Modeling Car-Following Behavior in Downtown Area based on Unsupervised Clustering and Variable Selection Method*

摘要

In this research, an innovative framework that taking advantage of unsupervised clustering and variable selection method is proposed for the modeling of car-following behavior, suitable for incorporating explainable microscopic traffic models into understanding driver behavior. The proposed framework retains the advantages of both conventional and data-driven method. The experimental result presented in this paper shows that the unsupervised clustering method helps identify driver behaviors naturally in an intelligible way, while variable selection has shown a good property of identifying the true model of driving task while efficiently reducing model complexity. Especially, the proposed framework is demonstrated using real-world data collected from a sequence of instrumented install on a driving vehicle in Sakae, downtown area of Nagoya city, Japan. Gazis-Herman-Rothery (GHR) models, one of the most extensively used non-linear car-following models is calibrated against the same data and used as a reference benchmark.