This research deals with the case of a smart intersection, where several cars approach the intersection from various directions, and a smart traffic light must decide about the time intervals of RED and GREEN in each direction, based not only on the number of vehicles in each lane, but also on other factors such as the type of vehicles (e.g. emergency vehicles), and the social characteristics of the passengers (e.g. a handicapped person, a student who is late for an exam). Those factors will be gleaned from the IoT (Internet of Things) network amongst cars, traffic lights, individuals, municipality data, and more. Once those priorities have been examined, they are fed into the algorithm we have devised, and outputted as a timing schedule for the different sides of the intersection. In this paper we present the algorithm, the prioritizing research, its implementation in the algorithm and initial results.
Background: Accuracy of electrocardiogram (ECG) interpretation is important for identification of ST-elevation myocardial infarction (STEMI) by Emergency Medical Services (EMS) personnel who recognize STEMI in the field and activate the coronary catheterization laboratory. According to previous research, there is improvement in diagnosis of STEMIs for healthcare providers who read an average of > 20 ECGs per week. This study evaluated the effectiveness of online ECG modules on improving diagnostic accuracy. Methods: EMS personnel received 25 ECGs per week to interpret via an online program. Diagnostic accuracy was assessed for improvement via completion of an ECG evaluation package before and after the intervention. Job satisfaction data were collected to determine the impact of the educational initiative. Results: A total of 64 participants completed the study. Overall, there was an improvement in ECG diagnostic accuracy from 50.8% to 61.2% (95% confidence interval [CI], 7.7-13.2; P < 0.0001). Specifically, there was significant improvement in the diagnosis of STEMI (8.5%; 95% CI, 4.9-12.3; P < 0.003) and supraventricular tachycardia (39.0%; R ESUM E
Smart devices and their connections to the Internet of Things (IoT) have been the subject of many papers in the past decade. In the context of IoT in transportation, one feature is the smart junction. This research deals with this junction, where several cars approach the intersection from different directions, and a smart traffic light must decide regarding the time intervals of red and green light in each direction. Out novel approach is based not only on the number of vehicles in each lane, but also on the social characteristics of the passengers (e.g. a handicapped person, a driver with no previous traffic violations). These factors will be gleaned from IoT network sources on cars, traffic lights, individuals, municipality data, and more. In this paper, we suggest using a VCG (Vickrey-Clarke-Groves) auction mechanism for the intersection scheduling, combining the social characteristic with a benefit parameter that expresses the passenger’s subjective perception of the importance of crossing the intersection as soon as possible. Our simulation results show the efficiency of the suggested protocol and demonstrate how the intersection scheduling depends on the passengers’ preferences, as well as on their social priorities.
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