Short Term Freeway Traffic Flow Prediction Using Genetically-Optimized Time-Delay-Based Neural Networks

Proper prediction of traffic flow parameters is an essential component of any proactive traffic control system and one of the pillars of advanced management of dynamic traffic networks. In this paper, the authors present a new short term traffic flow prediction system based on an advanced Time Delay Neural Network (TDNN) model, the structure of which is optimized using a Genetic Algorithm (GA). The model's performance is validated using both simulated and real traffic flow data from the California Testbed in Orange County, California. The model predicts flow and occupancy values at a given freeway site based on contributions from their recent temporal profile as well the spatial contribution from neighboring sites. Both temporal and spatial effects were found essential for proper prediction. An in-depth investigation of the variables pertinent to traffic flow prediction was conducted examining the extent of the “look-back” interval, the extent of prediction in the future, the extent of spatial contribution, the resolution of the input data, and their effects on prediction accuracy. Results obtained indicate that the prediction errors vary inversely with the extent of the spatial contribution, and that the inclusion of three loop stations in both directions of the subject station is sufficient for practical purposes. Also, the longer the extent of prediction, the more the predicted values tend toward the mean of the actual, for which case the optimal look-back interval also shortens. Interestingly, it was found that coarser data resolution is better for longer extents of prediction. The implication is that the level of data aggregation/resolution should be comparable to the prediction horizon for best accuracy. The model performed acceptably using both simulated and real data. The model also showed potential to be superior to such other well-known neural network models as the Multi layer Feed-forward (MLF) when applied to the same problem.

• Record URL:
  http://www.escholarship.org/uc/item/4t05p2mp
• Record URL:
  https://merritt.cdlib.org/d/ark%3A%2F13030%2Fm56b075t/1/producer%2F41183306.pdf
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41183306
• Corporate Authors:

  University of California, Berkeley

  California PATH Program, Institute of Transportation Studies
  Richmond Field Station, 1357 South 46th Street
  Richmond, CA  United States  94804-4648

  University of Toronto

  Department of Civil Engineering, 35 St George Street
  Toronto, Ontario  Canada  M5S 1A4

  University of California, Irvine

  Institude of Transportation Studies
  Department of Civil and Environmental Engineering
  Irvine, CA  United States  92697-3600

  California Department of Transportation

  Office of Research, P.O. Box 942873
  Sacramento, CA  United States  94273-0001
• Authors:
  • Abdulhai, B
  • Porwal, H
  • Recker, W
• Publication Date: 1999-1

Language

• English

Media Info

• Media Type: Digital/other
• Pagination: 25p + appendix
• Serial:
  • PATH Working Paper
  • Publisher: University of California, Berkeley
  • ISSN: 1055-1417

Subject/Index Terms

• TRT Terms: Advanced traffic management systems; Advanced traffic management systems; Forecasting; Genetic algorithms; Highway traffic control; Intelligent transportation systems; Mathematical models; Neural networks; Traffic flow
• ATRI Terms: Advanced traffic management systems (ATMS); Forecast; Intelligent transport systems (ITS); Modelling; Neural network; Traffic control; Traffic flow
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01395940
• Record Type: Publication
• Source Agency: ARRB
• Report/Paper Numbers: UCB-ITS-PWP-99-1
• Files: PATH, CALTRANS, TRIS, ATRI, STATEDOT
• Created Date: Aug 23 2012 2:22PM