High-Resolution Traffic Flow Prediction Model Based on Deep Learning

Authors

  • 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

DOI:

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

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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How to Cite

Yao, Z., & Wang, Y. (2019). High-Resolution Traffic Flow Prediction Model Based on Deep Learning. Journal of Computer Science Research, 1(1), 1–9. https://doi.org/10.30564/jcsr.v1i1.381

Issue

Vol. 1 , Iss. 1 (April 2019)

Article Type

Review

License

Copyright © 2019 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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