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On-board data of detected vehicles play a critical role in the management of urban road traffic operation and the estimation of traffic status. Unfortunately, due to limitations of technology and privacy issues, the sampling frequency of the detected vehicle data is low and the coverage is also limited. Continuous vehicle trajectories cannot be obtained. To overcome the above problems, this paper proposes an unscented Kalman filter (UKF)-based method to reconstruct the trajectories at signalized intersections using sparse probe data of vehicles. We first divide the intersection into multiple road sections and use a quadratic programming problem to estimate the travel time of each section. The weight of each initial possible trajectory is calculated separately, and the trajectory is updated using the unscented Kalman filter (UKF); then, the trajectory between two updates is also obtained accordingly. Finally, the method is applied to the actual scenario provided by the NGSIM data and compared with the real trajectory. The mean absolute error (MAE) is adopted to evaluate the accuracy of the proposed trajectory reconstruction. Sensitivity analysis is provided in order to provide the requirement of sampling frequency to obtain highly accurate reconstructed vehicle trajectories under this method. The results demonstrate the applicability of the technique to the signalized intersection. Therefore, the method enables us to obtain richer and more accurate trajectory data information, providing a strong prior basis for future urban road traffic management and scholars using trajectory data for various studies.

The study focused on the preventive effects of the chain management model on pressure ulcers in the operating room. Sqoop big data collection module is used to collect patient information from various hospital information systems in a distributed manner. The data were from the clinical data center of the Zhongshan Hospital Xiamen University General Hospital, and 268 patients were selected as the research subjects. A chain management model is constructed, concerning the preventive measures, the management of each link, the perioperative pressure ulcer management, and the reporting of pressure ulcers. Then, the two groups were compared for the SAS and SDS scores before and after nursing, the pressure ulcer sites, pressure ulcer reporting rate, pressure ulcer staging, and nursing satisfaction. The results show that it is not that more collection modules will lead to better cluster performance and that the execution delay is caused by MapReduce requiring the JAVA virtual machine, and after reaching a certain point, the increase in the number of tasks will slow down the process, and as data size increases, DataNote has an expanded capability to analyze data. After nursing treatment, the SAS and SDS scores of the two groups of patients were significantly lower than before treatment ( P < 0.05 ). The pressure ulcers were mainly distributed in the forehead, mandible, cheeks, front chest, and knees in the two groups, and the difference between the two groups was statistically significant ( P < 0.05 ). The total satisfaction of the observation group was 93.28%, and the total satisfaction of the control group was 92.54%. The patients’ satisfaction with the chain management model was higher than that of conventional nursing.

COVID-19 has swept the world for more than one year. Public transport, especially railway passenger transport for long distance and extensive areas, is the most potential risk source of the pandemic. In railway passenger transport, real-name train ticket check can effectively track the propagation path of the pandemic, which is of most significance for spread prevention of COVID-19. In this study, the evaluation index system of real-name check of ticket in trains was first established, in which 22 indexes in 4 levels were included, and their weights were determined by the Method of Kantiray Weighting. Furthermore, a comprehensive evaluation model of real-name system check was proposed, in which normalized work of different indexes was done, and key factors affecting the evaluation were founded. Finally, typical data of 8 trains check were collected, and from the evaluation analysis, we can find out that most factors affecting results of real-name ticket check are attendant management, train organization level, train service, and real-name ticket check proportion, based on which corresponding countermeasures and suggestions are given oriented to short-, medium-, and long-distance trains, respectively, all of which can reduce the risk of pandemic propagation in railway system. [ABSTRACT FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)