A Conceptual Design of Spatio‐Temporal Agent‐ Based Model for Volcanic Evacuation

The understanding of evacuation processes is important for improving the effectiveness of evacuation plans in the event of volcanic disasters. In terms of social processes, the enactment of evacuations in volcanic crises depends on the variability of individual/household responses. This variability of population response is related to the uncertainty and unpredictability of the hazard characteristics of volcanoes—specifically, the exact moment at which the eruption occurs (temporal), the magnitude of the eruption and which locations are impacted (spatial). In order to provide enhanced evacuation planning, it is important to recognise the potential problems that emerge during evacuation processes due to such variability. Evacuation simulations are one approach to understanding these processes. However, experimenting with volcanic evacuations in the real world is risky and challenging, and so an agent‐based model is proposed to simulate volcanic evacuation. This paper highlights the literature gap for this topic and provides the conceptual design for a simulation using an agent‐based model. As an implementation, an initial evacuation model is presented for Mount Merapi in Indonesia, together with potential applications of the model for supporting volcanic evacuation management, discussion of the initial outcomes and suggestions for future work

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

Simulating Electric Vehicle Short-Notice Wildfire Evacuation in California Rural Communities

The transportation sector in California has begun a shift toward adopting Electric Vehicles (EVs) as a primary source of individual and corporate mobility. The US Government and the State of California are initiating public-sector financed charging station infrastructure to help in this change-over to EVs. Automobile companies and private enterprises are also heavily investing in Battery Electric Vehicle (BEV) infrastructure going forward. The state of California is subject to natural disasters such as Fire, Earthquakes, and periodic flooding. Increasing numbers of BEVs may add new challenges to mass evacuations that are often associated with natural disasters. This work focuses on unique challenges in providing BEV charging infrastructure during evacuations in regions that: are small towns with a considerable rural population, are prone to natural disasters, have a single evacuation route, have underdeveloped EV charging infrastructure, are considerable distance to a major center of EV charging infrastructure and safety from the mass evacuation scenario, have a secondary small charging location also available on the single evacuation route that leads to the major city of safety. To analyze the unique challenges of these particular mass-evacuation scenarios, a simulation was created to estimate the evacuation times of the BEV population given a set charging infrastructure. The model also includes BEV charging infrastructure, and for a single secondary charging station that is along the evacuation route. The objective of the simulation model is to determine the charging needs for a rural evacuation scenario and the ideal distance to an alternate secondary charging station along a single evacuation route in order to minimize total evacuation time. In order to provide a more realistic set of scenarios for the model, two different rural evacuation scenarios were analyzed. Kernville, California, in Kern County that is 52 miles from Bakersfield Auberry, California, in Fresno County that is 36 miles from Fresno The BEV charging infrastructure model inputs are customized for assumed BEV charging infrastructure in the year 2025 based on historical BEV registration numbers according to the Department of Motor Vehicles. The simulation results show that the projected charging infrastructure in the year 2025 would suffice for an evacuation scenario in which 90% of the BEV arrive at the evacuation destination within 10 hours of the evacuation order. However, due to the severity of potential danger in short-notice wildfire evacuations, it would be ideal to further decrease the total evacuation time. The simulation model found that increasing the charging infrastructure by one level 3 charge plug had a much larger impact on minimizing evacuation time than increasing it by two level 2 charge plugs. Therefore, it would be beneficial for the rural towns to invest in level 3 chargers to shorten evacuation times

Modeling of Household Evacuation Decision, Departure Timing, and Number of Evacuating Vehicles from Hurricane Matthew

This dissertation investigates households’ evacuation decision, number of household vehicles used in evacuation, and departure timing from Hurricane Matthew. Regarding the evacuation decision, this dissertation takes a step further by presenting three level evacuation decision models that include full, partial, and no evacuation alternatives rather than the binary evacuate/stay decision. Multinomial (MNL) regression and random parameter MNL techniques were utilized to develop the prediction models. Results showed that some of the variables which affect the evacuate/stay decision have different influences on the three alternatives. The preferred MNL model was tested for random parameters and one random parameter (age of the respondent) was identified for the utility expression pertaining to the no evacuation alternative. For the vehicle choice study, zero truncated Poisson regression was utilized with the survey data. This modeling approach has rarely been applied to the evacuation context and the prediction of the number of household vehicles used is relatively understudied, compared to other evacuation-related decisions. The final preferred model contains three significant variables (marital status, gender, and evacuation timing from 6 am to noon). The final part of this dissertation investigates the factors affecting departure timing choice. Having an accurate estimate of the departure time will allow the prediction of dynamic evacuation demand and developing effective evacuation strategies which will enhance the overall evacuation planning and management. A Cox proportional-hazards model was utilized to model the evacuation departure timing. Four significant variables were identified in the final model, two of them are related to uncertainty. This part of the dissertation also studies evacuees’ stated preference about whether or not they would change their evacuation timing if they relived the hurricane event. In our study, almost 34% of respondents reported that they would change their departure timing if they relived the hurricane event. A binary logit model was utilized in this part and the preferred model contains five significant variables related to past experience, the type of evacuation order received, and the evacuation destination

Best-subset Selection for Complex Systems using Agent-based Simulation

It is difficult to analyze and determine strategies to control complex systems due to their inherent complexity. The complex interactions among elements make it difficult to develop and test decision makers' intuition of how the system will behave under different policies. Computer models are often used to simulate the system and to observe both direct and indirect effects of alternative interventions. However, many decision makers are unwilling to concede complete control to a computer model because of the abstractions in the model, and the other factors that cannot be modeled, such as physical, human, social and organizational relationship constraints. This dissertation develops an agent-based simulation (ABS) model to analyze a complex system and its policy alternatives, and contributes a best-subset selection (BSS) procedure that provides a group of good performing alternatives to which decision makers can then apply their subject and context knowledge in making a final decision for implementation. As a specific example of a complex system, a mass casualty incident (MCI) response system was simulated using an ABS model consisting of three interrelated sub-systems. The model was then validated by a series of sensitivity analysis experiments. The model provides a good test bed to evaluate various evacuation policies. In order to find the best policy that minimizes the overall mortality, two ranking-and-selection (R&S) procedures from the literature (Rinott (1978) and Kim and Nelson (2001)) were implemented and compared. Then a new best-subset selection (BSS) procedure was developed to efficiently select a statistically guaranteed best-subset containing all alternatives that are close enough to the best one for a pre-specified probability. Extensive numerical experiments were organized to prove the effectiveness and demonstrate the performance of the BSS procedure. The BSS procedure was then implemented in conjunction with the MCI ABS model to select the best evacuation policies. The experimental results demonstrate the feasibility and effectiveness of our agent-based optimization methodology for complex system policy evaluation and selection

e-Sanctuary: open multi-physics framework for modelling wildfire urban evacuation

The number of evacuees worldwide during wildfire keep rising, year after year. Fire evacuations at the wildland-urban interfaces (WUI) pose a serious challenge to fire and emergency services and are a global issue affecting thousands of communities around the world. But to date, there is a lack of comprehensive tools able to inform, train or aid the evacuation response and the decision making in case of wildfire. The present work describes a novel framework for modelling wildfire urban evacuations. The framework is based on multi-physics simulations that can quantify the evacuation performance. The work argues that an integrated approached requires considering and integrating all three important components of WUI evacuation, namely: fire spread, pedestrian movement, and traffic movement. The report includes a systematic review of each model component, and the key features needed for the integration into a comprehensive toolkit