Wavelet‐based short‐term forecasting with improved threshold recognition for urban expressway traffic conditions. Issue 6 (6th March 2018)

• Record Type: Journal Article
• Authors: Xiangxue, Wang; Lunhui, Xu
• Is Part Of: IET intelligent transport systems. Volume 12:Issue 6(2018)
• Page Start: 463
• Page End: 473
• Publication Date: 2018-03-06
• Subjects: intelligent transportation systems -- driver information systems -- road traffic control -- discrete wavelet transforms -- pattern clustering; wavelet‐based short‐term flow forecasting -- improved threshold recognition -- urban expressway traffic conditions -- traffic flow prediction -- intelligent route planning -- rerouting -- data‐driven mixed models -- traffic flow threshold identification -- improved semisupervised K‐means clustering algorithm -- hybrid multiscale traffic speed forecasting method -- wavelet decomposition -- traffic condition index -- three‐phase traffic flow theory -- traffic status -- model performance evaluation -- multisource travel speed data -- traffic threshold recognition algorithm -- three‐phase traffic flow transition -- adaptive thresholds -- real‐time quantitative surveillance; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2017.0236
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Accurate traffic flow prediction can provide reliable and precise information for traffic departments to formulate effective management measures and assist drivers in performing more intelligent route planning and rerouting. The authors propose a short‐term traffic flow forecasting framework for urban expressways based on data‐driven mixed models including an approach to traffic flow threshold identification based on an improved semi‐supervised K ‐means clustering algorithm, a hybrid multi‐scale traffic speed forecasting method based on wavelet decomposition, and a traffic condition index corresponding to three‐phase traffic flow theory for reflecting traffic status in real time. Model performance evaluation is performed using multi‐source travel speed data. The results show that the traffic threshold recognition algorithm can correctly identify traffic speed thresholds confirming to the three‐phase traffic flow transition and that the proposed short‐term estimation technique outperforms traditional auto‐regressive integrated moving average models, extended Kalman filtering methods, and artificial neural network models in terms of both accuracy and robustness. The proposed traffic condition index using adaptive thresholds and predicted speeds can provide real‐time quantitative surveillance for urban expressway traffic.