COMBINING KOHONEN MAPS WITH ARIMA TIME-SERIES MODELS TO FORECAST TRAFFIC FLOW

A hybrid method of short-term traffic forecasting is introduced; the K ARIMA method. The technique uses a Kohonen self-organizing map as an i nitial classifier; each class has an individually tuned ARIMA model as sociated with it. Using a Kohonen map which is hexagonal in layout eas es the problem of defining the classes. The explicit separation of the tasks of classification and functional approximation greatly improves forecasting performance compared to either a single ARIMA model or a backpropagation neural network. The model is demonstrated by producing forecasts of traffic flow, at horizons of half an hour and an hour, f or a French motorway. Performance is similar to that exhibited by othe r layered models, but the number of classes needed is much smaller (ty pically between two and four). Because the number of classes is small, it is concluded that the algorithm could be easily retrained in order to track long-term changes in traffic flow and should also prove to b e readily transferrable. Copyright (C) 1996 Elsevier Science Ltd