A Genetic Programming Approach To Explore The Crash Severity On Multi-Lane Roads

The study aims at understanding the relationship of geometric and environmental factors with injury related crashes as well as with severe crashes through the development of classification models. The Linear Genetic Programming (LGP) method is used to achieve these objectives. LGP is based on the traditional genetic algorithm, except that it evolves computer programs. The methodology is different from traditional non-parametric methods like classification and regression trees which develop only one model, with fixed criteria, for any given dataset. The LGP on the other hand not only evolves numerous models through the concept of biological evolution, and using the evolutionary operators of crossover and mutation, but also allows the investigator to choose the best models, developed over various runs, based on classification rates. Discipulus™ software was used to evolve the models. The results included vision obstruction which was found to be a leading factor for severe crashes. Percentage of trucks, even if small, is more likely to make the crashes injury prone. The \u27lawn and curb\u27 median are found to be safe for angle/turning movement crashes. Dry surface conditions as well as good pavement conditions decrease the severity of crashes and so also wider shoulder and sidewalk widths. Interaction terms among variables like on-street parking with higher posted speed limit have been found to make injuries more probable. © 2009 Elsevier Ltd. All rights reserved