中南大学学报(英文版)
中南大學學報(英文版)
중남대학학보(영문판)
JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY(ENGLISH EDITION)
2015年
2期
779-786
,共8页
孟梦%王博彬%邵春福%李慧轩%黃育兆
孟夢%王博彬%邵春福%李慧軒%黃育兆
맹몽%왕박빈%소춘복%리혜헌%황육조
engineering of communication and transportation system%short-term traffic flow prediction%advancedk-nearest neighbor method%pattern recognition%balanced binary tree technique
Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems (ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advancedk-nearest neighbor (AKNN) method and balanced binary tree (AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method,k-nearest neighbor (KNN) method and the auto-regressive and moving average (ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions. The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.