An intuitive methodology of developing injury, property-damage only (PDO) and fatal accident models for unsignalized intersections based on the Traffic Accident Surveillance and Analysis System (TASAS) in California is illustrated. A fairly new grouping and classifying technique called Classification and Regression Trees (CART) was used as a building block for developing prediction models. The proposed methodology includes a 3-level prediction procedure with a "tree" structure for easy interpretation and applications. It includes an adjustment procedure for different reporting levels of PDO accidents in different police jurisdictions. It also describes some applications of accident prediction models and some areas where future automation of the TASAS system could be made. An integrated traffic accident system for prediction, surveillance, improvement, evaluation, and monitoring studies is discussed. Techniques discussed for such a system include computer intensive tree building methods, knowledge-based expert systems, randomisation experiments. Macroscopic-type models for injury, and PDO accidents per year were derived, and the following factors were found to be significant: traffic intensity, proportion of cross street traffic, intersection type, control type, number of lanes, and left-turn arrangements, traffic flow arrangements, and environmental locations. Relevant factors for fatal accidents are traffic intensity, percentage of cross street traffic, and design speed. Based on the results, it is also apparent that the models derived from the proposed methodology and TASAS provide more intuition and flexibility than the existing models used in California.
Samenvatting