Description:
During and after a disaster, affected communities grapple with how to respond and make sense of the experience. The physical and mental health of individuals is often adversely affected, as is the well being of the community. In early 2014, a fire in the Morwell open cut coalmine adjacent to the Hazelwood power station in the Latrobe Valley, Victoria burned for approximately 45 days, shrouding surrounding communities in smoke. As authorities struggled to put out the fire, the nearby communities became increasingly concerned about the perceived health risks of exposure to the smoke, particulate matter and gas emissions from the burning coal. The Hazelwood mine fire, initially treated as a fire emergency, 'evolved into a chronic technological disaster and a significant and lengthy environmental and health crisis' (Government of Victoria 2014, p. 28). In response to the crisis, people turned to social media as an alternative space in which to share information, tell their stories and organise for the purpose of activism. This paper takes the Hazelwood mine fire as a case study to examine how a community used social media (specifically Facebook) during a complex technological crisis involving health effects. It examines the issues facing emergency organisations and communities in relation to information and trust, and identifies the strengths and pitfalls of social media use in relation to community empowerment and engagement.
Description:
We seek to engineer adaptive coordination between agents working in and across dynamic organizations in a complex, distributed setting. Guided by predefined social policies, agents can create social commitments at run time to achieve coordination of knowledge and behaviour. We demonstrate coordination requirements by providing example policies, drawing on the need for knowledge cultivation in an emergency management scenario.
Description:
This paper describes the progress of our ongoing research into collaborative report writing for emergency management using web technologies to improve communication and shared knowledge in emergency situations. Specifically, the work presented in this paper focuses on the development of a user attribution framework that enhances the Differential Synchronisation (diffsync) technique by exploiting its diff operation. This technique improves real-time collaborative editing for emergency management by combining the benefits of user attribution with diffsync features such as convergence, scalability, and robustness to poor network environments. As a proof of concept, we implement a prototype collaborative system and report results of simulations to test scalability, efficiency, and correctness. Further, we consider the potential benefits of this framework for web-based collaborative report writing in the context of emergency management.
Description:
Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.
Description:
Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.