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agent-based modeling Under Climate Uncertainties: The Coastal Resilience (CRes-ABM) Model

JOURNAL OF GEOVISUALIZATION AND SPATIAL ANALYSIS 2025年第1期9卷 1-20页

作者： Roukounis, Charalampos Nikolaos Chondros, Michalis Tsoukala, Vasiliki K. Tsihrintzis, Vassilios A. Natl Tech Univ Athens Ctr Assessment Nat Hazards & Proact Planning Sch Rural Surveying & Geoinformat Engn 9 Heroon Polytech Str Athens 15780 Greece Natl Tech Univ Athens Sch Rural Surveying & Geoinformat Engn Lab Reclamat Works & Water Resources Management 9 Heroon Polytech Str Zographou Athens 15780 Greece Natl Tech Univ Athens Sch Civil Engn Dept Water Resources & Environm Engn Lab Harbour Works 9 Heroon Polytech Str Athens 15780 Greece

The Coastal Resilience agent-based Model (CRes-ABM) is introduced, which was created to evaluate the resilience of coastal areas to the effects of climate change, with a particular emphasis on sea-level rise and extreme weather events. The aim is to provide information for developing policies and making decisions about boosting coastal resilience. CRes-ABM simulates stakeholder interactions and adaptation responses within coastal communities by integrating agent-based modeling techniques, geospatial data, and climate change scenarios. On the basis of the CResI index and the effect of adaptation strategies (CResI_adapt), CRes-ABM produces vulnerability-resilience maps. A variety of parameters are used to monitor agent decisions throughout each simulation. We compare the outcomes using historical data (1976-2005) and two custom climate change scenarios (2041-2100). A thorough framework for comprehending and addressing issues related to coastal resilience is provided by CRes-ABM, which makes it easier to make evidence-based decisions about successful adaptation measures. The structure of CRes-ABM allows it to be applied to various coastal regions worldwide, enabling the evaluation of resilience across different geographic, climatic, and socio-economic contexts.

关键词： Climate change adaptation Coastal resilience index Sea-level rise Coastal resilience assessment Coastal flooding agent-based modeling

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agent-based modeling as a Tool for Ecological Comanagement of Grazing Lands

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RANGELAND ECOLOGY & MANAGEMENT 2025年 99卷 88-101页

作者： Monlezun, Anna Clare Boone, Randall B. Wagner, Gianna Trainor, Nick Lynn, Stacy J. Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA Colorado State Univ Nat Resources Ecol Lab Ft Collins CO 80523 USA Trainor Cattle Co Watkins CO 80137 USA

Rangeland management necessitates addressing complex and dynamic social-ecological challenges and opportunities at scales appropriate to target landscapes. We combine geospatial and climate data in an agent-based system dynamics model to simulate temporally and spatially scalable rangeland human- environment-animal-forage relationships. modeling highly variable grazing system elements requires both adaptability and mathematical realism. agent-based modeling makes it possible to test alternative approaches to real-world scenarios without taking risks associated with actual experimentation on working operations. Our agent-based model, Ecological Comanagement of Rangelands, or ECo-Range, allows managers to simulate cattle grazing scenarios by setting environmental conditions and management decisions that affect simulation outcomes. In this sense, ECo-Range is not just a product of scientific inquiry, but a tool to be used for collaborative discovery, as it illuminates relationships among environmental conditions, management decisions, and ecological and livestock outcomes for modeled landscapes. ECo-Range explores the complexities of grazing management, embracing rather than excluding variability and heterogeneity inherent in rangeland social-ecological systems. We first present ECo-Range, explaining the model's relevance to the fields of simulation modeling and rangeland management. We then present a case study on the Colorado Front Range as proof of concept to test the utility, validity, and applicability of ECo-Range as a learning tool to explore scenarios related to government-owned landscapes that necessitate comanagement approaches to cattle grazing. (c) 2025 The Author(s). Published by Elsevier Inc. on behalf of The Society for Range Management. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/)

关键词： agent-based modeling Conservation Human dimensions Land management tools Rangeland ecosystems System dynamics

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agent-based modeling in solid waste management: Advantages, progress, challenges and prospects

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ENVIRONMENTAL IMPACT ASSESSMENT REVIEW 2025年 110卷

作者： Tian, Xi Peng, Fei Wei, Guoen Xiao, Chong Ma, Qingyuan Hu, Zhikang Liu, Yaobin Nanchang Univ Res Ctr Cent China Econ & Social Dev Nanchang 330031 Peoples R China Nanchang Univ Engn Res Ctr Watershed Carbon Neutralizat Minist Educ Nanchang 330031 Peoples R China Nanchang Univ Sch Econ & Management Nanchang 330031 Peoples R China Nanchang Univ Sch Resources & Environm Nanchang 330031 Peoples R China Jiangxi Univ Finance & Econ Nanchang 330031 Peoples R China

The growing issue of solid waste management (SWM) is recognized as a significant challenge to ecosystem preservation. agent-based modeling (ABM) has received significant attention for its capability to address complex systems and simulate the outcomes of strategic implementation. This review compares ABM with other methods and provides a comprehensive overview of research on ABM in SWM from 2000 to 2023, emphasizing its advantages, progress, challenges, and future directions. Results indicate that: 1) ABM possesses 8 key advantages in simulating individual behavior, responses to environmental changes across time and spatial scales, and decision-making processes, namely interactivity, heterogeneity, dynamism, traceability, spatiality, scalability, complexity, and adaptability. 2) Current research primarily focuses on simulating behavioral and strategic effects of SWM (accounting for 45.5 %), while multi-model coupling is becoming a new trend. 3) ABM encounters challenges in its research, including a lack of standardized research steps, high data dependency, limited computing resources, and difficulties in algorithm explanation. Therefore, this study introduces a set of normative steps that provide clear guidance for research and help ensure the reproducibility and accuracy of studies. Future research should incorporate big data and emerging technologies to enhance computational efficiency and processing capabilities of models. To better utilize ABM for achieving environmental protection and sustainable development goals, prioritizing integration of multi-model coupling, interdisciplinary collaboration, visualization, and open-source code sharing as key strategies is essential.

关键词： Solid waste management agent-based modeling Sustainable development Complex systems simulation Systematic review

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agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspections

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DATA & POLICY 2025年 7卷 e6-e6页

作者： Koid, Eunice van der Voort, Haiko Warnier, Martijn Delft Univ Technol Multiactor Syst Dept Delft Netherlands

Effective enforcement of laws and regulations hinges heavily on robust inspection policies. While data-driven approaches to testing the effectiveness of these policies are gaining popularity, they suffer significant drawbacks, particularly a lack of explainability and generalizability. This paper proposes an approach to crafting inspection policies that combines data-driven insights with behavioral theories to create an agent-based simulation model that we call a theory-infused phenomenological agent-based model (TIP-ABM). Moreover, this approach outlines a systematic process for combining theories and data to construct a phenomenological ABM, beginning with defining macro-level empirical phenomena. Illustrated through a case study of the Dutch inland shipping sector, the proposed methodology enhances explainability by illuminating inspectors' tacit knowledge while iterating between statistical data and underlying theories. The broader generalizability of the proposed approach beyond the inland shipping context requires further research. Policy Significance Statement Inspectorates often formulate inspection policies using models based on historical data. However, the specific behavioral interactions driving these models are often not well understood. This paper proposes a data-driven approach to constructing inspection models that shed light on the fundamental mechanisms contributing to the observed system-level behavior in real-world data. We illustrate the construction of a theory-infused phenomenological agent-based model by enriching historical data with behavioral theories. Adopting such models enables inspection agencies to glean valuable insights into the fundamental principles influencing inspection policies, thereby fostering the development of less biased models and more effective inspection policies.

关键词： agent-based modeling data-driven modeling inspections risk-based regulation

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A Retrospective Evaluation of Pandemic Policy Impact on University Campus: An agent-based modeling Approach for Mobility, Disease Propagation, and Testing During COVID-19

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Chen, Yijie Islam, Md Tariqul Jain, Saurabh Chowdhury, Bijoy Dripta Barua Son, Young-Jun Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA Ctr Dis Control & Prevent 1600 Clifton Rd Atlanta GA 30333 USA Purdue Univ Edwardson Sch Ind Engn 315 Grant St W Lafayette IN 47906 USA Indian Inst Technol Ind Engn & Operat Res Mumbai India Nike Beaverton OR USA

In the fight against the spread of infectious disease, university campuses present a unique set of obstacles because of the prevalence of communal living arrangements and the difficulties of restricting sociability and group gatherings. In the last few years, the dynamics of the pandemic have evolved, and so too have the challenges university campuses face in ensuring their communities' health and safety. This study introduces a complex and adaptable hybrid model, which was developed, applied, and validated using data from the peak incidents of SARS-CoV-2 during 2020-2021. It combines an agent-based model (ABM) with a modified SEIR system dynamics approach. Recognizing the shifting landscape of the pandemic, this model has been designed to function as both a historical case study of COVID-19 dynamics on a university campus and a framework for addressing future pandemic threats. By simulating disease propagation considering contact distance (i.e., 0-3 feet, 3-6 feet), contact duration (e.g., indoor layout, capacity, ventilation), and individual behaviors (e.g., partying, gathering, sporting, off-campus activities), the model serves as a comprehensive analysis tool. It incorporates GIS-based campus data, real-time Wi-Fi occupancy data, student schedule-based behavioral patterns to closely emulate campus life. The testing frequency (e.g., mandatory, voluntary), methods (e.g., PCR, antigen, antibody, saline gargle, saliva), and different containment policies (e.g., mask-wearing, vaccination) enhance the model's predictive capabilities. The model's flexible structure allows stakeholders to adapt it to current and emerging scenarios. Retrospective analyses indicate that strategies like indoor mask mandates, frequent testing, and high vaccination rates were pivotal in managing spread of disease. The model's predictive accuracy, as evidenced by high accurate rate (i.e., 85.1% accuracy with an average deviation of 4.42 cases per day) when comparing the model output to actual

关键词： agent-based modeling COVID-19 Disease propagation Simulation Predictive modeling Pandemic Mobility

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Integrating reinforcement-learning-based vehicle dispatch algorithm into agent-based modeling of autonomous taxis

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TRANSPORTATION 2025年第2期52卷 641-667页

作者： Li, Zequn Lokhandwala, Mustafa Al-Abbasi, Abubakr O. Aggarwal, Vaneet Cai, Hua Purdue Univ Sch Ind Engn 315 N Grant St W Lafayette IN 47907 USA Purdue Univ Environm & Ecol Engn W Lafayette IN 47907 USA

While increasing number of studies are using agent-based models to study the potential environmental, economic, and social impacts of shared autonomous vehicles, the idle vehicle dispatching in these models is often simplified to heuristic rules. Because the system performance of an autonomous taxi fleet can be significantly affected by the vehicle dispatching algorithm, refining the vehicle dispatching can help us better evaluate the potential benefits and impacts of shared autonomous vehicle systems. The recent development of reinforcement-learning-based vehicle dispatching algorithms provides opportunities to improve autonomous vehicle system modeling with efficient and scalable vehicle dispatching. This study integrates a reinforcement learning algorithm into to an agent-based simulation model of a ride hailing system. Using an autonomous taxi fleet in New York City as a case study, we compared the system performance of using the proposed Deep Q-Network (DQN) method for dispatching decision with common rule-based and heuristic dispatch algorithms from relevant literatures. The results show that (1) DQN dispatches vehicles more conservatively (with less dispatching activities and distances) but achieved similar (slightly lower) rider service level with proactive dispatch methods;and (2) DQN outperformed all other dispatch methods evaluated in this study with significantly higher dispatch efficiency, as measured by the ratio of the number of extra riders served due to dispatching to the extra fleet dispatch distance.

关键词： Autonomous vehicles agent-based modeling Reinforcement learning Ride hailing Vehicle dispatching efficiency

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Shared low-speed autonomous vehicles for short-distance trips: agent-based modeling with mode choice analysis

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TRANSPORTATION PLANNING AND TECHNOLOGY 2025年第2期48卷 313-341页

作者： Zhang, Zihe Liu, Jun Nie, Qifan Jones, Steven Univ Alabama Dept Civil Construct & Environm Engn Tuscaloosa AL 35487 USA Univ Alabama Alabama Transportat Inst Tuscaloosa AL USA Univ Alabama Transportat Policy Res Ctr Tuscaloosa AL USA

Previous research has envisioned behavior changes with the availability of shared autonomous vehicle (SAV) services. It is expected that SAVs will compete with conventional human-driven vehicles and lead to a shift in mode share. If designed as single-occupancy, low-speed, and lightweight, SAVs can also be considered a micro-mobility mode targeting short-distance trips in geofencing areas, which has been lacking in discussion in the state-of-the-art literature. To address this gap, this study envisions changes in mode choice with the implementation of a single-occupancy Shared Low-speed Light-weight Autonomous Mobility (SLLAM) system deployed, serving trips at speeds below 25 mph. agent-based simulations are conducted to evaluate the mode share of the SLLAM system. The findings demonstrate that the SLLAM system has the potential to serve 36.98% of the trip demand in the study area. Increased operating speeds enhance the SLLAM mode share, making it a more attractive option for existing private vehicle trips.

关键词： Low-speed autonomous vehicle agent-based modeling Mode choice Sensitive analysis Mode share Shared autonamous vehicle

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Analyzing wildfire evacuation dynamics with agent-based modeling in damaged road networks

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SAFETY SCIENCE 2025年 187卷

作者： Ma, Fangjiao Lee, Ji Yun Univ Oklahoma Cooperat Inst Severe & High Impact Weather Res & O Norman OK 73072 USA Washington State Univ Pullman WA 99164 USA

Wildfires increasingly threaten residents in the Western United States. Despite numerous state and local initiatives aimed at mitigating these risks, completely eliminating the wildfire dangers remains unfeasible due to substantial inherent uncertainties. In this case, evacuation is the most important and effective strategy for reducing human casualties during wildfire events. While the primary goal of evacuation-moving people at risk to safer places-appears straightforward to achieve, the reality is complicated by unpredictable human behaviors and the surge in travel demand, which often results in severe traffic congestion and, consequently, a heightened risk to human lives. In addition, the reduced traffic-carrying capacities of road segments due to wildfires further exacerbate these challenges. In this context, wildfire evacuation simulation can serve as an effective experimental means for emergency management and evacuation planning, offering a cost-effective method to identify bottlenecks and critical congestion points during an evacuation. This paper proposes an agent-based modeling (ABM) framework specifically designed to simulate wildfire evacuations in damaged transportation settings. The proposed framework uniquely integrates wildfire simulation and road network vulnerability assessment with ABM, allowing for a detailed representation of human behaviors during evacuations and the dynamic network functionality in microscopic traffic simulation. A notable contribution of this study is its fully probabilistic approach, which evaluates evacuation performance and identifies critical components of the road network not under a single scenario but under a range of representative scenarios. This probabilistic perspective provides a more comprehensive understanding of potentially vulnerable and congested points, thereby enabling emergency managers and transportation planners to better allocate resources and enhance mobility during wildfire evacuations. The effective

关键词： Wildfire Evacuation agent-based modeling Vulnerability assessment Traffic simulation Machine learning

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SERDUX-MARCIM: Maritime Cyberattack simulation using Dynamic modeling, Compartmental Models in Epidemiology and agent-based modeling

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2025年第3期24卷 1-48页

作者： Cabuya-Padilla, Diego Diaz-Lopez, Daniel Martinez-Paez, Juan Hernandez, Laura Castaneda-Marroquin, Carlos Naval Sch Admiral Padilla Engn Fac Tv 52 17-150 Isla Manzanillo Cartagena 130001 Colombia Univ Rosario Sch Engn Sci & Technol Carrera 6 12 C-16 Bogota 111711 Colombia Serval Networks Ave Ind 4 Madrid 28108 Spain

Cybersecurity protects computer data, programs, systems, and networks from unauthorized access, attacks, or theft. By studying cyberattacks, cybersecurity professionals gain insights into attackers' tactics, techniques, and methods, which are crucial for developing effective defense strategies and preventing future attacks. This paper introduces SERDUX-MARCIM, a model for simulation, modeling, and analyzing cyberattacks' propagation in maritime infrastructure, considering network-specific characteristics and target and attacker capabilities. This proposal is supported by a simulation environment in Matlab and Netlogo, considering some of the most accepted cyber risk assessment methodologies and compartmental models in epidemiology. Considering the complexities of the maritime sector. SERDUX-MARCIM is also validated through extensive experimentation in different attack scenarios that represent real-world cyber campaigns in the maritime sector, showing the effectiveness of our proposal.

关键词： agent-based modeling Compartmental models Cyberattack Cyberdefense Cybersecurity Cyber risk Cyber threats Dynamic modeling Maritime cybersecurity Naval Cyber situational awareness.

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Improving evacuation policies through agent-based modeling and stakeholder engagement in hazard-prone areas

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2025年 119卷

作者： Moradi, Hossein Iskandar, Rouba Rodriguez, Sebastian Singh, Dhirendra Dugdale, Julie Tzempelikos, Dimitrios Sfetsos, Athanasios Bakogianni, Evangelia Pavlidi, Evrydiki Diaz, Josue Ribas, Margarita Moragues, Alexandre Estrany, Joan RMIT Europe Barcelona Spain Univ Grenoble Alps Grenoble France RMIT Univ Melbourne Australia CSIRO Data61 Clayton Australia Nisyros Municipal Attica Greece Natl Ctr Sci Res Agia Paraskevi Aghia Paraskevi Greece Government of the Balearic Islands Mallorca Spain Univ Balearic Isl Nat Hazards & Emergenc Observ Balear Isl Mallorca Spain

This paper introduces C-SAFE, a comprehensive framework for developing emergency evacuation simulations to enhance pre-disaster preparedness. C-SAFE combines a simulation framework encompassing five key dimensions: Infrastructure, Population Demographics, Evacuation Policy, Hazard Model, and Human Behavior Model. This allows for the systematic creation of what-if scenarios to test the effectiveness of evacuation policies under various conditions. Additionally, the framework integrates a co-creation process involving local stakeholders, ensuring the scenarios are tailored to specific contexts. This complementary operation provides an iterative mechanism, addressing both the granularity required for simulation resolution and the detail needed for practical and context-sensitive simulation scenario development. C-SAFE is tested in Greece focusing on earthquake hazards, producing metrics such as time analysis, heatmaps of population dynamics, and insights into preferred safe locations. The validation process in this case study demonstrates (1) an improved understanding of evacuation concerns, (2) a clear process for defining evacuation scenarios, and (3) enhanced communication between stakeholders through simulations. Furthermore, the framework is also applied in Spain, focusing on flash flood hazards, where results show, for example, that the early warning system could reduce the number of stranded individuals by 37 %, confirming the framework's value for optimizing evacuation policies according to practitioners.

关键词： agent-based modeling Human behavior modeling Co-creation processes Evacuation strategies Disaster preparedness

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