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Using the CVP Traffic Detection Model at Road-Section Applies to Traffic Information Collection and Monitor - the Case Study
DOI:
https://doi.org/10.30564/aia.v1i2.1211Abstract
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
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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.