Predicting the future state of a vehicle in a stop&go behavior based on ANFIS models design

Stop&go cruise system is an extension to ACC which is able to automatically accelerate and decelerate the vehicle in city traffic. There have been attempts to model stop&go waves via microscopic and macroscopic traffic models. But predicting the future state of the maneuver has not attracted much attention. The purpose of this study is to design adaptive neuro-fuzzy inference system (ANFIS) models to simulate and predict the future state of the stop&go maneuver in real traffic flow for different steps ahead. These models are designed based on the real traffic data and model the acceleration of the vehicle which performs a stop&go maneuver. Using the field data, the performance of the presented models is validated and compared with the real traffic datasets. The results show very close compatibility between the model outputs and maneuvers in real traffic flow.