Car-Following Trajectory Modeling with Machine Learning—A showcase
نویسنده
چکیده
30 This paper attempts to showcase the benefits and merits of using artificial intelligence techniques 31 in transportation applications. The example we use in this paper is modeling of a car-following 32 trajectory data and comparing the machine learning approach to regression analysis. For the 33 machine learning approach, we use Neuro-Fuzzy Actor-Critic Reinforcement Learning 34 (NFACRL). We train the NFACRL network using vehicle trajectory data extracted from the 35 Naturalistic Car Driving Study (NCDS) databases provided by the Virginia Tech Transportation 36 Institute (VTTI). Our results show that both the machine learning and regression analysis could 37 predict the upcoming acceleration value with a very high R 2 value (more than 0.98). However, 38 only the machine learning approach could reproduce the vehicle trajectory, while the regression 39 analysis would ultimately lead to an erroneous model. 40
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