Road accident prediction modeling and diagnostics of accident causality: A comprehensive methodology

This dissertation formulated a comprehensive methodology for road accident prediction and diagnostics of accident causality. It provides conceptual blueprint and the analytical framework for the development of the Highway Safety Manual currently contemplated by the Transportation Research Board. Accident prediction models known as Safety Performance Functions (SPF) were developed for the following facilities: rural 4-lane interstate freeways in the mountainous terrain, rural 2-lane, arterial highways in the mountainous terrain, rural 4-lane interstate freeways in rolling and flat terrain. Accident models were developed using Poisson distributional assumptions. Additionally conceptual formulation of the level of service concept applicable to highway safety was developed.; Diagnostics of accident causality is performed by using pattern recognition algorithm and direct diagnostic methods introduced in this dissertation. This methodology is based on the idea that traffic accidents can be viewed as independent Bernoulli trials and that it is possible to detect deviation from the random statistical process by computing cumulative probability for each of the accident characteristics. In the course of this study a framework of normative parameters to provide a knowledge base for the diagnostics of safety problems was developed. Development of the diagnostic knowledge base and of pattern recognition algorithm led to the following finding: Existence of accident patterns susceptible to correction may or may not be a accompanied by the over-representation in accident frequency detected by the safety performance functions or high accident rates. The implication of this finding on the road safety policy is as follows: cost-effective safety improvement counter-measures may be constructed at locations which exhibit overall accident frequency well within expected range. This point is generally overlooked by the public agencies funding road safety improvement projects.