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Recently, with the rapid development of deep learning models, traffic analysis using image datasets recently has attracted more attention. Specifically, the network traffic can be represented to images as the input for deep learning models to provide various applications (e.g. Spatio-Temporal traffic forecasting). In this study, we propose a new image generation approach for traffic density classification in terms of large-scale road network. Particularly, traffic volume and speed are at certain areas able to be measured by using surveillance systems (e.g. loop detectors). However, measuring the density is difficult which depends on the spatial correlation from the perspective of the network. Consequently, an effective image generation approach, based on information arrival and departure time of vehicles, is proposed to deal with this problem. Regarding the experiment, traffic density classification using a convolutional neural network is executed on roadside equipment data of 11 continuous intersections for evaluating the effectiveness of the proposed approach.

Traffic analysis using computer vision techniques is attracting more attention for the development of intelligent transportation systems. Consequently, counting traffic volume based on the CCTV system is one of the main applications. However, this issue is still a challenging task, especially in the case of complex areas that involve many vehicle movements. This study performs an investigation of how to improve video-based vehicle counting for traffic analysis. Specifically, we propose a comprehensive framework with multiple classes and movements for vehicle counting. In particular, we first adopt state-of-the-art deep learning methods for vehicle detection and tracking. Then, an appropriate trajectory approach for monitoring the movements of vehicles using distinguished regions tracking is presented in order to improve the performance of the counting. Regarding the experiment, we collect and pre-process the CCTV data at a complex intersection to evaluate our proposed framework. In particular, the implementation indicates the promising results of our proposed method, which achieve accuracy around 80% to 98% for different movements for a very complex scenario with only a single view of the camera.

Traffic forecasting plays an important role of modern Intelligent Transportation Systems (ITS). With the recent rapid advancement in deep learning, graph neural networks (GNNs) have become an emerging research issue for improving the traffic forecasting problem. Specifically, one of the main types of GNNs is the spatial-temporal GNN (ST-GNN), which has been applied to various time-series forecasting applications. This study aims to provide an overview of recent ST-GNN models for traffic forecasting. Particularly, we propose a new taxonomy of ST-GNN by dividing existing models into four approaches such as graph convolutional recurrent neural network, fully graph convolutional network, graph multi-attention network, and self-learning graph structure. Sequentially, we present experimental results based on the reconstruction of representative models using selected benchmark datasets to evaluate the main contributions of the key components in each type of ST-GNN. Finally, we discuss several open research issues for further investigations. [ABSTRACT FROM AUTHOR]

Recently, with the rapid development of deep learning models, traffic analysis using image datasets recently has attracted more attention. Specifically, the network traffic can be represented to images as the input for deep learning models to provide various applications (e.g. Spatio-Temporal traffic forecasting). In this study, we propose a new image generation approach for traffic density classification in terms of large-scale road network. Particularly, traffic volume and speed are at certain areas able to be measured by using surveillance systems (e.g. loop detectors). However, measuring the density is difficult which depends on the spatial correlation from the perspective of the network. Consequently, an effective image generation approach, based on information arrival and departure time of vehicles, is proposed to deal with this problem. Regarding the experiment, traffic density classification using a convolutional neural network is executed on roadside equipment data of 11 continuous intersections for evaluating the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Traffic analysis using computer vision techniques is attracting more attention for the development of intelligent transportation systems. Consequently, counting traffic volume based on the CCTV system is one of the main applications. However, this issue is still a challenging task, especially in the case of complex areas that involve many vehicle movements. This study performs an investigation of how to improve video-based vehicle counting for traffic analysis. Specifically, we propose a comprehensive framework with multiple classes and movements for vehicle counting. In particular, we first adopt state-of-the-art deep learning methods for vehicle detection and tracking. Then, an appropriate trajectory approach for monitoring the movements of vehicles using distinguished regions tracking is presented in order to improve the performance of the counting. Regarding the experiment, we collect and pre-process the CCTV data at a complex intersection to evaluate our proposed framework. In particular, the implementation indicates the promising results of our proposed method, which achieve accuracy around 80% to 98% for different movements for a very complex scenario with only a single view of the camera. [ABSTRACT FROM AUTHOR]