期刊名称：Neural Information Processing: Letters and Reviews
电子版ISSN：1738-2532
出版年度：2006
卷号：10
期号：8
页码：201-209
出版社：Neural Information Processing
摘要：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.
