NEURAL-NETWORK FOR GAP ACCEPTANCE AT STOP-CONTROLLED INTERSECTIONS

The behavior of gap acceptance by vehicles at intersections with stop signs involves the complex interaction of numerous geometric, traffic, and environmental factors. Several methods, including empirical analy sis, and theoretical, logit, and probit models have been used to estim ate gap acceptance at stop-controlled intersections. In the past, neur al networks have been used to examine problems involving complex inter relationship among many variables and found to perform better than con ventional methods. This paper describes the development of a neural ne twork and a binary-logit model for predicting accepted or rejected gap s at rural, low-volume two-way stop-controlled intersections. The type of control, the turning movements in both the major and minor directi ons, size of gap, service time, stop type, vehicular speed, queue in t he minor direction, and existence of vehicle in the opposite approach were found to influence the driver's decision to accept or reject a ga p. The results of the neural network and the binary-logit model were c ompared with the observations recorded in the field, The results revea led that the neural network correctly predicted a higher percentage of accepted or rejected gaps than the binary-logit model.