SHORT-TERM INTERURBAN TRAFFIC FORECASTS USING NEURAL NETWORKS

Back-propagation neural networks were trained to make short-term forec asts of traffic flow, speed and occupancy in the Utrecht/Rotterdam/Hag ue region of The Netherlands. A problem which had to be faced when des igning the system was the vast number of possible input parameters. Wh ilst neural networks which utilised all available inputs performed wel l, their size made them impractical for implementation. A technique of stepwise reduction of network size was developed by elasticity testin g the large neural networks, showing a way of overcoming this difficul ty. Results for occupancy and flow forecasts by this method show some promise, but do not out-perform naive predictors. Forecasts of vehicle speed were much less successful, perhaps because of the distorting ef fect of slow moving vehicles, particularly in low flow conditions. The elasticity tests were found to be useful, not only as a means of enab ling network size reduction, but as a means of interpreting the neural network model.