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.

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.

Safety at highway rail grade crossings (HRCs) continues to be a serious concern despite improved safety practices. Accident frequencies remain high despite increasing emphasis on HRCs safety. Consequently, there is a need to re-examine both the design practices and the safety evaluation methods at HRCs. Previous studies developed accident prediction models by incorporating highway, crossing inventory, rail, and vehicle traffic characteristics, but none of these factors considered population in the vicinity of HRCs. This study developed a binary logit regression model to predict accident likelihood at HRCs by incorporating various contributory factors in addition to population (based on census blocks 2010) within five miles of crossings. Previous North Dakota accident data from 2000 to 2016 was analyzed and used in the model development. The model results show that the number of daily trains, the maximum typical train speed, the number of through railroad tracks, and the number of highway/traffic lanes all affect accident likelihood. The presence of pavement markings in the form of stop lines helps reduce accident probability, while populations within five miles of HRCs have a positive relationship with crash likelihood. This study will help transportation agencies improve HRC safety.

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.