Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction

Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffic flows and short-term prediction of congestion occurrence due to rush-hour or incidents can be beneficial to such systems to effectively manage and direct the traffic to the most appropriate detours. Many of the current traffic flow prediction systems are designed by utilizing a central processing component where the prediction is carried out through aggregation of the information gathered from all measuring stations. However, centralized systems are not scalable and fail provide real-time feedback to the system whereas in a decentralized scheme, each node is responsible to predict its own short-term congestion based on the local current measurements in neighboring nodes. We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in real-time based on the congestion state of the neighboring stations. Moreover, historical data from the deployment site is not required, which makes the proposed method more suitable for newly installed stations. In order to achieve higher performance, we introduce a regularized Euclidean loss function that favors high congestion samples over low congestion samples to avoid the impact of the unbalanced training dataset. A novel dataset for this purpose is designed based on the traffic data obtained from traffic control stations in northern California. Extensive experiments conducted on the designed benchmark reflect a successful congestion prediction

An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network

Traffic flow prediction is an important part of intelligent transportation systems. In recent years, most methods have considered only the feature relationships of spatial dimensions of traffic flow data, and ignored the feature fusion of spatial and temporal aspects. Traffic flow has the features of periodicity, nonlinearity and complexity. There are many relatively isolated points in the nodes of traffic flow, resulting in the features usually being accompanied by high-frequency noise. The previous methods directly used the graph convolution network for feature extraction. A polynomial approximation graph convolution network is essentially a convolution operation to enhance the weight of high-frequency signals, which lead to excessive high-frequency noise and reduce prediction accuracy to a certain extent. In this paper, a deep learning framework is proposed for a causal gated low-pass graph convolution neural network (CGLGCN) for traffic flow prediction. The full convolution structure adopted by the causal convolution gated linear unit (C-GLU) extracts the time features of traffic flow to avoid the problem of long running time associated with recursive networks. The reduction of running parameters and running time greatly improved the efficiency of the model. The new graph convolution neural network with self-designed low-pass filter was able to extract spatial features, enhance the weight of low-frequency signal features, suppress the influence of high-frequency noise, extract the spatial features of each node more comprehensively, and improve the prediction accuracy of the framework. Several experiments were carried out on two real-world real data sets. Compared with the existing models, our model achieved better results for short-term and long-term prediction.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators

Tunnel Traffic Forecasting Using Deep Learning

Tunnel traffic congestion can increase the risk of traffic accidents, tunnel fires, and environmental effect. Despite numerous studies on traffic forecasting using deep learning, research on tunnel traffic remains limited.Utilizing traffic flow data from the Norwegian Public Road Administration, this thesis analyzes the applicability of recurrent neural networks for tunnel traffic prediction. The data is retrieved from different sources and traffic sensors near or inside the tunnels are selected through a geo-spatial analysis. The recurrent neural network is designed to be trained on either a single tunnel or several tunnels. Furthermore, based on their geographical location and population density, the tunnels are classified as urban or sub-urban. Based on the results of the experiments and the sample of tunnels used,the recurrent neural network outperformed the baseline for urban tunnels in terms of root-mean-squared-error. However, the performance advantage was not significant for sub-urban tunnels. The addition of features such as temporal features and category features provided no significant results.These findings are discussed in the final sections of the thesis