Replicating capacity and congestion in microscale agent-based simulations

Disaster events cause detrimental impacts for communities across the globe, ranging from large numbers of fatalities and injuries, to the loss of homes and devastating financial impacts. Emergency professionals are facedwith the challenge of providing sustainable solutions to mitigate these consequences and require tools to aid the assessment of potential impacts. Current modelling tools have either focused on modelling either the microscale (e.g. individual confined spaces such as buildings or stadiums) or the macroscale (e.g. city scale). The aim of thisresearch is to create microscale agent-based modelling (ABM) tools, incorporating a realistic representation of human behaviours, which will help management professionals assess and improve their contingency plans for emergency scenarios. The focus has been on creating a microscale agent-based model of a pedestrian pavement and crossroads, to include overtaking and giving way, alongside the inclusion of varied population characteristics. This research has found that by improving pedestrian interactions (e.g. overtaking and giving way interactions) on pavements and at crossroads more robust travel time estimates can be achieved. To produce more realistic behaviour traits, microscale models should consider: (1) varied walking speed (2) population density, (3) patience level and (4) an exit split percentage for crossroads. Comparisons to 1.34 m/s (3mph) models without additional variables show the travel times may be misrepresentative by up to 78% in pavements and 305% in crossroads for some population types. This has the potential to cause cascading effects such as a significant increase in fatalities or injuries as communities cannot reach safety in the anticipated time

A review of traffic models for wildland-urban interface wildfire evacuation

Recent years have seen an increased prevalence of wildfires, some of which has spread into the wildland-urban interface and lead to large-scale evacuations. Large-scale evacuations gives rise to both logistical and traffic related issues. To aid in the planning and execution of such evacuations reliable modelling tools to simulate evacuation traffic are needed. Today no traffic model exists which is dedicated only to simulate wildfire evacuation in the wildland/urban interface. The aim of this thesis is to identify benchmark characteristics needed in such a model and review 12 existing models, both traffic models and evacuation models, and their potential usefulness in WUI wildfire scenarios. The thesis concludes that some models can be tuned to represent aspects of a WUI fire evacuation and that future research should focus on integrating traffic modelling with modelling of fire/smoke spread and pedestrian movement

An Agent-Based Exploration of the Hurricane Forecast-Evacuation System Dynamics

In the mainland US, the hurricane-forecast-evacuation system is uncertain, dynamic, and complex. As a result, it is difficult to know whether to issue warnings, implement evacuation management strategies, or how to make forecasts more useful for evacuations. This dissertation helps address these needs, by holistically exploring the system’s complex dynamics from a new perspective. Specifically, by developing – and using – an empirically informed, agent-based modeling framework called FLEE (Forecasting Laboratory for Exploring the Evacuation-system). The framework represents the key, interwoven elements to hurricane evacuations: the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connections between systems (forecasts and warning information, traffic). The dissertation’s first article describes FLEE’s conceptualization, implementation, and validation, and presents proof-of-concept experiments illustrating its behaviors when key parameters are modified. In the second article, sensitivity analyses are conducted on FLEE to assess how evacuations change with evacuation management strategies and policies (public transportation, contraflow, evacuation order timing), evolving population characteristics (population growth, urbanization), and real and synthetic forecast scenarios impacting the Florida peninsula (Irma, Dorian, rapid-onset version of Irma). The third article begins to explore how forecast elements (e.g., track and intensity) contribute to evacuation success, and whether improved forecast accuracy over time translates to improved evacuations outcomes. In doing so, we demonstrate how coupled natural-human models – including agent-based models –can be a societally-relevant alternative to traditional metrics of forecast accuracy. Lastly, the fourth article contains a brief literature review of inequities in transportation access and their implication on evacuation modeling. Together, the articles demonstrate how modeling frameworks like FLEE are powerful tools capable of studying the hurricane-forecast-evacuation system across many real and hypothetical forecast-population-infrastructure scenarios. The research compliments, and builds-upon empirical work, and supports researchers, practitioners, and policy-makers in hazard risk management, meteorology, and related disciplines, thereby offering the promise of direct applications to mitigate hurricane losses

Evacuation planning under selfish evacuation routing

In case of an evacuation a large number of evacuees must be routed through a street network to let them leave the endangered area and reach safe places. In such a situation a lot of evacuees use the street network in a short time span and so the network capacity will be insufficient. With an evacuation plan the traffic could be guided through the network for a better use of network capacity. But to implement the solution planned by a central decision maker, optimal routes must be communicated to all network users, which lead to a high communication effort. Furthermore, it must be ensured that the evacuees take the given routes. But a lot of people do not follow the instructions from authorities in a panic situation. They do what they assume is best for themselves. Such selfish behaviour leads to a suboptimal distribution of traffic and results in congestion. In this thesis we present a concept to guide the evacuees through the network without determining optimal routes for all network users. With the blockage of street sections we force the evacuees to use other routes than the preferred ones but give them the possibility to choose their routes on their own. The thesis presents different mathematical model formulations and heuristic for the described problem. In a comprehensive computational study, with real world examples, the functionality of the presented concept and methods are tested