Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast

•We sequentially concatenate Neural Network with ARIMA for traffic state prediction.•Neural Network captures network-scale co-movement pattern of all traffic flows.•ARIMA postprocesses the NN residuals to extract location-specific traffic features.•The postprocessing significantly improves the accur...

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Vydáno v:Transportation research. Part C, Emerging technologies Ročník 111; s. 352 - 372
Hlavní autoři: Ma, Tao, Antoniou, Constantinos, Toledo, Tomer
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.02.2020
Témata:
ARIMA
Machine learning
MSVR
Network-wide traffic prediction
Neural network
Postprocess residuals
Postprocess residuals
Network-wide traffic prediction
Neural network
MSVR
ARIMA
Machine learning
ISSN:0968-090X, 1879-2359
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Popis
Shrnutí:•We sequentially concatenate Neural Network with ARIMA for traffic state prediction.•Neural Network captures network-scale co-movement pattern of all traffic flows.•ARIMA postprocesses the NN residuals to extract location-specific traffic features.•The postprocessing significantly improves the accuracy of prediction. We propose a novel approach for network-wide traffic state prediction where the statistical time series model ARIMA is used to postprocess the residuals out of the fundamental machine learning algorithm MLP. This approach is named as NN-ARIMA. Neural Network MLP is employed to capture network-scale co-movement pattern of all traffic flows, and ARIMA is used to further extract location-specific traffic features in the residual time series out of Neural Network. The experiment results show that the postprocessing the residuals of Neural Network by the ARIMA analysis helps to significantly improve accuracy of traffic state prediction by 8.9–13.4% in term of mean squared error reduction. In order to verify the efficiency of the ARIMA analysis in the postprocessing, Multidimensional Support Vector Regression (MSVR) model is also employed to replace the role of Neural Network in the comparative experiment. Two streams of comparisons, (1) NN vs. NN-ARIMA and (2) MSVR vs. MSVR-ARIMA, are performed and show consistent results. The proposed approach not only can capture network-wide co-movement pattern of traffic flows, but also seize location-specific traffic characteristics as well as sharp nonlinearity of macroscopic traffic variables. The case study indicates that the accuracy of prediction can be significantly improved when both network-scale traffic features and location-specific characteristics are taken into account.
ISSN:0968-090X
1879-2359
DOI:10.1016/j.trc.2019.12.022