Cellular Automata for Traffic Accident Analysis: A Systematic Review

Vehicular accidents are among the leading causes of fatalities, economic losses, and traffic disturbances worldwide. Understanding the intricacies of accident emergence and propagation is crucial for devising mitigation strategies. Among the different analysis approaches, cellular automata modeling stands out as a powerful tool since it enables the reproduction of complex collective dynamics through simple local interactions. Although numerous CA-based studies have addressed traffic accidents under different conditions, the literature still lacks a comprehensive synthesis dedicated to cellular automata-based vehicle-to-vehicle accident modeling. This systematic review classifies and comparatively analyzes how cellular automata models represent vehicle-to-vehicle collision mechanisms, dangerous traffic states, accident occurrence, and accident-related traffic effects. Through searches in Scopus, Web of Science, TRID, and IEEE Xplore, 740 records were identified and subsequently filtered to 90 papers according to the PRISMA methodology. The selected studies were classified according to a common taxonomy that includes rear-end, head-on, and side-impact collisions, as well as lateral conflicts. The contribution of the proposed taxonomy is threefold: to provide a deeper understanding of the key modeling principles, to review the types of accidents most frequently analyzed, and to identify current research gaps. Overall, this paper serves as a reference point for developing, comparing, and extending cellular automata models for traffic safety analysis.