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Human Car-Following Behavior: Parametric, Machine-Learning, and Deep-Learning Perspectives

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1212)

Abstract

Emerging automated vehicles and mixed traffic flow have been substantially increased demand for modeling human driving behavior in both academia and industry. As a result, many car-following (CF) models have been proposed using parametric and data-driven approaches. Considering the large number of CF models, the critical question is which CF model or category of models (e.g. machine-learning) could accurately regenerate human CF behavior. This study conducts a cross-category comparison between one parametric model (intelligent driver model (IDM)), two new machine-learning CF models based on feedforward neural network (FNN) and recurrent neural network (RNN), and one novel deep-learning CF model (Deep-RNN) with long short-term memory (LSTM). The models are developed in TensorFlow and compared at local and global levels. At the local level, Deep-RNN significantly outperformed the others, followed by RNN and FNN. At the global level, IDM demonstrated the best performance, followed closely by Deep-RNN. The result illustrates there is no one-size fit model and the model should be selected given projects’ characteristics. The result suggests a hybrid approach, which integrates parametric and deep-learning models, could precisely regenerate human car-following behavior.

Keywords

Human driving behavior Car-Following Deep-learning Artificial neural network Machine learning 

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Systems and EnterprisesStevens Institute of TechnologyHobokenUSA
  2. 2.Department of Civil and Environmental EngineeringRutgers UniversityPiscatawayUSA

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