Real-time Traffic Flow Forecasting based on Wavelet Neural Network

Rihan Li, Jianmin Xu, Qiang Luo, Sangen Hu


Real-time traffic flow forecasting is the core of Intelligent Transportation System (ITS), and the foundation of multi-subsystemâ??s implementation in ITS. Traffic flow, which is highly time-relevant, with the features of high non-linear and non-determinism, can be treated as the time sequence forecast. On the basis of these features of traffic flow, this paper tries to deal with this issue based on Wavelet Neural Network (WNN) specially. At the same time, the paper realizes the analogue simulation through the Matlab software programming, by taking a road for example. And the simulation results show that the traffic flow can be precisely forecasted using Wavelet Neural Network, and its value is close to the expectations.


ITS; Real-time Forecasting; Traffic Flow; WNN

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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