On-line prediction of border crossing traffic using an enhanced Spinning Network method

• We enhance the Spinning Network (SPN) algorithm developed by Huang and Sadek (2009).
• We incorporate the Dynamic Time Warping (DTW) algorithm into SPN.
• We characterize the complexity and nonlinearity of the Peace Bridge volume dataset.
• We develop predictive models for the Peace Bridge Border Crossing.
• We compare SPN performance to SARIMA and Support Vector Regression.

This paper improves on the Spinning Network (SPN) method, a novel forecasting technique, inspired by human memory which was recently developed by Huang and Sadek (2009). The improvement centers on the use of the Dynamic Time Warping (DTW) algorithm to assess the similarity between two given time series, instead of using the Euclidean Distance as was the case with the original SPN. Following this, the enhanced method (i.e., hereafter referred to as the DTW–SPN) is used to predict hourly traffic volumes at the Peace Bridge, an international border crossing connecting Western New York State in the U.S. and Southern Ontario in Canada. The performance of the DTW–SPN is then compared to that of three other forecasting methods, namely: (1) the original SPN (referred to as the Euclidean–SPN); (2) the Seasonal Autoregressive Integrated Moving Average (SARIMA) method; and (3) Support Vector Regression (SVR). Both classified as well as non-classified datasets are utilized, with the classification made on the basis of the type of the day to which the data items belong (i.e. Mondays through Thursdays, Fridays, weekends, holidays, and game days). The results indicate that, in terms of the Mean Absolute Percent Error, the DTW–SPN performed the best for all data groups with the exception of the “game day” group, where SVR performed slightly better. From a computational efficiency standpoint, the SPN-type algorithms require runtime significantly lower than that for either SARIMA or SVR. The performance of the DTW–SPN was also quite acceptable even when the data was not classified, indicating the robustness of the proposed forecasting method in dealing with heterogeneous data.