An Extended Car-Following Model Considering Backward-Looking Effect: A Machine Learning Approach

Abstract

Most car-following models have mainly focused on the effects of the lead vehicle on the target vehicle's behaviour or the driver's forward-looking effects, but not the effects of the vehicle behind the target vehicle (the following vehicle) or the driver's backward-looking effects. Therefore, this study proposes a data-driven car-following model that incorporates both backward- and forward-looking effects using a deep neural network (DNN). This model is called the “DNN with backward-looking effect (DNN-BE) model”. The DNN-BE model produced higher prediction accuracy than the DNN model with forward-looking effects only and a conventional mathematical car-following model that considers both forward- and backward-looking effects. It was found that the target vehicle is more likely to accelerate when the spacing with the following vehicle is shorter and the spacing with the lead vehicle is longer. The result of permutation importance also shows that variables related to the following vehicle are more important when the spacing with the following vehicles is shorter.