Traffic Speed Prediction: An Attention-Based Method.

Duanyang Liu,Xiao Han,Guojiang Shen,Longfeng Tang

doi:10.3390/s19183836

Duanyang Liu, Xiao Han + Show 2 more

Open Access

https://doi.org/10.3390/s19183836

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Abstract

Short-term traffic speed prediction has become one of the most important parts of intelligent transportation systems (ITSs). In recent years, deep learning methods have demonstrated their superiority both in accuracy and efficiency. However, most of them only consider the temporal information, overlooking the spatial or some environmental factors, especially the different correlations between the target road and the surrounding roads. This paper proposes a traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA) to address the above issues. We apply temporal clustering to the target road to distinguish the traffic environment. Traffic data in each cluster have a similar distribution, which can help improve the prediction accuracy. A hierarchical attention-based mechanism is then used to extract the features at each time step. The encoder measures the importance of spatial features, and the decoder measures the temporal ones. The proposed method is evaluated over the data of a certain area in Hangzhou, and experiments have shown that this method can outperform the state of the art for traffic speed prediction.

Highlights

Traffic speed prediction, especially short-term prediction, has become increasingly important in intelligent transportation systems (ITSs) [1]
To deal with the problems above, we introduce a novel traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA)
We propose a traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA) for traffic speed prediction

Summary

Introduction

Especially short-term prediction (less than 20 min), has become increasingly important in intelligent transportation systems (ITSs) [1]. Many modern traffic facilities and applications rely heavily on the accuracy of prediction. The navigation system can provide an optimal route for travelers based on real-time prediction, and can calculate the cost of travel time, which is helpful for making plans. The traffic speed can reflect the traffic state of the road network; based on the current value of traffic speed and its future short-term change trend, managers can partition the traffic network [2], optimize the signal timing, and guide the traffic traveling, so as to make full use of road resources and alleviate traffic congestion. Considering that the current traffic states are relevant to the upstream and downstream roads, and are similar to the same horizon of previous weekdays and weekends, various data-driven algorithms have been proposed to increase the prediction reliability and accuracy. Approaches can be categorized into three parts: parametric methods, non-parametric methods, and deep learning methods [3,4,5]

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Results

Conclusion