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Traffic forecasting in urban area has attracted substantial attention in recent years due to its significant assitance for traffic dispatching and trip planning. However, this task is very challenging due to the complex dependencies inherent in the traffic process. In our study, recent and periodic dependencies are identified and used to describe the corresponding near-term and long-term effects of historical traffic data on future traffic states. Subsequently, during the recent dependency modeling, each road is found to correlate with its adjacent roads through traffic flow diffusion, then the correlation intensities are quantified and used to choose strongly correlated roads to build a critical road sequence. While for the periodic dependency modeling, since the historical speed series of a road segment exhibits multi-frequency attributes (i.e., low-frequency daily period and high-frequency stochastic fluctuation), wavelet transform is conducted to decompose the original speed series into low and high-frequency sub-series. On these bases, we propose a hybrid traffic speed forecasting model, flow and wavelet-integrated spatio-temporal network (FW-STN). In the FW-STN, the recent features are captured by the convolutional neural network (CNN) with near-term traffic data from the derived critical road sequence, and the periodic features are captured by the long short-term memory (LSTM) with the low and high-frequency sub-series. Both the recent and periodic features are then fused to conduct the final prediction. Experimental results on real traffic data show that the proposed approach outperforms nine state-of-the-art methods (with improvements of 3% ~15% in mean average percentage error).

As a crucial part of the Intelligent Transportation System, traffic forecasting is of great help for traffic management and guidance. However, predicting short-term traffic conditions on a large-scale road network is challenging due to the complex spatio-temporal dependencies found in traffic data. Previous studies used Euclidean proximity or topological adjacency to explore the spatial correlation of traffic flows, but did not consider the higher-order connectivity patterns exhibited in a road network, which have a significant influence on traffic propagation. Meanwhile, traffic sequences display distinct multiple time-frequency properties, yet few researchers have made full use of this resource. To fill this gap, we propose a novel hybrid framework - Wavelet-based Higher-order Spatial-Temporal method (Wavelet-HST) to accurately predict network-scale traffic speeds. Wavelet-HST first uses discrete wavelet transform (DWT) to decompose raw traffic data into several components with different frequency sub-bands. Then a motif-based graph convolutional recurrent neural network (Motif-GCRNN) is proposed to learn the higher-order spatio-temporal dependencies of traffic speeds from low-frequency components, and auto-regressive moving average (ARMA) models are employed to simulate random fluctuations from the high-frequency components. We evaluate the framework on a traffic dataset collected in Chengdu, China, and experimental results demonstrate that Wavelet-HST outperforms six state-of-art prediction methods by an improvement of 7.8% ~10.5% in the root mean square error.