Abstract:
Transportation services are increasingly important in people’s daily lives and benefit individuals and economic development. Traffic flows are unpredictable and volatile, limiting transportation services. To stabilize intelligent transport systems and optimize traffic scheduling, precise traffic flow forecasting is essential. This paper examines short-term traffic flow forecasting using a deep belief network ensemble approach. Real-world traffic flow data is decomposed into Intrinsic Mode Functions (IMFs) and EEMD residue. Then, using weather conditions and day properties, the mRMR (minimum Redundancy Maximum Relevance Feature Selection) method extracts the essential feature subset for each component. The ensemble model’s output is summed up from each component’s DBN-trained forecasting results. The proposed method outperforms the single DBN and other methods.
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