OPTIMIZATION OF DYNAMIC NEURAL NETWORKS PERFORMANCE FOR SHORT-TERM TRAFFIC PREDICTION

This paper presents an approach to optimize the short-term traffic prediction performance using multiple topologies of dynamic neural networks and various network-related and traffic-related settings. The study emphasizes the potential benefit of optimizing the prediction performance by deploying multi-model approaches under parameters and traffic condition settings. The emphasis of the paper is on the application of temporal-processing topologies in short-term speed predictions in the range of 5 to 20 minute-horizons. Three network topologies are utilized: Jordan/Elman, partially recurrent networks and time-lagged feedforward networks. The input patterns were constructed from data collected at the target location, as well as the upstream and downstream locations. However, various combinations were also considered. To encourage the networks to associate with historical information on recurrent conditions, a time factor was attached to the input patterns to introduce time recognition capabilities, in addition to information encoded in the recent past data. The optimal prediction settings (type of topology and input settings) were determined such that the performance is maximized under different traffic conditions at the target and adjacent locations. The optimized performance of the dynamic neural networks was compared to that of a statistical non-linear time series approach, which was outperformed in most cases

• Supplemental Notes:
  • Publication Date: 2003. Transportation Research Board, Washington DC. Remarks: Paper prepared for presentation at the 82nd annual meeting of the Transportation Research Board, Washington, D.C., January 2003. Format: CD ROM
• 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

  California Department of Transportation

  1120 N Street
  Sacramento, CA  United States  95814

  University of California, Berkeley

  Department of Electrical Engineering and Computer Sciences
  Berkeley, CA  United States  94720
• Authors:
  • Ishak, Sherif
  • Kotha, Prashanth
  • Alecsandru, Ciprian
• Conference:
  • Transportation Research Board 82nd Annual Meeting
  • Location: Washington DC, United States
  • Date: 2003-1-12 to 2003-1-16
• Date: 2003

Language

• English

Media Info

• Pagination: 21 p.

Subject/Index Terms

• TRT Terms: Motor vehicles; Neural networks; Speed; Traffic estimation; Traffic speed
• Subject Areas: Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 00942941
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: PATH, STATEDOT
• Created Date: Jun 2 2003 12:00AM