Analysis and Forecasting for Traffic Flow Data

The urban transportation system involves the challenging task of transferring people and materials across densely populated areas, and hence its operational efficiency directly affects the entire city. In this study, we overcome the restriction of both time and space by introducing an online version of the principal component analysis (PCA), called the projection approximation subspace tracking with deflation (PASTd) algorithm. The algorithm is implemented to derive core traffic patterns of traffic flow data of Baltimore, Maryland, US. The k-nearest-neighbor (KNN) method is applied to predict the values of these core traffic patterns in the near future. Thus, the traffic information of Baltimore County can be forecasted with linear complexity and traffic congestion can be traced with little latency. Unlike traditional traffic prediction methods, our method aims at network-level prediction, regardless of urban or freeway road segments. The results show that our forecasting method is efficient, flexible, and robust.