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Neural Network Models For Automated Detection Of Non-recurring Congestion

Author

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  • Ritchie, Stephen G.
  • Cheu, Ruey L.

Abstract

This research addressed the first year of a proposed multi-year research effort that would investigate, assess, and develop neural network models from the field of artificial intelligence for automated detection of non- recurring congestion in integrated freeway and signalized surface street networks. In this research, spatial and temporal traffic patterns are recognized and classified by an artificial neural network.

Suggested Citation

  • Ritchie, Stephen G. & Cheu, Ruey L., 1993. "Neural Network Models For Automated Detection Of Non-recurring Congestion," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6r89f2hw, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt6r89f2hw
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    Cited by:

    1. Hall, Randolph & Mehta, Yatrik, 1998. "Incident Management: Process Analysis And Improvement Phase 1: Review Of Procedures," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt45r743q6, Institute of Transportation Studies, UC Berkeley.
    2. Ritchie, Stephen G. & Abdulhai, Baher, 1997. "Development Testing And Evaluation Of Advanced Techniques For Freeway Incident Detection," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5720z2mw, Institute of Transportation Studies, UC Berkeley.

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