Abstract
Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep 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, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods.
Highlights
Accurate and real-time traffic flow forecasting is essential to adaptive traffic control system and traffic guidance system, which is of great significance for alleviating urban traffic congestions
Aiming at the shortcomings of the previous traffic flow forecasting methods, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series
This paper proposed a short-term traffic flow multistep prediction method based on similarity search of time series
Summary
Accurate and real-time traffic flow forecasting is essential to adaptive traffic control system and traffic guidance system, which is of great significance for alleviating urban traffic congestions. Hu et al [5] proposed a short-term traffic flow forecasting method based on chaotic theory, which is a significant attempt to forecast traffic flow from the viewpoint of nonlinear time series. It is essential to establish a short-term traffic flow multistep forecasting method which can make full use of similarity characteristics of traffic flow data. Aiming at the shortcomings of the previous traffic flow forecasting methods, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. The general idea of the proposed method mainly includes two parts: first, the input data of prediction model are determined by searching similar time series instead of the data of the prior time instant; second, the echo state networks model is used for short-term traffic flow multistep forecasting.
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