A Decision Support Model for Transportation Companies to Examine Driver Behavior

The entire society, and particularly the transportation companies, has an interest to improve traffic safety. Besides more than 3000 lost lives on roads every day, there are significant financial consequences of road accidents (RTAs). purpose this article is design efficient model for providing information about driver propensity RTAs based assessing their personality traits. This achieved by creating a fuzzy inference system (FIS) where inputs scores from implemented psychological instruments output number experienced driver. To adjust functioning FIS empirical data, bee colony optimization (BCO) metaheuristic applied. In procedure, we test three approaches defining variables initial compare performance. Simulation results show differences between considered and, generally, very promising achievements proposed algorithm. best-found reached 36% improvement objective function compared starting FIS. can be used, inter alia, as decision-making tool in recruitment procedures professional drivers assess RTAs, that saving people reducing costs companies.