During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computingbased mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.

Due to the rapid development of communication and sensing technology, a large amount of mobile data is collected so that we can infer the complex movement laws of humans. For cities, some unusual events may endanger public safety. If the early warning of an abnormal event can be issued, it is of great application value to urban construction services. To detect urban anomalies, this paper proposes the Hierarchical Urban Anomaly Detection (HUAD) framework. The first step in this framework is to build rough anomaly characteristics that need to be calculated by some traffic flow consisted of subway and taxi data. In the second step, the alternative abnormal regions were obtained. Then, the long short-term memory (LSTM) network is used to predict the traffic to get the historical anomaly scores. Following that, the refined anomaly characteristics are generated from adjacent regions, adjacent periods and historical anomalies. The final abnormal regions were detected by One-Class Support Vector Machine (OC-SVM). At last, based on real data sets, we analyzes the traffic flow of the target region and adjacent regions from multiple perspectives in view of the large crowd gathering activities, and the effectiveness of the method is verified. INDEX TERMS Spatio-temporal data fusion, traffic flow prediction, urban anomaly detection. I. INTRODUCTION

The car-following model describes the microscopic behavior of the vehicle. However, the existing car-following models set the drivers’ reaction time to a fixed value without considering its dynamics. In order to improve the accuracy of car-following model, this paper proposes Deep Feature Learning-based Car-Following Model (DeepCF), a car-following model based on fatigue driving and Generative Adversarial Networks (GAN). The model is composed of the drivers’ reaction time model and the car-following decision algorithm. First, we regard driving fatigue as the starting point to study the influence of driving time and the acceleration of the preceding vehicle on the drivers’ reaction time, and develop a coarse-grained drivers’ reaction time model. Secondly, considering the impact of fatigue driving on car-following decisions, we utilize GAN to generate a driving decision database based on reaction time and use Euclidean distance as a decision search indicator. Finally, we conduct experiments on a real data set, and the results indicate that our DeepCF model is superior to baseline models.

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