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

DOI PDF 参考文献30件

書誌事項

公開日
2020-10-11
資源種別
journal article
権利情報
  • https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
  • https://doi.org/10.15223/policy-029
  • https://doi.org/10.15223/policy-037
DOI
  • 10.1109/smc42975.2020.9282910
公開者
IEEE

説明

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.

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