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

AbstractThe 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.

• Record URL:
  https://doi.org/10.1061/JTEPBS.TEENG-7729
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/24732907
• Supplemental Notes:
  • © 2023 American Society of Civil Engineers 2023
• Authors:
  • Zhang, Qi
  • Shi, Yunyang
  • Yin, Ruyang
  • Tao, Hong
  • Xu, Zhihong
  • Wang, Zihan
  • Chen, Siyuan
  • Xing, Jiping
• Publication Date: 2023-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 04023055
• Serial:
  • Journal of Transportation Engineering, Part A: Systems
  • Volume: 149
  • Issue Number: 7
  • Publisher: American Society of Civil Engineers
  • ISSN: 2473-2907
  • EISSN: 2473-2893
  • Serial URL: http://ascelibrary.org/journal/jtepbs

Subject/Index Terms

• TRT Terms: Automated highways; Highway traffic control; Intelligent transportation systems; Machine learning; Microscopic traffic flow; Real time control
• Geographic Terms: Hangzhou (China); Ningbo (China); Shanghai (China)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01885600
• Record Type: Publication
• Files: TRIS, ASCE
• Created Date: Jun 22 2023 9:49AM