Developing Car-Following Models for Winter Maintenance Operations Incorporating Machine Learning Methods

Abstract

Car-following models have been explored thoroughly for different vehicle types, such as cars and trucks. Although snowplows can be classified as trucks, their unique physical and operational characteristics impose a distinct following behavior. Michigan Department of Transportation has recently tested a collision avoidance system to reduce rear-end crashes. The incorporated radar in this system provides valuable information, defining two objectives for this study: (1) investigating the impacts of snowplows on car-following behavior considering car–car and car–snowplow vehicle-type combinations; (2) exploring the effects of the proposed collision avoidance system on car-following behavior by comparing car-following models for collected data with and without such a system. Firstly, space and time headway analyses are performed to compare different vehicle-type combinations. Then, the Gipps’ model is calibrated, and two data-driven car-following models are trained incorporating support vector regression and a long short-term memory network. These models are calibrated/trained to evaluate the performance of models with and without considering the heterogeneity of driving behavior among road users. The results indicate that the presence of snowplows leads to statistically significant different car-following models. Besides, it is shown that the collision avoidance system slightly improves the behavior of the following vehicles, which is not statistically significant. Also, it is concluded that considering driving behavior heterogeneity leads to more realistic prediction of the following behavior, compared to assuming homogeneous driving styles in traffic. Finally, the performances of the three developed car-following models are compared. Developing specific models for winter maintenance operations is an early step toward developing microsimulation models for adverse weather conditions.