EMD based hybrid models in short term traffic speed forecast Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/w66347420

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  • Short-term freeway traffic speed prediction is essential to improving mobility and safety. It has been a challenging, yet unresolved issue. Traffic speed prediction can be applied to enhance the intelligent freeway traffic management and control for applications as operational and regulation planning. For example, with more reliable traffic speed prediction, the Advanced Traveler Information System (ATIS) can provide travelers with travel time information which allows travelers to arrange their schedule accordingly. Moreover, traffic managers can use the prediction information to deploy various traffic management strategies so as to increase the efficiency of the whole network. In this research, a hybrid empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA) (or equivalently EMD-ARIMA) approach is developed to predict the short-term traffic speed on freeways. In general, there are three stages in the hybrid EMD-ARIMA forecasting framework. The first step is the EMD stage which decomposes the freeway traffic speed series data into a number of intrinsic mode function (IMF) components and a residue. The second stage is to find the appropriate ARIMA model for each IMF and residue, then make predictions based on the appropriate ARIMA model. The third stage is to combine the prediction results of each IMF and residue to get the predictions. Two experiments are conducted thereafter. The experimental results indicate that the proposed hybrid EMD-ARIMA framework is capable to predict short-term freeway traffic speed with high accuracy.
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