With rising instances of extreme events and urban settlements, this paper outlines a pedestrian evacuation modeling framework using volunteered geographical information from OpenStreetMap and simplified queuing-network model to estimate evacuation time, detect bottlenecks and test different evacuation strategies. An example case of a total city wide evacuation is presented for a selection of UK cities with similar total road surface area. Evacuation times are presented for scenarios with and without intervention, where intervention implies that densities on roads are capped to enable maximum flow, highlighting the benefit of rapid evacuation time assessment to benchmark cities.

Estimating city evacuation time is a nontrivial problem due to the interaction between thousands of individual agents, giving rise to various collective phenomena, such as bottleneck formation, intermittent flow, and stop-and-go waves. We present a mean field approach to draw relationships between road network spatial attributes, the number of evacuees, and the resultant evacuation time estimate (ETE). Using volunteered geographic information, we divide 50 United Kingdom cities into a total of 704 catchment areas (CAs) which we define as an area where all agents share the same nearest exit node. 90% of the agents are within ≈6,847 m of CA exit nodes with ≈13,778 agents/CA. We establish a characteristic flow rate from catchment area attributes (population, distance to exit node, and exit node width) and a mean flow rate in a free-flow regime by simulating total evacuations using an agent based "queuing network" model. We use these variables to determine a relationship between catchment area attributes and resultant ETEs. This relationship could enable emergency planners to make a rapid appraisal of evacuation strategies and help support decisions in the run up to a crisis.

In recent years, we have seen a surge in the number of natural disasters (Munich, Loss events worldwide 2013. Rapid urbanisation and population growth are contributing factors. However, the planning tools available are usually specific to a region and incompatible in new areas. Therefore, aim of the overall project is to utilise growing wealth of crowd-sourced open spatial databases like OpenStreetMap (OSM) (Haklay and Weber, Pervasive Comput IEEE 7(4):12-18, 2008), computational mobility and behavioural models to achieve rapid simulation of large-scale evacuation effort in response to major crises. As part of an initial effort to gain insight into disaster resilience of various UK cities, 7 amenities across 11 cities have been studied. Correlations between population count (GPWv3) (Center for International Earth Science Information Network (CIESIN)/Columbia University and Centro Internacional de Agricultura Tropical (CIAT), Gridded Population of the World, Version 3 (GPWv3): Population Density Grid, 2005) and number of critical amenities that have the potential to suffer increase in demand during a crisis have been looked at. Similarly, correlations between pairs of potentially interdependent population weighted amenities have also been investigated by working with the assumption that if they are spatially well correlated, they can work better. As the work is ongoing, a worldwide geographically specific 'EvacuationFriendliness Index' is envisioned at the end of this project. As the research focus expands take suitability of road networks for emergency evacuation and dynamic effects using agents based models, the outcome is expected to have implication on emergency planning in the short term by testing multiple strategies in the run up to a disaster and influence policy makers in the long term by identifying weakest links and bottlenecks in a city system.

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