SHORT-TERM TRAFFIC FLOW PREDICTION: NEURAL NETWORK APPROACH

Much of the current activity in the area of intelligent vehicle-highway systems (IVHSs) focuses on one simple objective: to collect more data. Clearly, improvements in sensor technology and communication systems will allow transportation agencies to more closely monitor the condition of the surface transportation system. However, monitoring alone cannot improve the safety or efficiency of the system. It is imperative that surveillance data be used to manage the system in a proactive rather than a reactive manner. Proactive traffic management will require the ability to predict traffic conditions. Previous predictive modeling approaches can be grouped into three categories: (a) historical, data-based algorithms; (b) time-series models; and (c) simulations. A relatively new mathematical model, the neural network, offers an attractive alternative because neural networks can model undefined, complex nonlinear surfaces. In a comparison of a backpropagation neural network model with the more traditional approaches of an historical, data-based algorithm and a time-series model, the backpropagation model was clearly superior, although all three models did an adequate job of predicting future traffic volumes. The backpropagation model was more responsive to dynamic conditions than the historical, data-based algorithm, and it did not experience the lag and overprediction characteristics of the time-series model. Given these advantages and the backpropagation model's ability to run in a parallel computing environment, it appears that such neural network prediction models hold considerable potential for use in real-time IVHS applications.

• Record URL:
  http://onlinepubs.trb.org/Onlinepubs/trr/1994/1453/1453-011.pdf
• Record URL:
  https://scholar.google.com/scholar_lookup?title=SHORT-TERM+TRAFFIC+FLOW+PREDICTION%3A+NEURAL+NETWORK+APPROACH&author=B.+Smith&author=M.+Demetsky&publication_year=1994
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/0309060613
• Supplemental Notes:
  • This paper appears in Transportation Research Record No. 1453, Intelligent Transportation Systems: Evaluation, Driver Behavior, and Artificial Intelligence. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved
• Authors:
  • Smith, B L
  • Demetsky, M J
• Publication Date: 1994

Language

• English

Media Info

• Features: Figures; References; Tables;
• Pagination: p. 98-104
• Monograph Title: Intelligent transportation systems: evaluation, driver behavior, and artificial intelligence
• Serial:
  • Transportation Research Record
  • Issue Number: 1453
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Algorithms; Alternatives analysis; Backpropagation; Forecasting; Intelligent transportation systems; Mathematical models; Mathematical prediction; Neural networks; Real time control; Real time data processing; Simulation; Time series analysis; Traffic flow; Traffic volume
• Old TRIS Terms: Proactive traffic management
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory;

Filing Info

• Accession Number: 00676575
• Record Type: Publication
• ISBN: 0309060613
• Files: TRIS, TRB, ATRI
• Created Date: Apr 13 1995 12:00AM