Multi-Dimensional Traffic Flow Time Series Analysis with Self-Organizing Maps

The two important features of self-organizing maps (SOM), topological preservation and easy visualization, give it great potential for analyzing multi-dimensional time series, specifically traffic flow time series in an urban traffic network. This paper investigates the application of SOM in the representation and prediction of multi-dimensional traffic time series. First, SOMs are applied to cluster the time series and to project each multi-dimensional vector onto a two-dimensional SOM plane while preserving the topological relationships of the original data. Then, the easy visualization of the SOMs is utilized and several explora-tory methods are used to investigate the physical meaning of the clusters as well as how the traffic flow vec-tors evolve with time. Finally, the k-nearest neighbor (kNN) algorithm is applied to the clustering result to perform short-term predictions of the traffic flow vectors. Analysis of real world traffic data shows the effec-tiveness of these methods for traffic flow predictions, for they can capture the nonlinear information of traffic flows data and predict traffic flows on multiple links simultaneously.