using the reaction delay as the driver effects in the development of car-following models

car-following models, as the most popular microscopic traffic flow modeling, is increasingly being used by transportation experts to evaluate new intelligent transportation system (its) applications. a number of factors including individual differences of age, gender, and risk-taking behavior, have been found to influence car-following behavior. this paper presents a novel idea to calculate the driver-vehicle unit (dvu) instantaneous reaction delay of dvu as the human effects. unlike previous works, where the reaction delay is considered to be fixed, considering the proposed idea, three input-output models are developed to estimate fv acceleration based on soft computing approaches. the models are developed based on the reaction delay as an input. in these modeling, the inputs and outputs are chosen with respect to this feature to design the soft computing models. the performance of models is evaluated based on field data and compared to a number of existing car-following models. the results show that new soft computing models based on instantaneous reaction delay outperformed the other car-following models. the proposed models can be recruited in driver assistant devices, safe distance keeping observers, collision prevention systems and other its applications.