Neural Network Models For Automated Detection Of Non-recurring Congestion

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.

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Year of publication:	1993-01-01
Authors:	Ritchie, Stephen G. ; Cheu, Ruey L.
Institutions:	Institute for Transportation Studies (ITS), University of California-Berkeley
Subject:	Neural networks (Computer science) \| Traffic congestion \| Detectors \| Disabled vehicles on express highways

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Extent:	application/pdf
Series:	Institute of Transportation Studies, Research Reports, Working Papers, Proceedings.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series Institute of Transportation Studies (UCB)
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010536711