A Short-term Traffic Flow Prediction Method Based on Similarity Search of Time Series
In order to improve the effectiveness of short-term traffic flow prediction,in view of the existing forecasting models,this paper presents a short-term traffic flow multi-step prediction method based on similarity search of time series.Firstly,the landmark model is used to represent time series of traffic flow data.Then,the input data of prediction model are determined through searching similar time series.Finally,an echo state networks model is used for traffic flow multi-step prediction.The performance of the proposed method is evaluated with expressway traffic flow data collected from loop detectors in a Chinese megacity.The experimental results demonstrate that the prediction accuracy of the echo state networks model is 6.25％ and 3.85％ higher than ARRIMA model and BP neural network model,respectively.Therefore,using time series similarity search results as input data,the model can further improve the short-term traffic flow prediction accuracy.
time seriessimilarity searchlandmark modelecho state networksshort-term prediction
国家科技期刊平台
NSTL
万方数据
维普
