Abstract
In order to meet the highway guidance demand, this work studies the short-term traffic flow prediction method of highway. The Yu-Wu highway which is the main road in Chongqing, China, traffic flow time series is taken as the study object. It uses phase space reconstruction theory and Lyapunov exponent to analyze the nonlinear character of traffic flow. A new Volterra prediction method based on model order reduction via quadratic-linear systems (QLMOR) is applied to predict the traffic flow. Compared with Taylor-expansion-based methods, these QLMOR-reduced Volterra models retain more information of the system and more accuracy. The simulation results using this new Volterra model to predict short time traffic flow reveal that the accuracy of chaotic traffic flow prediction is enough for highway guidance and could be a new reference for intelligent highway management.
Highlights
The prediction of highway traffic flow is a crucial part of transportation planning, traffic control, and intelligent transportation systems [1]
A new predicting model based on model order reduction via quadratic-linear systems is used to predict the chaotic traffic flow
Comparing with RBFNN prediction algorithm and old Volterra algorithm, the results show that the accuracy of new Volterra algorithm is better than RBFNN algorithm and old Volterra algorithm
Summary
The prediction of highway traffic flow is a crucial part of transportation planning, traffic control, and intelligent transportation systems [1]. During the decades a variety of techniques have been applied to forecast the shortterm traffic flow, such as fuzzy theory [3], neural networks [4], Kalman filter [5], and wavelet analysis [6] et al But the models generated by these methods could not capture some strongly nonlinear characteristics of short-term traffic flow data. Chaos theory is an effective tool to study nonlinear system This tool has been applied to forecast short-term traffic flow time series [7, 8]. If the traffic flow data is a chaotic time series, the phase space reconstruction theory could be applied to predict the traffic flow. A new predicting model based on model order reduction via quadratic-linear systems is used to predict the chaotic traffic flow This new prediction model has no truncation error and contains more information of original system. It is more accurate than other prediction models ever used before
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