Open AccessUsing the CVP Traffic Detection Model at Road-Section Applies to Traffic Information Collection and Monitor - the Case Study
Author(s) -
Shing Tenqchen,
Yongbo Su,
Keng-Pin Chen
Publication year - 2019
Publication title -
artificial intelligence advances
Language(s) - English
Resource type - Journals
ISSN - 2661-3220
DOI - 10.30564/aia.v1i2.1211
Subject(s) - traffic flow (computer networking) , computer science , section (typography) , real time computing , detector , data collection , floating car data , artificial neural network , traffic congestion reconstruction with kerner's three phase theory , transport engineering , recurrent neural network , simulation , computer network , engineering , artificial intelligence , traffic congestion , telecommunications , statistics , mathematics , operating system
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.