AI Empowered Communication Systems for Intelligent Transportation Systems

Intelligent control of traffic has significant influence on the scheduling efficiency of urban traffic flow. Therefore, in order to improve the efficiency of vehicles at intersections, first, the Back Propagation (BP) neural network is used to propose a vehicle passing model at the intersection, and based on the intelligent traffic control system model, the Earliest Deadline First (EDF) dynamic scheduling algorithm is used to improve the Controller Area Network (CAN) communication network. Finally, the simulation test is used to evaluate the effectiveness of the proposed model and the improved CAN bus communication network. The results show that the neural network model can be used to predict the passage time of vehicles queuing at intersections with an error of less than 10%. The improved CAN bus communication can improve the data transmission rate, and the success rate of data transmission under different load rates is above 95%. In conclusion, the application of artificial intelligence technology in intelligent traffic system can improve the efficiency of vehicle scheduling and the efficiency of communication system. This research is of great significance to improve the communication performance of the transportation system and scheduling efficiency.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.3017183
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Lv, Zhihan
  • 0000-0001-8164-1405
  • Lou, Ranran
  • Singh, Amit Kumar
  • 0000-0001-7359-2068
• Publication Date: 2021-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 4579-4587
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 7
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Computer network protocols; Data communications; Intersections; Neural networks; Predictive models; Traffic flow
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01787758
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 11 2021 3:21PM