Short-Term Traffic Flow Forecasting Based on Periodicity Similarity Characteristics
The methodology has been putted forward that the periodicity similarity should be consideration when using Elman neural network (ENN) to forecast short-term traffic flow, which is not only helpful to save training time, reduce training sample size, but also enhance forecasting efficiency. Firstly, training sample of ENN has been designed based on the periodicity similarity of traffic flow and network structure has been established aiming at improving ENN global stability. Secondly, short-term traffic flow forecasting method based on ENN have been established by taking daily, weekly and monthly periodicity similarity into account respectively. Finally, forecasting results have been evaluated by four error statistics from two aspects: forecasting effect and efficiency. The conclusion has been summarized to three aspects.
KeywordsTraffic Flow Similarity Coefficient Mean Absolute Percentage Error Forecast Result Transportation Research Part
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