Spatial and Temporal Patterns in Large-Scale Traffic Speed Prediction

Abstract

The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy because traffic tends to exhibit regular patterns over time. However, in practice, the prediction performance can significantly vary across the network and during different time periods. Insight into those spatiotemporal trends can improve the performance of intelligent transportation systems. Traditional prediction error measures, such as the mean absolute percentage error, provide information about the individual links in the network but do not capture global trends. We propose unsupervised learning methods, such as k-means clustering, principal component analysis, and self-organizing maps, to mine spatiotemporal performance trends at the network level and for individual links. We perform prediction for a large interconnected road network and for multiple prediction horizons with an SVR-based algorithm. We show the effectiveness of the proposed performance analysis methods by applying them to the prediction data of the SVR.