Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks.

Sen Zhang,Jianjun Hu,Shaobo Li,Yong Yao,Yong Zhao,Jie Hu

doi:10.3390/s19102229

Abstract

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

Highlights

Onsets of freeway congestion reduce efficiency and capacity of transportation networks and should be forecast to take measures to prevent its formation in an accurate and timely manner in most situations [1,2]
We propose an accessible and general approach to collect, transform, and represent snapshots of transportation network maps marked with traffic congestion conditions for roads inside, which are publicly available from transportation administrative departments and online traffic map service providers
To integrate deep autoencoders (DAE)-like architectures and dense layers for traffic congestion prediction, we propose to use the architecture in Figure 2 for Deep Congestion Prediction Network (DCPN) to forecast traffic congestion on a network scale

Summary

Introduction

Onsets of freeway congestion reduce efficiency and capacity of transportation networks and should be forecast to take measures to prevent its formation in an accurate and timely manner in most situations [1,2]. Various kinds and amounts of traffic data have been used by researchers in recent years for traffic condition prediction and related research of intelligent transportation systems Most of these works use data sources such as road sensors, induction loops, automatic vehicle identification systems, remote traffic microwave sensors, in-road reflectors, floating car data, and simulation [3,4,10,11,12,13]. Many traffic administrative departments [16,17] and online map service providers [18,19,20] provide real-time or near real-time online traffic congestion condition maps to the general public for free Such services use and integrate various data sources, for example induction loops and more generally location-based data originating from apps active on Global Positioning System (GPS)-enabled smartphones carried in running cars. This paper proposes a systematic method to collect and use this new kind of data source for traffic congestion forecasting

Methods

Results

Discussion

Conclusion