Modeling Car-Following Behavior in Downtown Area based on Unsupervised Clustering and Variable Selection Method*
Modeling Car-Following Behavior in Downtown Area based on Unsupervised Clustering and Variable Selection Method*
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DOI：
10.1109/smc42975.2020.9282910
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Duc-An Nguyen;Jude Nwadiuto;H. Okuda;Tatsuya Suzuki
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文献类型：
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作者：
Duc-An Nguyen;Jude Nwadiuto;H. Okuda;Tatsuya Suzuki
本研究创新地提出了一种基于无监督聚类和变量选择方法的跟车行为建模框架，适用于将可解释的微观交通模型融入到驾驶员行为理解中。该框架保留了传统方法和数据驱动方法的优点。实验结果表明，无监督聚类方法能够以一种易懂的方式自然地识别驾驶员的行为，而变量选择在有效降低模型复杂度的同时，表现出了很好的识别驾驶任务真实模型的特性。特别是，使用从日本名古屋市闹市区Sakae的驾驶车辆上安装仪表化的序列收集的真实世界数据来演示所提出的框架。Gazis-Herman-Rothery(GHR)模型是应用最广泛的非线性跟车模型之一，根据相同的数据进行了校准，并用作参考基准。
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.