A naive Bayesian classifier-based algorithm for freeway traffic incident detection

This paper presents a naive Bayesian classifier-based algorithm for freeway non-recurrent traffic incident detection to enhance the accuracy and learning ability of intelligent traffic incident detection algorithm. The traffic wave theory is employed to establish a conceptual characteristic model of traffic incident, continuous characteristic variables are transferred into discrete characteristic variables via sub-discretization, and the naive bayesian-based traffic incident classifier is designed by regarding traffic incident detection as 0-1 classification problems. An experiment is carried on a section of a typical freeway, and the performance of the presented model and algorithm is validated via VISSIM simulation. Extensive simulation results show that the algorithm in freeway traffic incident detection system is of high accuracy and strong robustness even if the traffic volumes increase.