scholarly journals An Agent-Based Evaluation of Varying Evacuation Scenarios in Merapi: Simultaneous and Staged

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 317
Author(s):  
Jumadi Jumadi ◽  
Steve J. Carver ◽  
Duncan J. Quincey
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Relative Effectiveness ◽  
Regional Scale ◽  
Traffic Density ◽  
Peak Time ◽  
Agent Based Model ◽  
Merapi Volcano ◽  
Agent Based ◽  
Spatio Temporal

Mass evacuation should be conducted when a disaster threatens within a regional scale. It is reported that 400,000 people were evacuated during the last eruption of Merapi Volcano in 2010. Such a large-scale evacuation can lead to chaos or congestion, unless well managed. Staged evacuation has been investigated as a solution to reducing the degree of chaos during evacuation processes. However, there is a limited conception of how the stages should be ordered in terms of which group should move first and which group should follow. This paper proposes to develop evacuation stage ordering based on the geographical character of the people at risk and examine the ordering scenarios through an agent-based model of evacuation. We use several geographical features, such as proximity to the hazard, road network conditions (accessibility), size of the population, and demographics as the parameters for ranking the order of each population unit in GIS. From this concept, we produced several scenarios of ranking based on different weightings of the parameters. We applied the scenarios in an agent-based model of volcanic evacuation experiment to observe the results. Afterwards, the results were evaluated based on the ability to reduce the risk and spatio-temporal traffic density along road networks compared to the result of simultaneous evacuation to establish the relative effectiveness of the outcome. The result shows that the staged scenario has a better ability to reduce the potential traffic congestion during the peak time of the evacuation compared to the simultaneous strategy. However, the simultaneous strategy has better performance regarding the speed of reducing the risk. An evaluation of the relative performance of the four varying staged scenarios is also presented and discussed in this paper.

scholarly journals Simulating Spatio-Temporal Patterns of Bicycle Flows with an Agent-Based Model

2021 ◽  
Vol 10 (2) ◽  
pp. 88
Author(s):  
Dana Kaziyeva ◽  
Martin Loidl ◽  
Gudrun Wallentin
Keyword(s):  
Regional Scale ◽  
Evidence Base ◽  
Wide Field ◽  
Traffic Patterns ◽  
Agent Based Model ◽  
Validation Data ◽  
Agent Based ◽  
Mobility Behavior ◽  
Spatio Temporal ◽  
Models Validation

Transport planning strategies regard cycling promotion as a suitable means for tackling problems connected with motorized traffic such as limited space, congestion, and pollution. However, the evidence base for optimizing cycling promotion is weak in most cases, and information on bicycle patterns at a sufficient resolution is largely lacking. In this paper, we propose agent-based modeling to simulate bicycle traffic flows at a regional scale level for an entire day. The feasibility of the model is demonstrated in a use case in the Salzburg region, Austria. The simulation results in distinct spatio-temporal bicycle traffic patterns at high spatial (road segments) and temporal (minute) resolution. Scenario analysis positively assesses the model’s level of complexity, where the demographically parametrized behavior of cyclists outperforms stochastic null models. Validation with reference data from three sources shows a high correlation between simulated and observed bicycle traffic, where the predictive power is primarily related to the quality of the input and validation data. In conclusion, the implemented agent-based model successfully simulates bicycle patterns of 186,000 inhabitants within a reasonable time. This spatially explicit approach of modeling individual mobility behavior opens new opportunities for evidence-based planning and decision making in the wide field of cycling promotion

scholarly journals Real-time Analysis of City Scale Transportation Networks in New Orleans Metropolitan Area using an Agent Based Model Approach

2019 ◽  
Vol 271 ◽  
pp. 06007
Author(s):  
Millard McElwee ◽  
Bingyu Zhao ◽  
Kenichi Soga
Keyword(s):  
New Orleans ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Regional Scale ◽  
Agent Based Model ◽  
Agent Based ◽  
Real Time Analysis

The primary focus of this research is to develop and implement an agent-based model (ABM) to analyze the New Orleans Metropolitan transportation network near real-time. ABMs have grown in popularity because of their ability to analyze multifaceted community scale resilience with hundreds of thousands of links and millions of agents. Road closures and reduction in capacities are examples of influences on the weights or removal of edges which can affect the travel time, speed, and route of agents in the transportation model. Recent advances in high-performance computing (HPC) have made modeling networks on the city scale much less computationally intensive. We introduce an open-source ABM which utilizes parallel distributed computing to enable faster convergence to large scale problems. We simulate 50,000 agents on the entire southeastern Louisiana road network and part of Mississippi as well. This demonstrates the capability to simulate both city and regional scale transportation networks near real time.

scholarly journals An Agent Based Model to Detect Variations in the Transmission of Potters from Baetica Province

2021 ◽  
Author(s):  
Maria Coto-Sarmiento ◽  
Simon Carrignon
Keyword(s):  
Roman Empire ◽  
Social Learning ◽  
Material Culture ◽  
Learning Strategies ◽  
Large Scale ◽  
Scale Production ◽  
Agent Based Model ◽  
Pottery Production ◽  
Agent Based ◽  
Modes Of Transmission

The goal of this study is to analyse the transmission of technical skills among potters within the Roman Empire. Specifically, our case study has been focused on the production processes based on Baetica province (currently Andalusia) from 1st to 3rd century AD. Variability of material culture allows observing different production patterns that can explain how social learning evolves. Some differences can be detected in the making techniques processes through time and space that might explain different degrees of specialization. Unfortunately, it is extremely difficult to identify some evidence of social learning strategies in the archaeological record. In Archaeology, this process has been analysed by the study of the production of handmade pottery. In our case, we want to know if the modes of transmission could be similar with a more standardized production as Roman Age. We propose here an Agent-Based Model to compare different cultural processes of learning transmission. Archaeological evidence will be used to design the model. In this model, we implement a simple mechanism of pottery production with different social learning processes under different scenarios. In particular, the aim of this study is to quantify which one of those processes explain better the copying mechanisms among potters revealed in our dataset. We believe that the model presented here can provide a strong baseline for the exploration of transmission processes related to large-scale production.

scholarly journals Identifying Synergistic Interventions to Address COVID-19 Using a Large Scale Agent-Based Model

2020 ◽  
Author(s):  
Junjiang Li ◽  
Philippe J. Giabbanelli
Keyword(s):  
Public Health ◽  
Large Scale ◽  
Contact Tracing ◽  
Remote Work ◽  
Agent Based Model ◽  
Agent Based ◽  
Global Pandemic ◽  
Small Set ◽  
Factorial Design Of Experiments ◽  
The Individual

AbstractThere is a range of public health tools and interventions to address the global pandemic of COVID-19. Although it is essential for public health efforts to comprehensively identify which interventions have the largest impact on preventing new cases, most of the modeling studies that support such decision-making efforts have only considered a very small set of interventions. In addition, previous studies predominantly considered interventions as independent or examined a single scenario in which every possible intervention was applied. Reality has been more nuanced, as a subset of all possible interventions may be in effect for a given time period, in a given place. In this paper, we use cloud-based simulations and a previously published Agent-Based Model of COVID-19 (Covasim) to measure the individual and interacting contribution of interventions on reducing new infections in the US over 6 months. Simulated interventions include face masks, working remotely, stay-at-home orders, testing, contact tracing, and quarantining. Through a factorial design of experiments, we find that mask wearing together with transitioning to remote work/schooling has the largest impact. Having sufficient capacity to immediately and effectively perform contact tracing has a smaller contribution, primarily via interacting effects.

Gaming the System: An Agent-Based Model of Estimation Strategies and their Effects on System Performance

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
John Meluso ◽  
Jesse Austin-Breneman
Keyword(s):  
Systems Engineering ◽  
Large Scale ◽  
System Level ◽  
Conservation Strategy ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Bias Estimation ◽  
Agent Based Model ◽  
Agent Based ◽  
Complex Engineered Systems

Parameter estimates in large-scale complex engineered systems (LaCES) affect system evolution, yet can be difficult and expensive to test. Systems engineering uses analytical methods to reduce uncertainty, but a growing body of work from other disciplines indicates that cognitive heuristics also affect decision-making. Results from interviews with expert aerospace practitioners suggest that engineers bias estimation strategies. Practitioners reaffirmed known system features and posited that engineers may bias estimation methods as a negotiation and resource conservation strategy. Specifically, participants reported that some systems engineers “game the system” by biasing requirements to counteract subsystem estimation biases. An agent-based model (ABM) simulation which recreates these characteristics is presented. Model results suggest that system-level estimate accuracy and uncertainty depend on subsystem behavior and are not significantly affected by systems engineers' “gaming” strategy.

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