A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Abstract

Deep learning-based techniques are the state of the art in road traffic prediction or forecasting. Several deep neural networks have been proposed to predict the traffic but they have not been evaluated under common datasets. Current studies analyze their capacity to predict road traffic in general but do not focus on their capacity to predict the formation of congestions. This is critical for avoiding congestions or mitigate their negative impact. This paper progresses the current state of the art by presenting a comprehensive comparison of the state-of-the-art deep neural networks for road traffic prediction. The comparison is conducted using the same real traffic datasets, and under normal and congested traffic conditions. The evaluation includes new deep neural networks and error recurrent models. Our study first demonstrates that accurately predicting the traffic overall does not imply that a deep neural network can accurately predict the traffic when congestions are being formed. This reinforces the idea that prediction techniques must also be evaluated under congestion conditions. Our analysis also shows that exploiting the spatiotemporal evolution of the traffic (and not just the temporal one) provides better prediction accuracy overall and in particular under congestion conditions. The study also demonstrates that error recurrent models outperform deep neural networks that do not utilize an error feedback both under normal and congested traffic conditions. In particular, our study shows that the error recurrent model eRCNN is the deep learning technique that achieves to date the best traffic prediction accuracy. It is also important emphasizing that error recurrent models achieve better prediction accuracy with shallower neural networks and therefore lower computational cost.

On the Impact of Floating Car Data and Data Fusion on the Prediction of the Traffic Density, Flow and Speed Using an Error Recurrent Convolutional Neural Network

“…The architecture of the eRCNN is depicted in Figure 3. We demonstrated in [31] that this architecture and the design of the eRCNN is the optimum one for predicting the traffic under normal traffic conditions and under traffic congestion. All the eRCNNs used in this work are trained using the backpropagation through time algorithm (BPTT) and the ADAM algorithm [33], which is a v Q ρ…”

“…In this study, we predict the traffic variables using an error recurrent convolutional neural network (eRCNN) [18], since we previously demonstrated that this network achieves the best traffic predictions using data from fixed traffic detectors under general traffic conditions and under traffic congestion [31]. The eRCNN model takes as input the estimates of the three fundamental traffic variables.…”

Research on Traffic Situation Analysis for Urban Road Network Through Spatiotemporal Data Mining: A Case Study of Xi’an, China

“…Due to traffic congestion, the most influential cities in China suffer economic losses of $1 billion every year [3]. The European Commission calculates that the annual cost related to traffic congestion is about 100 billion euros (1% of GDP) [4]. The traffic congestion raises gas consumption, aggravates environmental pollution, and raises residents' travel time [5].…”

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

“…Scholars improved the SVM algorithm and obtained its improved model, such as support vector regression (SVR) [22], least-squares support vector machine (LSSVM) [23,24], and least-squares support vector regression (LSSVR) [25]. In recent years, inspired by neural networks, new technologies such as deep neural networks and deep learning have been developing rapidly, and traffic flow prediction technologies are also constantly updated and improved [26][27][28].…”

“…The purpose is to capture the target, that is, to complete the GWO. According to Equation(28), the decrease in the value of→ a will cause the value of → A to fluctuate accordingly and → A ∈ [−1, 1].…”