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With the continuous enrichment of traffic Internet-of-Things data acquisition methods, more and more spatiotemporal data on road networks is collected in real time by various sensors and multimedia devices. The data-driven deep learning approach can make full use of real-time data from a road network to predict future traffic status. By mining the spatiotemporal relationships between road units, the ability to predict network evolutionary behaviors is improved, which provides a new method of traffic management. There are strong semantic relations between road intersections or road sections in terms of traffic evolution. Modeling the network only from a shallow spatial topological perspective ignores the important intrinsic association of the dynamic network. In this paper, we propose a semantic associative neural network (SANN) for traffic evolution analysis by modeling the propagation effects and similarity patterns between road units. Considering the inadequacy of the fixed adjacent matrix, graph convolution is used to encode the semantic features of a road network and embed them in a bidirectional recurrent neural network for sequence prediction. Finally, the experiments are conducted based on speed data sets to prove the effectiveness of the proposed method. The model achieved a well-predicted accuracy of 95.33% and 84.08% on Pems-Bay and Los Angeles data sets. [ABSTRACT FROM AUTHOR]

Smart cities have been widely investigated, and several algorithms, techniques, and protocols have recently been developed to serve smart city environments. Intelligent traffic management is one of the main research fields in this area. It aims to control and monitor the traffic over downtown and highway scenarios. The real-time traffic characteristics on road networks highly affect safety conditions and driving behaviors. Detecting hazardous traffic conditions and congested areas early helps drivers make the best decision to avoid dangerous situations. After extensive training, machine learning algorithms are mainly used for prediction in this field. In the intelligent traffic control target, trained machines are beneficial in obtaining instant and accurate predictions regarding traffic congestion levels and their distributions. Several parameters control the efficiency of the selected machines. This includes the dataset quality, the regression algorithm's parameters, and how they have been tuned. In this work, we first create high-quality datasets representing the moving vehicles for different main roads in various countries, including Bank St in Canada, North Rodeo Drive (NRD) in Los Angeles, USA, Queen Rania Al Abdullah (QRA) road in Jordan, and Sheikh Zayed Road (SZR) in Saudi Arabia. Then, we test the most popular regression algorithms and evaluate their performance in terms of temporal prediction of traffic characteristics and traffic congestion in different geographical road scenarios. We optimized these regression algorithms and tuned the used parameters using the grid search approach. After that, more advanced metaheuristic optimization algorithms were used to improve the prediction accuracy of the selected machine. Experimentally, we have proved the enhancements in predicting traffic characteristics for the tested road scenarios. Both approaches show enhanced results compared to using the regression algorithm without tuning the parameters. It shows advanced results for both approaches for the coefficient of determination measure, named R squared (R 2), with approximately 0.07, 0.04, and 0.08 enhanced values for Bank St., NRD, and SZR roads, respectively. In contrast, it shows approximately 0.95, 0.49, and 3.62 enhanced values for Bank St., NRD, and SZR roads, respectively, for the Root Mean Squared Error (R M S E) measure. [ABSTRACT FROM AUTHOR]

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users’ devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall. [ABSTRACT FROM AUTHOR]

Traffic prediction plays a crucial role in an intelligent transportation system (ITS) for enabling advanced transportation management and services. In this paper, we address the problem of multi-step traffic speed prediction, including both short- and long-term predictions. We assert that it is important to consider not just the fixed spatial dependency of the road network (i.e., the connections between road segments) but also the dynamic spatial dependency of traffic within the static topology that intertwines with the temporal evolution of traffic condition across the entire network. We propose a novel deep learning model, named Self-Attention Graph Convolutional Network with Spatial, Sub-spatial and Temporal blocks (SAGCN-SST) model, that specifically capture such complex dynamic spatial–temporal processes. In SAGCN-SST, we integrate self-attention mechanism into graph convolutional networks in a novel framework design while using a sequence-to-sequence model in an encoder–decoder architecture for extracting long-temporal dependency of traffic speed. Two real-world datasets with frequent traffic congestion and accidents from large-scale road networks (i.e., Seattle and Los Angeles) are used to train and test our model. Our experiment results indicate that the proposed deep learning model consistently achieves the most accurate predictions (higher than 98% accuracy on both datasets for the short- and long-term predictions) when compared against well-known existing models in recent literature. The results also indicate that SAGCN-SST is robust against emergent traffic situations. • Novel traffic deep learning prediction model on large-scale road networks. • Graph convolution network and attention mechanism for spatial feature analysis. • Sequence-to-sequence architecture for temporal feature analysis. • Result validated using real-world traffic datasets. [ABSTRACT FROM AUTHOR]