Understanding and Managing Wildfire Risks to Residential Communities and Supply Chain Networks

Wildfire has become an increasing threat to humans, the built environment, and ecosystems in the United States. Several factors contribute to such an increase in wildfire risk, including climate change, rapid population growth and infrastructure development at the wildland-urban interface, and accumulated fuels from past wildfire management practices. Increases in wildfire activity have resulted in substantial human and economic losses in the past decade. For example, the 2023 Hawaii wildfires razed more than 2,200 homes and businesses while tragically claiming the lives of at least 115 individuals. A series of California wildfires in 2015, 2017, and 2018 resulted in direct economic losses of $4.8 billion,$18 billion, and $26.3 billion, respectively. In addition to the direct economic losses, the 2018 California wildfires disrupted many supply chain systems, which propagated across the regional economy and induced$88.6 billion in direct losses. These recent wildfires have underscored the urgent need for understanding, assessing, and managing wildfire risks to residential communities and supply chains. To this end, this dissertation aims at understanding and managing wildfire risks to humans, properties, and the regional economy, with a particular focus on residential communities and supply chain networks. To advance our understanding of various proactive and emergency activities, this dissertation begins by examining homeowners’ decisions on wildfire-related proactive actions, such as home hardening, vegetation treatment, and homeowners insurance, through an online survey and subsequently assesses the effect of these actions on the process of housing recovery. Next, this dissertation shifts its focus towards individual behaviors during wildfire events, encompassing their preferences and decisions made during wildfire evacuations. This entails the study of factors like evacuation triggers and timing, as well as a series of en-route decisions made by residents in wildfire-prone areas, all gathered through an online survey. Based on the survey results, data-driven models are developed for predicting evacuees’ behaviors during wildfires. Furthermore, this dissertation integrates these data-driven predictive models with wildfire simulations, vulnerability assessment, and traffic simulation to construct a comprehensive agent-based modeling (ABM) framework for wildfire evacuations under damaged transportation settings. The framework is designed to simulate traffic conditions during a wildfire evacuation and identifies the critical parts of the transportation network for pre-fire risk mitigation actions aimed at improving mobility during a wildfire evacuation.To assess wildfire risk to a supply chain network, this dissertation also proposes a probabilistic wildfire risk assessment framework. It provides rigorous probabilistic descriptions of wildfire ignition likelihood and growth, interaction between supply chain components and wildfire, consequent component damage, and network-level performance reduction. Then, a hypothetical forest-residuals-to-sustainable-aviation-fuel supply chain network is utilized as an illustrative example to demonstrate the capability and applicability of the proposed framework. The proposed framework can be used as a planning tool to evaluate network performance subject to a set of what-if scenarios and the effect of pre- and post-wildfire risk mitigation measures.Overall, this dissertation provides valuable insights for understanding the inherent drivers of individual’s preference on both wildfire proactive actions and evacuation decisions. This information can serve as a foundation for increasing community resilience by helping policymakers and stakeholders to increase participation rates in proactive actions and the responsiveness to evacuation orders. Moreover, the simulation tools and quantitative frameworks developed in this dissertation provide valuable support for stakeholders and policymakers in forecasting post-wildfire performance and implementing more effective pre-event mitigation strategies. These adaptable tools and frameworks show potential for broader applications across various domains, including water distribution networks, transportation systems, and electric power grids, making them valuable assets in addressing the complex challenges posed by dynamic and interconnected systems

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Tsunami evacuation model for Sumner, Christchurch, New Zealand

Sumner, a coastal suburb located to the south-east of Christchurch, New Zealand, is highly exposed to a number of tsunami hazards. In tsunami mitigation plans, evacuation plays a crucial role in saving human lives, especially for communities located in low-lying coastal areas. The aim of this thesis is to enhance the methodological basis for development of tsunami evacuation plans in Sumner. To achieve this, a numerical simulation output of far-field tsunami impacts in Sumner was used to establish the maximum likely inundation extent and flow depth. This, together with population census data and daily activity patterns specified for the study area, established the spatio-temporal basis for characterising population exposure to the tsunamic hazard. A geospatial evacuation analysis method (Least Cost Path Distance), augmented with variable population exposure and distributed travel speeds, was used to characterise spatial variation in evacuation times and the corresponding numbers of evacuees and vehicles. Three ‘extreme’ end-member scenarios were utilised to address possible evacuation methods; all pedestrians evacuated to 20 metres elevation, all pedestrians to bus stops for evacuation using public transport, and all people evacuated using private vehicles. This thesis has made a methodological contribution to tsunami evacuation simulation by characterising variable spatio-temporal population exposure, and incorporating terrain properties into population and vehicle movements. The methods are equally applicable to other locations, to other hazards, and for both pre- and post-disaster evacuation analyses

Evacuation planning in the Auckland Volcanic Field, New Zealand: a spatio-temporal approach for emergency management and transportation network decisions

Auckland is the largest city in New Zealand (pop. 1.5 million) and is situated atop an active monogenetic volcanic field. When volcanic activity next occurs, the most effective means of protecting the people who reside and work in the region will be to evacuate the danger zone prior to the eruption. This study investigates the evacuation demand throughout the Auckland Volcanic Field and the capacity of the transportation network to fulfil such a demand. Diurnal movements of the population are assessed and, due to the seemingly random pattern of eruptions in the past, a non-specific approach is adopted to determine spatial vulnerabilities at a micro-scale (neighbourhoods). We achieve this through the calculation of population-, household- and car-to-exit capacity ratios. Following an analysis of transportation hub functionality and the susceptibility of motorway bridges to a new eruption, modelling using dynamic route and traffic assignment was undertaken to determine various evacuation attributes at a macro-scale and forecast total network clearance times. Evacuation demand was found to be highly correlated to diurnal population movements and neighbourhood boundary types, a trend that was also evident in the evacuation capacity ratio results. Elevated population to evacuation capacity ratios occur during the day in and around the central city, and at night in many of the outlying suburbs. Low-mobility populations generally have better than average access to public transportation. Macro-scale vulnerability was far more contingent upon the destination of evacuees, with favourable results for evacuation within the region as opposed to outside the region. Clearance times for intra-regional evacuation ranged from one to nine hours, whereas those for inter-regional evacuation were found to be so high, that the results were unrealistic. Therefore, we conclude that, from a mobility standpoint, there is considerable merit to intra-regional evacuation

Evaluating Human Driving Behavior and Traffic Operation Conditions during Wildfire Evacuation Using Connected Vehicles Data

With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the western United States, and the communities in wildland-urban interface (WUI) areas are at the greatest risk of fire. Understanding the driving behavior of individuals during evacuating fire-affected WUI areas is important because the evacuees may encounter difficult driving conditions and traffic congestions due to proximity to flammable vegetation and limited exit routes. Existing studies lack empirical data on evacuee driving behavior and traffic operation conditions during a wildfire evacuation. This study used two distinct connected vehicles (CV) datasets that contain lane-level precision historical vehicle trajectory and driving events datasets to investigate the traffic delays and driving behavior of individuals during historical wildfire evacuation events. The results of the study showed that the CV-datasets are a valuable source to accurately evaluate human driving behavior and calculate traffic delays in wildfire-caused evacuations

A systematic review of methodologies for human behavior modelling and routing optimization in large-scale evacuation planning

Frequent and escalating natural disasters pose an increasing threat to society and the environment. Effective disaster management strategies are crucial to mitigate their impact. This paper reviews recent methodologies for large-scale evacuation planning, a key element in risk reduction. A systematic analysis of 100 articles and conference proceedings in evacuation planning, focusing on human factors/behavior modeling and evacuation routing optimization, reveals that Agent-Based Simulation (ABS) is commonly used to predict human factors/behaviors. Heuristics/metaheuristics and traffic assignment techniques dominate evacuation routing planning, often aiming to identify the shortest evacuation path. While evacuation decisions and route choice are extensively studied, optimization approaches frequently lack integration with human factors/behavior modeling. This review underscores the need for further research to enhance evacuation planning by integrating human factors/behavior and optimization methodologies for increased effectiveness and efficiency

An operational research-based integrated approach for mass evacuation planning of a city

Large-scale disasters are constantly occurring around the world, and in many cases evacuation of regions of city is needed. ‘Operational Research/Management Science’ (OR/MS) has been widely used in emergency planning for over five decades. Warning dissemination, evacuee transportation and shelter management are three ‘Evacuation Support Functions’ (ESF) generic to many hazards. This thesis has adopted a case study approach to illustrate the importance of integrated approach of evacuation planning and particularly the role of OR/MS models. In the warning dissemination phase, uncertainty in the household’s behaviour as ‘warning informants’ has been investigated along with uncertainties in the warning system. An agentbased model (ABM) was developed for ESF-1 with households as agents and ‘warning informants’ behaviour as the agent behaviour. The model was used to study warning dissemination effectiveness under various conditions of the official channel. In the transportation phase, uncertainties in the household’s behaviour such as departure time (a function of ESF-1), means of transport and destination have been. Households could evacuate as pedestrians, using car or evacuation buses. An ABM was developed to study the evacuation performance (measured in evacuation travel time). In this thesis, a holistic approach for planning the public evacuation shelters called ‘Shelter Information Management System’ (SIMS) has been developed. A generic allocation framework of was developed to available shelter capacity to the shelter demand by considering the evacuation travel time. This was formulated using integer programming. In the sheltering phase, the uncertainty in household shelter choices (either nearest/allocated/convenient) has been studied for its impact on allocation policies using sensitivity analyses. Using analyses from the models and detailed examination of household states from ‘warning to safety’, it was found that the three ESFs though sequential in time, however have lot of interdependencies from the perspective of evacuation planning. This thesis has illustrated an OR/MS based integrated approach including and beyond single ESF preparedness. The developed approach will help in understanding the inter-linkages of the three evacuation phases and preparing a multi-agency-based evacuation planning evacuatio

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios