Modify car following model by human effects based on Locally Linear Neuro Fuzzy

Abstract

Nowadays, simulation has become a cost-effective option for the evaluation of infrastructure improvements, on-road traffic management systems, and in vehicle driver support systems due to the fast evolution of computational modeling techniques. This paper presents a Locally Linear Neuro-Fuzzy (LLNF) model to simulate and predict the future behavior of a Driver-Vehicle-Unit (DVU). Local Linear Model Tree (LOLIMOT) learning algorithm is applied to train the model using real traffic data. This model was developed based on a new idea for estimating the instantaneous reaction of DVU, as an input of LLNF model. The model?s performance was evaluated based on real observed traffic data and also through comparisons with the results of LLNF models based on constant reaction delay. The results showed that LLNF model based on instantaneous reaction delay input outperformed the other car following models.