A car-following model considering asymmetric driving behavior based on long short-term memory neural networks |
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| Institution: | 1. College of Physics and Electronics, Hunan University of Arts and Science, Changde 415000, China;2. Logistics Research Center, Shanghai Maritime University, Shanghai 200135, China;3. Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region;1. Department of Automation, Tsinghua University, Beijing 100084, China;2. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China;3. Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, United States;1. Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China;2. College of Automation, Chongqing University, Chongqing 400030, China |
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| Abstract: | Asymmetric driving behavior is a critical characteristic of human driving behaviors and has a significant impact on traffic flow. In consideration of the asymmetric driving behavior, this paper proposes a long short-term memory (LSTM) neural networks (NN) based car-following (CF) model to capture realistic traffic flow characteristics by incorporating the driving memory. The NGSIM data are used to calibrate and validate the proposed CF model. Meanwhile, three characteristics closely related to the asymmetric driving behavior are investigated: hysteresis, discrete driving, and intensity difference. The simulation results show the good performance of the proposed CF model on reproducing realistic traffic flow features. Moreover, to further demonstrate the superiority of the proposed CF model, two other CF models including recurrent neural network based CF model and asymmetric full velocity difference model, are compared with LSTM-NN model. The results reveal that LSTM-NN model can capture the asymmetric driving behavior well and outperforms other models. |
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| Keywords: | Traffic flow Car-following Asymmetric driving behavior Deep learning Long short-term memory |
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