Using the CVP Traffic Detection Model at Road-Section Applies to Traffic Information Collection and Monitor - the Case Study

Authors

  • Shing Tenqchen ChungHwa Telecom Telecommunication, Laboratories (CHTTL), Taiwan
  • Yen-Jung Su Graduate student of Dept. of Electronic & Computer Engineering, NTUST
  • Keng-Pin Chen ChungHwa Telecom Telecommunication, Laboratories (CHTTL), Taiwan, China

DOI:

https://doi.org/10.30564/aia.v1i2.1211

Abstract

This paper proposes a using Cellular-Based Vehicle Probe (CVP) at road-section (RS) method to detect and setup a model for traffic flow information (info) collection and monitor. There are multiple traffic collection devices including CVP, ETC-Based Vehicle Probe (EVP), Vehicle Detector (VD), and CCTV as traffic resources to serve as road condition info for predicting the traffic jam problem, monitor and control. The main project has been applied at Tai # 2 Ghee-Jing roadway connects to Wan-Li section as a trial field on fiscal year of 2017-2018. This paper proposes a man-flow turning into traffic-flow with Long-Short Time Memory (LTSM) from recurrent neural network (RNN) model. We also provide a model verification and validation methodology with RNN for cross verification of system performance.

Keywords:

Intelligent Transport Systems (ITS), ETC-Based Vehicle Probe (EVP), Vehicle Detector (VD), Long-Short Time Memory (LTSM), Recurrent Neural Network (RNN)

References

[1] Lo, Bin-Rong , Jiang, Jhe-Ye, Tung, Shen-Lung. Northern Bin-Hai Railroad at Gee-Gin Road Section with Crowded Stable Corridor with from ITS System Constructed Plan Technical Service Report. Gee-Lung City Hall Transportation and Tourism Department, 2018: 27-63.

[2] Lo, Bin-Rong , Jiang, Jhe-Ye, Tung, Shen-Lung. Northern Bin-Hai Railroad at Gee-Gin Road Section with Crowded Stable Corridor with from ITS System Constructed Plan Technical Service Report. Gee-Lung City Hall Transportation and Tourism Department, 2019, 03: 1-30.

[3] Tenqchen, S., Chen, Keng-Pin, Tung, Shen-Lung, Jiang, Jhe-Yi. Using Neural Network Technology to Develop CVP Traffic Flow Information - the Case Study,TL Technical Journal, 2018, 48(4): 16-25.ISSN-1015-0730

[4] Adolf D. May. Fundamentals of Traffic Flow. Prentice - Hall, Inc. Englewood Cliff New Jersey 07632, second edition, 1990.

[5] Green Shields Model.http://www.webpages.uidaho.edu/niatt_labmanual/chapters/trafficflowtheory/theoryandconcepts/GreenshieldsModel.htm

[6] Kingma, Diederik P., Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.

[7] Lewis, C. D. Industrial and business forecasting methods: A radical guide to exponential smoothing and curve fitting. London, Boston: Butterworth Scientific, 1982: 305.

[8] Sak, Haşim, Andrew Senior, and Françoise Beaufays. Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Fifteenth annual conference of the international speech communication association, 2014.

[9] Yann LeCun, Leon Bottou, Yoshua Bengio, Patrick Haffner. Gradient-Based Learning Applied to Document Recognition, 1998.

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How to Cite

Tenqchen, S., Su, Y.-J., & Chen, K.-P. (2019). Using the CVP Traffic Detection Model at Road-Section Applies to Traffic Information Collection and Monitor - the Case Study. Artificial Intelligence Advances, 1(2), 38–43. https://doi.org/10.30564/aia.v1i2.1211

Issue

Vol. 1 , Iss. 2 (October 2019)

Article Type

Articles

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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