An Integrated Framework for Real-Time Intelligent Traffic Management of Smart Highways
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 7
Abstract
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.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study is supported by the National Natural Science Foundation of China (No. 42261144745) and China Postdoctoral International Exchange Program (No. 1121002320).
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 7 • July 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 24, 2022
Accepted: Feb 8, 2023
Published online: May 9, 2023
Published in print: Jul 1, 2023
Discussion open until: Oct 9, 2023
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