>consequences of road accidents. Due to the limitation of budget for safety improvement of all parts of the <span>road, the road segments with more accidents should be recognized for safety budget assignment. So, <span>considering this fact it's important to identify the segments with high and low number of accidents to <span>optimize the road safety program. In this study, a novel chromosome coding method and a fitness function <span>which are consistent with Genetic Algorithm are proposed. The proposed methodology is also validated by <span>using two mathematical parameters so that the results confirm that the proposed modeling works properly. <span>Afterward, the proposed dynamic segmentation method is compared with the other static segmentation <span>methods along 51 km of Shahrood–Sabzevar highway. The proposed method may have more advantages <span>comparing to static segmentation methods for all of the performance indexes which were considered in this <span>study. The proposed method has a variance about two times higher than the one for accident density in <span>comparison with the other static segmentation methods. About 62% and 34% improvement is achieved in <span>average of segments accident density and total segments density respectively in comparison with the other <span>fixed methods.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span> </span>