PREDICTING TRAFFIC CONGESTION USING RECURRENT NEURAL NETWORKS

Proper prediction of traffic congestion is an essential component of many intelligent transportation systems (ITS). Multi-layer feed-forward (MLF) networks were commonly used for this purpose. However, MLF models fail to fully capture the temporal relationship among traffic series. In this paper, we present a traffic congestion prediction method based on partially recurrent neural networks (RNNs). In recurrent neural networks, the temporal relationship of the traffic series data is explicitly modeled via internal states. By using sensors embedded in the expressway, Elman RNN models have been developed to predict future volume and occupancy values 1, 5, 10 and 15 minutes in advance given current sensor data. A comparison between recurrent and traditional MLF neural networks was performed and the results suggest that RNN outperformed MLF with up to 5 percent improvement of prediction accuracy.

• Supplemental Notes:
  • Full Conference Proceedings available on CD-ROM.
• Corporate Authors:

  ITS America

  1100 17th Street, NW, 12th Floor
  Washington, DC  United States  20036
• Authors:
  • Zhou, C
  • Nelson, P C
• Conference:
  • 9th World Congress on Intelligent Transport Systems
  • Location: Chicago, Illinois
  • Date: 2002-10-14 to 2002-10-17
• Publication Date: 2002

Language

• English

Media Info

• Pagination: 9p

Subject/Index Terms

• TRT Terms: Expressways; Improvements; Intelligent transportation systems; Neural networks; Sensors; Traffic congestion; Traffic volume
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 00960260
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 8 2003 12:00AM