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 i...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. Ffrst, 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 exploratory methods are used to investigate the physical meaning of the clusters as well as how the traffic flow vectors 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.展开更多
基金the National Key Basic Research and Development (973) Program of China (No. 2006CB705506)the National High-Tech Research and Development (863) Program of China (No. 2007AA11Z222)the National Natural Science Foundation of China (Nos. 60774034, 60721003, and 50708054).
文摘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. Ffrst, 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 exploratory methods are used to investigate the physical meaning of the clusters as well as how the traffic flow vectors 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.
