Freeway Car-following Model and Simulation Based on Adaptive Neuro-fuzzy Inference System

Abstract:

In order to better describe the fuzzy and uncertain characteristics of drivers when following a leading car on the freeway,the adaptive neuro-fuzzy inference system(ANFIS) was applied to the car-following model.Firstly,the real-time car-following data collected by the five-wheel system were denoised by the wavelet tool in order to eliminate the disturbance from the surrounding and recover the initial information of data.Meanwhile,the driver’s reaction time was caculated by the correlation function in term of signal processing method.Then,the car-following model based on ANFIS was developed with the relative speed,distance headway between leading vehicle and following vehicle and speed of following vehicle as inputs and car-following acceleration as the output.Finally,the model that generated the adaptive rule of drivers’ car-following behavior was simulated and trained to compare with General Motors based car-following models.The car-following model based on ANFIS proves to be able to reflect the driving behavior of real freeway situaton.