Abstract
As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed prediction model based on wavelet transform and neural network is, therefore, proposed in this article to improve traffic prediction methods. The wavelet transform is used to decompose the original traffic speed data, and then the coefficients obtained after the decomposition are used to reconstruct the high-frequency random sequences and the low-frequency trend sequence. Secondly, a GRU neural network is constructed to learn the trend of low-frequency sequence. The spatiotemporal correlation between input data is extracted by adjusting the input of the model. Meanwhile, an ARMA model is used to fit unstable random fluctuations of high-frequency sequences. Last of all, the prediction results of the two models are added together to obtain the final prediction result. The proposed prediction model is validated by using road section speed data based on the floating car data collected in Ningbo. The results show that the proposed model has high accuracy and robustness.
Highlights
With the socioeconomic development and the acceleration of urbanization, the demand for transportation continues to grow. ough the urban transportation system has been developing as well, trying to match with the increasing demand for transportation, and its supply capacity is improving through the construction of transportation infrastructure, it is still one step behind
A speed prediction study down to the lane scale has been proposed recently. e researchers built a two-layer deep learning framework that combines Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) to predict the speed of different lanes [12]. e combined prediction model combines different prediction algorithms, which can give full play to the advantages of each model to obtain better predicted results
The model is composed of three parts: wavelet transform (W), GRU, and autoregressive moving average (ARMA). e spatiotemporal relationship of urban traffic speed data is taken into account in this model
Summary
With the socioeconomic development and the acceleration of urbanization, the demand for transportation continues to grow. ough the urban transportation system has been developing as well, trying to match with the increasing demand for transportation, and its supply capacity is improving through the construction of transportation infrastructure, it is still one step behind. Traffic congestion often starts on one or more sections and spreads to other sections after a period of time, resulting in regional congestion [6] Regarding this trait of congestion, some scholars in early years had built nonparametric models using speed data of the studied road section and its upstream and Mathematical Problems in Engineering downstream sections, which can better capture the spatiotemporal correlation between road sections and will improve the accuracy of the prediction models [7]. Due to their high flexibility, good learning, and generalization capabilities, algorithms based on neural networks have been widely used in transportation-related tasks [8].
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