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THE USE OF NEURAL NETWORKS TO RECOGNISE AND PREDICT TRAFFIC CONGESTION
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1993
Year
Traffic TheoryEngineeringMachine LearningTraffic FlowRecurrent Neural NetworkIntelligent Traffic ManagementData SciencePattern RecognitionTraffic PredictionTransportation EngineeringShort Term ForecastingPredictive AnalyticsNeural Network TechnologyComputer ScienceStatistical Pattern RecognitionForecastingIntelligent ForecastingTraffic FlowsTraffic ModelCongestion Management
This paper shows how a new method of called neuro- computing (neural networks) can assist in the following types of pattern recognition problem: a) the problem of recognising that the road system is in a particular state of congestion; and b) the problem of real time short term forecasting. Section 2 describes the main features of a neural network approach. Trials of its application to: a) congestion recognition; and b) to short term forecasting of traffic flows on an urban network are presented in sections 3 and 4 respectively. A method for training neural networks via a simulation package to infer parameters that are not directly measurable is then suggested. The results indicate that although much work still needs to be done, neural network technology can provide a powerful method of analysing, interpreting and predicting complex data sets.