Segmentation of Risk Factors for Fatal Crashes at Urban Signalized Intersections: A Multi-Perspective Model Approach

Abstract

Signalized intersections are frequently installed in developing countries to facilitate efficient traffic flow and seldom to increase traffic safety. As a result, fatal collisions still occur at intersections with signals. The purpose of this study is to gain a better understanding of signalized intersection safety by identifying and segmenting traffic and geometric risk factors associated with fatal crashes. For this purpose, a thorough road inventory survey—primary crash data—was used to analyze crashes at 67 signalized intersections in Hyderabad, an Indian metropolitan city. This paper proposes a multi-perspective model application and segmentation strategy that classifies a group of important crash factors determining crash fatality at urban signalized intersections by combining machine learning, data mining, and statistical modeling results. The proposed segmentation divided the crash parameters into three distinct categories: very high, high, and moderate risk factors. The key findings show that major road width, lack of right-turn protection, and absence of all-red time are the most influential factors contributing to fatal crashes at signalized intersections. Based on the findings, several policy recommendations were proposed. The segmentation of signalized intersection features would provide useful insights into the level of their influence and the impact of signalized intersection design on safety in developing countries. The study’s findings and proposed policy insights may assist transportation officials in developing, prioritizing, and implementing specialized safety countermeasures for signalized intersections.