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Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

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College of Science, Wuhan University of Technology, Wuhan 430063, China
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School of mathematics and statistics, Pingdingshan University, Pingdingshan 467000, China
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Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2016, 18(12), 454; https://doi.org/10.3390/e18120454
Received: 13 September 2016 / Revised: 22 November 2016 / Accepted: 12 December 2016 / Published: 20 December 2016
(This article belongs to the Section Information Theory, Probability and Statistics)
This paper studies the grey coupled prediction problem of traffic data with panel data characteristics. Traffic flow data collected continuously at the same site typically has panel data characteristics. The longitudinal data (daily flow) is time-series data, which show an obvious intra-day trend and can be predicted using the autoregressive integrated moving average (ARIMA) model. The cross-sectional data is composed of observations at the same time intervals on different days and shows weekly seasonality and limited data characteristics; this data can be predicted using the rolling seasonal grey model (RSDGM(1,1)). The length of the rolling sequence is determined using matrix perturbation analysis. Then, a coupled model is established based on the ARIMA and RSDGM(1,1) models; the coupled prediction is achieved at the intersection of the time-series data and cross-sectional data, and the weights are determined using grey relational analysis. Finally, numerical experiments on 16 groups of cross-sectional data show that the RSDGM(1,1) model has good adaptability and stability and can effectively predict changes in traffic flow. The performance of the coupled model is also better than that of the benchmark model, the coupled model with equal weights and the Bayesian combination model. View Full-Text
Keywords: grey coupled model; panel data characteristics; rolling seasonal grey model (RSDGM(1,1)); cycle truncation accumulated generating operation (CTAGO); nearness grey relational degree; traffic flow prediction grey coupled model; panel data characteristics; rolling seasonal grey model (RSDGM(1,1)); cycle truncation accumulated generating operation (CTAGO); nearness grey relational degree; traffic flow prediction
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Yang, J.; Xiao, X.; Mao, S.; Rao, C.; Wen, J. Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics. Entropy 2016, 18, 454.

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