Genetically-Designed Time Delay Neural Networks for Multiple-interval Urban Freeway Traffic Flow Forecasting

Previous research for short-term traffic prediction mostly forecasts only one time interval ahead. Such a methodology may not be adequate for response to emergency circumstances and road maintenance activities that last for a few hours or a longer period. In this study, various approaches, including naïve factor methods, exponential weighted moving average (EWMA), autoregressive integrated moving average (ARIMA), and genetically-designed time delay neural network (GA-TDNN) are proposed for predicting traffic flow of continuous 12 hours ahead on a freeway near the City of Calgary, Canada. Study results show that the ARIMA models outperform EWMA models, which in turn superior to the factor methods. GA-TDNN results in only comparable accuracy with the ARIMA model, and it seems not worth to develop such complicated models. However, the adaptive nature of neural networks promises better accuracy as they are exposed to more observations during field operation. Its non-parametric approach also guarantees a greater portability and much faster computing speed for real-time applications. Keywords— Time-delay neural network (TDNN), autoregressive integrated moving average (ARIMA), traffic forecasting