A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework

This paper presents a hybrid short-term traffic speed prediction framework through empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA). The goals of this paper are to investigate (1) does the hybrid model provide better short-term traffic conditions (i.e. traffic speeds) than the traditional models? (2) how the performance of the hybrid model varies for varying scenarios such as mixed traffic flow and vehicle-type specific traffic prediction in a work zone, on-ramp, and off-ramp; and (3) why hybrid models provide better prediction than other single-staged models. Using empirical data from a work zone on interstate I91 in Springfield, MA and the on/off-ramp data from the Georgia State Route 400, the proposed hybrid EMD–ARIMA modelling framework is tested in the four distinct scenarios aforementioned. The prediction results of the hybrid EMD–ARIMA model are evaluated against the experimental data and also compared with the results from the traditional ARIMA, the Holt–Winters, the artificial neural network models, and a naive model. The evaluation results showed that the hybrid EMD–ARIMA model outperforms the traditional forecasting models in different scenarios.

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