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

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)

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)

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References


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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

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

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