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A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

Meng, Meng ORCID: 0000-0001-7240-6454, Shao, Chun-fu, Wong, Yiik-diew, Wang, Bo-bin and Li, Hui-xuan (2015) A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques. Journal of Central South University, 22 (2). pp. 779-786. ISSN 2095-2899 (Print), 2227-5223 (Online) (doi:https://doi.org/10.1007/s11771-015-2582-y)

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Abstract

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 advanced k-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.

Item Type: Article
Uncontrolled Keywords: engineering of communication and transportation system short-term traffic flow prediction advanced k-nearest neighbor method pattern recognition balanced binary tree technique
Subjects: H Social Sciences > HE Transportation and Communications
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Connected Cities Research Group
Faculty of Business > Department of Systems Management & Strategy
Last Modified: 11 Feb 2019 16:38
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/22698

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