High-Resolution Traffic Flow Prediction Model Based on Deep Learning

Zhihong Yao (School of Transportation and Logistics, Southwest Jiaotong University;National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China;TOPS Laboratory, Department of Civil and Environmental Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, WI 53706, USA)
Yibing Wang (Department of Architecture Engineering, Yantai Vocational College, Yantai, Shandong, 264670, China)

Abstract


The traditional platoon dispersion model is based on the hypothesis of probability distribution, and the time resolution of the existing traffic flow prediction model is too big to be applied to the adaptive signal timing optimization. Based on the view of the platoon dispersion model, the relationship between vehicle arrival at downstream intersection and vehicle departure from the upstream intersection was analyzed. Then, the high-resolution traffic flow prediction model based on deep learning was proposed. The departure flow rate at the upstream was taking as the input and the arrival flow rate at downstream intersection was taking as the output in this model. Finally, the parameters of the proposed model were trained by the field survey data, and this model was implemented to predict the arrival flow rate of the downstream intersection. The result shows that the proposed model can better reflect the fluctuant characteristics of traffic flow and reduced the sum of the squared errors (SSE), MSE, and MAE by 13.17%, 13.21%, and 14.24%, compared with Robertson’s model. Thus, the proposed model can be applied for real-time adaptive signal timing optimization.


Keywords


Traffic flow prediction;Deep learning;Time resolution;Platoon dispersion;Signal timing optimization;Real time

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References


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DOI: https://doi.org/10.30564/jcsr.v1i1.381

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