Dynamic traffic demand uncertainty prediction using radio-frequency identification data and link volume data

Dynamic traffic demand is crucial for developing effective strategies and algorithms for real-time traffic management and control. The uncertainty of traffic demand provides additional information while its prediction is very complicated and is inadequately investigated in the existing literature. Recently, radio-frequency identification (RFID) technology has been deployed to monitor the condition of traffic. In this study, the authors propose a modelling system to predict dynamic traffic demand uncertainty using the RFID data and the link traffic volume. The modelling system includes an optimisation model using the RFID data to calculate the historical traffic demand in terms of origin–destination matrix, a state-space model solved by Kalman filter to predict the traffic demand, and a GARCH model to capture the conditional variance of the predicted traffic demand. Performance measures are applied to evaluate prediction accuracy. The case study in Nanjing, China shows that the modelling system has the desirable performance to successfully predict uncertainties of dynamic traffic demand.

• Record URL:
  • https://doi.org/10.1049/iet-its.2018.5317
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Supplemental Notes:
  • Abstract reprinted with permission of the Institution of Engineering and Technology.
• Authors:
  • Liu, Yu
  • Liu, Zhao
  • Li, Xiugang
  • 0000-0002-5966-8158
  • Huang, Wei
  • Wei, Yun
  • Cao, Jinde
  • Guo, Jianhua
• Publication Date: 2019-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1309-1317
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 13
  • Issue Number: 8
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Data analysis; Highway traffic; Kalman filtering; Link volume; Mathematical prediction; Matrices (Mathematics); Optimization; Radio frequency identification; Telecommunications; Uncertainty
• Non-Preferred Terms: traffic demand
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01715359
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 30 2019 1:01PM