title: A red-light running prevention system based on artificial neural network and vehicle trajectory data.
abstract: The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.
authors.affiliation: Transportation School, Southeast University, 2 Sipailou, Nanjing 210096, China ; School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA.
authors.forename: Pengfei
authors.initials: P
authors.lastname: Li
authors.affiliation: Highway School, Chang'an University, The Middle Section of Southern Second Ring Road, Xi'an 710064, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
authors.affiliation: Transportation School, Southeast University, 2 Sipailou, Nanjing 210096, China.
authors.forename: Xiucheng
authors.initials: X
authors.lastname: Guo
mesh_terms: D000056:Accident Prevention; D000465:Algorithms; D001334:Automobile Driving / Q000706:statistics & numerical data; D003657:Decision Making; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D015203:Reproducibility of Results