Distributed agent-based building evacuation simulator

The optimisation of the evacuation of a building plays a fundamental role in emergency situations. The behaviour of individuals, the directions that civilians receive, and the actions of the emergency personnel, will affect the success of the operation. We describe a simulation system that represents the individual, intelligent, and interacting agents that cooperate and compete while evacuating the building. The system also takes into account detailed information about the building and the sensory capabilities that it may contain. Since the level of detail represented in such a simulation can lead to computational needs that grow at least as a polynomial function of the number of the simulated agents, we propose an agent-oriented Distributed Building Evacuation Simulator (DBES). The DBES is integrated with a wireless sensor network which offers a closed loop representation of the evacuation procedure, including the sensed data and the emergency decision making

Simplified adaptive routing and its impact on quality of service and quality of information

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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During emergency response situations, decisions have to be made in a timely manner. Multiple entities have to be optimally coordinated and numerous resources must be allocated efficiently, creating a very interesting and challenging technical problem. In this paper we present a simulation system that models the evacuation of a multi-storey building. Autonomous intelligent agents are used to represent various types of actors that interact inside a virtual physical world. We also model virtual hazards, such as fire, that spread inside the building evacuation simulator. A real wireless sensor network is used to monitor the spread of the hazards while an external event generator provides input to the sensors. We study the effect of different disaster scenarios and agent behaviours, such as human behaviour during an emergency, on the result of the evacuation procedure. Our initial results indicate that the safety of the evacuees and the evacuation time depend on local interactions between the participants and are affected by the actors’ decisions. The integration with the wireless sensor network gives us the opportunity to investigate the effect of sensed information on resource allocation and allows us to study the impact of network issues on the decision making process

Using demand mapping to assess the benefits of urban green and blue space in cities from four continents

The benefits of urban green and blue infrastructure (UGI) are widely discussed, but rarely take into account local conditions or contexts. Although assessments increasingly consider the demand for the ecosystem services that UGI provides, they tend to only map the spatial pattern of pressures such as heat, or air pollution, and lack a wider understanding of where the beneficiaries are located and who will benefit most. We assess UGI in five cities from four continents with contrasting climate, socio-political context, and size. For three example services (air pollution removal, heat mitigation, accessible greenspace), we run an assessment that takes into account spatial patterns in the socio-economic demand for ecosystem services and develops metrics that reflect local context, drawing on the principles of vulnerability assessment. Despite similar overall levels of UGI (from 35 to 50% of urban footprint), the amount of service provided differs substantially between cities. Aggregate cooling ranged from 0.44 °C (Leicester) to 0.98 °C (Medellin), while pollution removal ranged from 488 kg PM2.5/yr (Zomba) to 48,400 kg PM2.5/yr (Dhaka). Percentage population with access to nearby greenspace ranged from 82% (Dhaka) to 100% (Zomba). The spatial patterns of pressure, of ecosystem service, and of maximum benefit within a city do not necessarily match, and this has implications for planning optimum locations for UGI in cities