Car-following model with multiple predicting and controlling modules based on assumptions of anticipation behavior

Anticipation of driver actions may be a key factor in car-following behavior. Although many car-following models have been proposed, most of them are regarded as instantaneous feedback controllers. This paper considers taking anticipatory actions into account, as in feed-forward controllers. This paper retrofits this onto a previously proposed model assuming that drivers adjust acceleration to maximize instantaneous utility, adding predicting and controlling modules associated with different target times. The weight of each predicting module is updated depending on the circumstances of the driver over the last few seconds. This paper verifies that the proposed model improves estimation accuracy in terms of the root mean squared error of headway distance by calibration with observed car-following data from probe cars and video images. The goal of this model is future application in exploration of bottleneck phenomena.