Open AccessWavelet‐based short‐term forecasting with improved threshold recognition for urban expressway traffic conditions
Author(s) -
Xiangxue Wang,
Lunhui Xu
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2017.0236
Subject(s) - traffic flow (computer networking) , computer science , intelligent transportation system , kalman filter , robustness (evolution) , traffic generation model , wavelet , traffic congestion reconstruction with kerner's three phase theory , data mining , artificial neural network , cluster analysis , floating car data , term (time) , artificial intelligence , real time computing , traffic congestion , engineering , transport engineering , biochemistry , chemistry , computer security , gene , physics , quantum mechanics
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.