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

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.

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

“…Then, we use these network-only images containing road segments marked with congestion levels to build a dataset for traffic congestion research. The congestion level given by the color of each pixel inside images like Figure 1b is linearly converted to a normalized value based on its color (transparent, green, yellow, red, or dark red) to form an original traffic congestion matrix P t [30]. Specifically, transparent pixels are converted to 0.0, green ones to 0.25, yellow ones to 0.5, red ones to 0.75, and dark red one to 1.0, because traffic congestion levels are categorized by the online service provider based on the calculated linear travel time index.…”

“…Duan et al segmented an urban area of Xi'an into 16 × 16 grids and summed the number of trips from a certain origin to a certain destination when predicting the number of such trips [29]. Zhang et al divided a metropolitan freeway transportation network in Seattle into grids and calculated the average congestion level for each grid when predicting traffic congestion in that area [30]. However, these operations are often selected without further consideration, and specifically, there has rarely been an evaluation of their impacts regarding the prediction of traffic flow variables.…”

TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction

“…It was found that the proposed approach outperformed other existing models. In [22], the authors proposed e a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder which was able to learn temporal correlations of a transportation network and predicting traffic flow. Their architecture outperformed all their reviewed works.…”

“…Stacked denoising autoencoders for traffic flow prediction are adapted to learn network-wide relationships, these are necessary to estimate missing traffic flow data, and thus predict the future traffic value as a missing point with respect to the input data. SAE networks have been used in [19,22,23]. Figure 2 reports the SAE architecture used in this work to perform tests and comparisons.…”

Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach

“…In previous literature, different approaches have been utilized for short-term travel speed prediction including time series analysis methods [14,15], statistical regressions [16], artificial neural (NN) [17,18], and support vector regression (SVR) methods [19]. Although, time series and statistical methods have good theoretical interpretations, these methods have been frequently questioned regarding prediction performance.…”