Short-Term Traffic Speed Forecasting Based on Data Recorded at Irregular Intervals

Recent growth in demand for proactive real-time transportation management systems has led to major advances in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, and genetic algorithms to short-term traffic forecasting to make forecasts more reliable, efficient, and accurate. However, most of these methods can only deal with data recorded at regular time intervals, which restricts the range of data collection tools to presence-type detectors or other equipment that generates regular data. The study reported here is an attempt to extend several existing time series forecasting methods to accommodate data recorded at irregular time intervals, which would allow transportation management systems to obtain predicted traffic speeds from intermittent data sources such as Global Positioning System (GPS). To improve forecasting performance, acceleration information was introduced, and information from segments adjacent to the current forecasting segment was adopted. The study tested several methods using GPS data from 480 Hong Kong taxis. The results show that the best performance in terms of mean absolute relative error is obtained by using a neural network model that aggregates speed information and acceleration information from the current forecasting segment and adjacent segments.

• Record URL:
  • https://doi.org/10.1109/TITS.2012.2203122
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • Qing Ye
  • Szeto, W Y
  • Wong, S C
• Publication Date: 2012

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1727-1737
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 13
  • Issue Number: 4
  • 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: Acceleration (Mechanics); Forecasting; Global Positioning System; Neural networks; Time intervals; Traffic speed
• Subject Areas: Data and Information Technology; Highways; I70: Traffic and Transport;

Filing Info

• Accession Number: 01501382
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Dec 17 2013 9:31AM