Abstract

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).

Highlights

  • Traffic forecasting in urban road networks plays an important role in Intelligent Transportation Systems (ITS) [1]

  • 2) We propose to learn the periodic dependencies of traffic process with different-frequency sub-series decomposed by wavelet transform

  • In this paper, we propose a hybrid model, namely a flow and wavelet-integrated spatio-temporal network (FW-STN), for short-term traffic speed forecasting of individual road segment

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Summary

Introduction

Traffic forecasting in urban road networks plays an important role in Intelligent Transportation Systems (ITS) [1]. Accurate and reliable traffic forecasting can help to improve transportation dispatching and guide urban travel. Traffic controllers can arrange vehicle diversions and allocate resources before the predicted congestion occurs, while drivers can choose their driving paths based on the predicted traffic states of road segments in order to reduce travel time. These practical capabilities have made traffic forecasting a hot topic for researchers in this field. Traffic forecasting aims to make accurate estimations of future traffic states based on historical traffic data, as well as additional context information [3]. Complex dependencies exist between these factors and future traffic states, making the forecasting a very challenging one

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