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

• 作者: Keng-Pin Chen Shing Tenqchen Yen-Jung Su
• 来源期刊: 人工智能进展(英文)
• 学科: 工学
• 关键词: Intelligent Transport Systems(ITS) ETC-Based VEHICLE Probe(EVP) VEHICLE Detector(VD) Long-Short Time Memory(LTSM) RECURRENT Neural Network(RNN)
• 年，卷（期）: 2019,（2）
• 页码范围: 38-43
• 页数: 6页
• 分类号: TN9

摘要

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.