Korea initiated a new experiment, called a dynamic response system for open democratic societies as a principle to respond to the novel coronavirus (COVID-19). The global pandemic of COVID-19 led to a surge in demand for healthcare medical masks and respirators, and strained the global supply chain of mask production and distribution systems. This study provides a systemic view of critical personal protective equipment for both healthcare staff and the public to stop the spread of COVID-19. This study investigates the dynamic response system of healthcare mask production to the coronavirus and discusses lessons learned in view of systems thinking. The study shows that it is critical to developing a quick and dynamic response system to the evolving market conditions with flexible and agile operations. Visibility with transparency with information sharing with the public is also critical under global pandemic. Due to the shortage of mask supply, smart consumption is required along with collaboration with public and private sectors, as well as global organizations. Democratic leadership and a well-prepared strategic plan for long-term period are essential to the open society to prepare the global pandemic in the future. This study serves as a benchmark for dynamic and timely responses to the global pandemic.
This paper proposes a novel approach to analyze potential accessibility to ambulance services by combining the demand-covered-ratio and potential serviceability with the ambulance-covering-ratio. A Geographic Information System (GIS)-based spatial analysis will assist ambulance service planners and designers to assess and provide rational service coverage based on simulated random incidents. The proposed analytical model is compared to the gravity-based two-step floating catchment area method. The study found that the proposed model could efficiently identify under-covered and overlapped ambulance service coverage to improve service quality, timeliness, and efficiency. The spatial accessibility and serviceability identified with geospatial random events show that the model is able to plan rational ambulance service coverage in consideration of households and travel time. The model can be applied to both regional and statewide coverage plans to aid the interpretation of those plans.
Abstract:To elucidate a realistic traffic assignment scenario, a multi-criterion decision system is essential. A traffic assignment model designed to simulate real-life situation may therefore utilize absolute and/or relative impedance. Ideally, the decision-making process should identify a set of traffic impedances (factors working against the smooth flow of traffic) along with pertinent parameters in order for the decision system to select the most optimal or the least-impeded route. In this study, we developed geospatial algorithms that consider multiple impedances. The impedances utilized in this study included, traffic patterns, capacity and congestion. The attributes of the decision-making process also prioritize multi-traffic scenarios by adopting first-in-first-out prioritization method. We also further subdivided classical impedance into either relative impedance or absolute impedance. The main advantage of this innovative multi-attribute, impedance-based trip assignment model is that it can be implemented in a manner of algebraic approach to utilize shortest path algorithm embedded in a Geographic Information Systems (GIS)-Graphical User Interface tool. Thus, the GIS package can therefore handle the multi-attribute impedance effectively. Furthermore, the method utilized in this paper displays flexibility and better adaptation to a multi-modal transportation system. Transportation, logistics, and random events, such as terrorism, can be easily analyzed with pertinent impedance.
The member states of International Maritime Organization (IMO) have been leading in and enforcing the use of automatic identification systems (AIS) in the analysis of ship-to-ship collisions, vessel monitoring, and maritime traffic management offshore. This study will help non-federal stakeholders understand the AIS data and contribute to future research by assessing difficulties and improving access to data and applications. This study introduces the basics of AIS materials, shared channels, and currently developed applications, and discusses areas where they can be incorporated in the future. The literature revealed that using AIS data will be beneficial to the public as well as to business and public agencies.IMO has been leading in and enforcing the use of AIS in ship-to-ship collisions, vessel monitoring, and maritime traffic management offshore. U.S. federal agencies actively access nationwide AIS (NAIS) information to perform a variety of functions, including security, safety, and policy-making.However, access to the data needed by non-federal stakeholders, including in the marine industry and academic fields, is still limited; AIS data are not very well known, and related research and use is at the preliminary stage. However, if the importance and usefulness of AIS information were known, stakeholders could make rapid progress in the use of the data and its related applications in the near future. Therefore, this study aims to help non-federal stakeholders understand the AIS data and support future research by assessing difficulties and improving access to data and its applications.This study introduces the basics of AIS materials, shared channels, and currently developed applications, and discusses areas where they can be incorporated in the future. It examines the existing literature relating to AIS data and its derived products of application. The study is structured as follows: first, this study discusses the background and introduces the status of the data in Section 2. Data scientists and information engineers understand the need for data and determine the right information to collect. Because data consumes memory and storage space, it can be expensive depending on the size of the data. This is discussed in Section 3, on the nature of the data and the policy of collecting, using, and sharing data. Views of data science, information management, and governance are described in Section 4. This section describes the methods and tools for collecting, storing, distributing, and visualizing AIS data. Section 5 explores the evolution and development of applications using AIS. Finally, in the concluding section, the research is summarized, with suggestions to increase the accessibility and utilization of AIS data.
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