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原文传递 An Integrated Framework for Real-Time Intelligent Traffic Management of Smart Highways

题名：	An Integrated Framework for Real-Time Intelligent Traffic Management of Smart Highways
正文语种：	eng
作者：	Qi Zhang;Yunyang Shi;Ruyang Yin;Hong Tao;Zhihong Xu;Zihan Wang;Siyuan Chen;Jiping Xing
作者单位：	Jiangsu Key Laboratory of Urban Institute for Transport Studies Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies School of Transportation Southeast Univ. Nanjing Jiangsu 211189 China;Jiangsu Key Laboratory of Urban Institute for Transport Studies Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies School of Transportation Southeast Univ. Nanjing Jiangsu 211189 China;Dept. of Logistics and Maritime Studies Hong Kong Polytechnic Univ. Hung Horn Hong Kong China;Jiangsu Key Laboratory of Urban Institute for Transport Studies Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies School of Transportation Southeast Univ. Nanjing Jiangsu 211189 China;China Construction & Design International (CCDI) (Suzhou) Exploration and Design Consultant Co. Ltd. Suzhou China;Jiangsu Key Laboratory of Urban Institute for Transport Studies Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies School of Transportation Southeast Univ. Nanjing Jiangsu 211189 China;Jiangsu Key Laboratory of Urban Institute for Transport Studies Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies School of Transportation Southeast Univ. Nanjing Jiangsu 211189 China;Jiangsu Key Laboratory of Urban ITS Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies School of Transportation Southeast Univ. Nanjing Jiangsu 211189 China
关键词：	Intelligent transportation; Real-time simulation; Deep learning; Smart highway; Traffic management and control
摘要：	The new generation smart highways (NGSH) have emerged as irresistible trends to enhance the efficiency and safety of transportation systems. An integral component of the NGSH is the automation of the intelligent traffic management system (ITMS). This study investigates an integrated framework for the ITMS that incorporates the fine-grained microscopic simulation and deep learning technologies based on real-time traffic data. The framework commences by performing dynamic corrections based on the license plate, vehicle speed, location, and other information provided by the real-time bayonet data in order to simulate the realistic traffic flow along the highway. A deep learning model based on long short-term memory (LSTM) is then applied to predict the short-term traffic volume on major highway segments. Based on prediction results, a collaborative management method is constructed that combines variable speed limits and ramp metering. The case study on the Shanghai-Hangzhou-Ningbo Highway in China suggests the real-time simulation model can control the average error of the traffic volume on the main segments by 4.58%. The LSTM-based model can accurately predict the short-term traffic volume with a relative error of 85% below 15% in both offline and online modes. Consequently, the proposed collaborative framework improves the average speed and traffic volume of controlled sections by 3.62% and 4.35%, respectively, demonstrating its effectiveness in improving the operation and management of the smart highways.
出版年：	2023
期刊名称：	Journal of Transportation Engineering
卷：	149
期：	7
页码：	04023055.1-04023055.10