Simulation Modelling Practice and Theory最新文献_第2页

Passenger flow simulation model for urban rail transit hubs based on cellular automata and multi-agent systems 基于元胞自动机和多智能体系统的城市轨道交通枢纽客流仿真模型

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-12-06 DOI: 10.1016/j.simpat.2025.103242

Lianbo Deng , Jingshuang Li , Huaru Liu , Xiaoshan Zhu , Xinlei Hu

The spatiotemporal rules of passenger flow in urban rail transit (URT) hubs are complex, meaning that simulation modeling and analysis of passenger flow distributions in hubs are very important in terms of scientifically organizing passenger flow and improving travel efficiency. In this study, an analysis was conducted of passengers' travel processes and behaviors, and a simulation model combining cellular automata (CA) and agent-based modeling (ABM) was proposed. A CA grid environment was used to describe the spatial constraints and movement logic, whereas ABM was employed to construct passenger agents. This approach included a visual perception model, a behavior decision-making model that took into consideration the influence of multiple factors, a fuzzy logic-based multi-channel selection model, and a group-competition-based action execution model, in order to finely characterize the individual microscopic behaviors. Tiyu Xilu Station of Guangzhou Metro in China was taken as a case study, and the simulation results were used to verify the effectiveness of the model. The key findings were as follows: the simulation results for escalator passenger throughput were close to the design capacity, with a difference of -4.2%; the service level for the west platform of Line 3 was lower than for the east platform, with the lowest being Level E; during peak hours, for every 10% increase in the degree of bidirectional pedestrian flow, the average dwell time increased by approximately 6.8%. These research results provide decision support for optimizing passenger flow organization in URT hubs.

城市轨道交通枢纽客流时空分布规律复杂，对枢纽客流分布进行仿真建模和分析对于科学组织客流、提高出行效率具有重要意义。本文通过对乘客出行过程和行为的分析，提出了一种结合元胞自动机（CA）和agent-based modeling （ABM）的仿真模型。采用CA网格环境描述空间约束和运动逻辑，采用ABM模型构建座席。该方法包括视觉感知模型、考虑多因素影响的行为决策模型、基于模糊逻辑的多渠道选择模型和基于群体竞争的行动执行模型，以精细表征个体微观行为。以广州地铁体余西路站为例，通过仿真结果验证了该模型的有效性。主要研究结果如下：自动扶梯乘客吞吐量模拟结果与设计容量接近，差值为-4.2%；3号线西月台服务水平低于东月台，最低为E级；在高峰时段，双向人流量每增加10%，平均停留时间增加约6.8%。研究结果为优化交通枢纽客流组织提供决策支持。

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引用次数: 0

The effect of microstructure on the behavior of an underground excavation 微观结构对地下开挖行为的影响

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-12-05 DOI: 10.1016/j.simpat.2025.103241

G. Xiroudakis , G. Saratsis , G.E. Exadaktylos , E. Machairas , E.A. Varouchakis , S. Mavrigiannakis

The room-and-pillar mining method is an ancient technique used in the extraction of underground deposits. Even today, it remains one of the most widespread and productive methods of underground mining, where the abandoned pillars ensure the stability of the opening. The mechanical behavior of the pillar plays an important role in its sizing and the estimation of ore recovery. In this paper, the rock material from a historical ancient underground quarry in Gortyna, Crete, Greece, was numerically investigated. This material, composed of limestone, that exhibits pronounced layering and is very soft, allowed the ancient Romans to excavate underground tunnels with a total length of >2.5 km. A high-resolution 3D X Phase Pro S2 360 camera, was used to capture panoramic photos in both horizontal and vertical planes. These photos were used to estimate the dimensions of the chambers and the central pillar of the accessible underground area of the site (the large underground quarry has been closed). The effect of micro-cracking of the rock on the behavior of the room and pillar excavation method was investigated using Linear Elastic Fracture Mechanics (LEFM) theory. For this purpose, a numerical model was created using the FLAC2D software to estimate the effective elastic parameters of the material based on its density and the arrangement of cracks within it. This analysis revealed that the presence of microcracks introduces an anisotropy similar to that of a transversely isotropic material. The observed anisotropy results in larger deformations in the main chamber crown than those obtained using average elastic properties from experimental, literature, and field investigations of limestone rock.

房柱采矿法是一种用于开采地下矿床的古老技术。即使在今天，它仍然是最广泛和最有效的地下采矿方法之一，其中废弃的支柱确保了开口的稳定性。矿柱的力学行为对矿柱的尺寸确定和回采率的估计起着重要的作用。本文对希腊克里特岛Gortyna一个历史悠久的地下采石场的岩石材料进行了数值研究。这种由石灰石组成的材料，具有明显的分层性，非常柔软，古罗马人因此得以挖掘总长2.5公里的地下隧道。高分辨率3D X Phase Pro S2 360相机，用于捕捉水平和垂直平面的全景照片。这些照片被用来估计墓室的尺寸和可到达的地下区域的中央柱子（大型地下采石场已经关闭）。采用线弹性断裂力学（LEFM）理论，研究了岩石微裂纹对硐室和矿柱开挖行为的影响。为此，利用FLAC2D软件建立数值模型，根据材料的密度和材料内部裂纹的排列来估计材料的有效弹性参数。这一分析表明，微裂纹的存在引入了类似于横向各向同性材料的各向异性。观察到的各向异性导致主室顶部的变形比通过实验、文献和现场调查石灰石的平均弹性特性获得的变形更大。

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引用次数: 0

Anchorage performance of split-grouted rock bolt in fractured rock slope 裂隙岩质边坡裂隙注浆锚杆锚固性能研究

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-12-04 DOI: 10.1016/j.simpat.2025.103239

Chengpu Peng , Lin Geng , Jiaxiang Liu , Liang Tang , Xianzhang Ling

To investigate the anchorage performance of split-grouted rock bolt in fractured rock slopes, a computational model was developed that accounts for rock bolt failure modes and the nonlinear mechanical behavior of the anchorage interface. Field pullout tests were conducted on three sets of split-grouted rock bolts, with ultimate strengths of 250.73 kN, 254.45 kN, and 253.66 kN, respectively. The computational model was then used to fit the experimental data, determining a peak shear strength of the anchorage interface as 0.55 MPa with a corresponding shear displacement of 3.31 mm and a residual shear strength of 0.17 MPa. The nonlinear mechanical characteristics of the anchorage interface were incorporated into numerical simulations to examine the effectiveness of split-grouted rock bolts in slopes with varying joint spacing and grouting radius. Results demonstrated that the combined reinforcement of rock bolts and grouting effectively enhanced the integrity of fragmented rock masses, significantly improving slope stability and altering the failure mode of fractured rock slopes. These conclusions provide valuable insights and practical guidance for the engineering design of split-grouted rock bolt reinforcement systems in fractured rock slope stabilization.

为了研究裂隙岩体边坡中裂隙锚杆的锚固性能，建立了考虑裂隙锚杆破坏模式和锚固界面非线性力学行为的计算模型。对3组裂缝锚杆进行了现场拉拔试验，极限强度分别为250.73 kN、254.45 kN和253.66 kN。利用计算模型对试验数据进行拟合，确定锚固界面峰值抗剪强度为0.55 MPa，对应的剪切位移为3.31 mm，残余抗剪强度为0.17 MPa。将锚固界面的非线性力学特性纳入数值模拟，考察了裂隙锚杆在不同缝距和注浆半径边坡中的有效性。结果表明，锚杆与注浆联合加固有效增强了破碎岩体的完整性，显著提高了边坡稳定性，改变了裂隙岩质边坡的破坏模式。这些结论为裂隙岩质边坡裂隙锚杆加固系统的工程设计提供了有价值的见解和实践指导。

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引用次数: 0

A multi-agent deep reinforcement learning model for crowd coordinated evacuation simulation in complex environments 复杂环境下人群协同疏散仿真的多智能体深度强化学习模型

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-12-03 DOI: 10.1016/j.simpat.2025.103238

Jixin Shi , Maoyu Li , Nan Jiang , Hanchen Yu , Hongyun Yang , Xiaodong Zhou , Lizhong Yang

Efficient evacuation in complex building environments remains a fundamental challenge in simulation modeling and public safety. Traditional models based on static path-finding or rule-based decision-making often result in unbalanced exit utilization and congestion, limiting their ability to represent adaptive human behavior in emergencies. To address these shortcomings, this study proposes a Collaborative Decision-Making Framework based on Multi-Agent Deep Reinforcement Learning (CDF-MADRL). The framework introduces a dynamic multi-objective reward mechanism that adaptively balances individual evacuation time and collective efficiency, and a localized observation strategy that significantly reduces computational burden in large-scale multi-agent environments. Implemented using the MAPPO algorithm, the model was validated in a complex asymmetric multi-exit scenario with varying population sizes. Results show that CDF-MADRL improves training stability by over 70% and reduces average evacuation time by 2–11% compared with baseline models. Beyond improving evacuation efficiency, this research contributes methodologically by demonstrating how reinforcement learning can be systematically embedded into simulation modeling practice, offering a scalable and intelligent framework for evacuation analysis. These findings highlight the potential of integrating artificial intelligence with simulation modeling to enhance resilience in complex built environments.

复杂建筑环境下的高效疏散仍然是仿真建模和公共安全领域的一个基本挑战。基于静态寻径或基于规则的决策的传统模型经常导致出口利用不平衡和拥塞，限制了它们在紧急情况下表现适应性人类行为的能力。为了解决这些不足，本研究提出了一种基于多智能体深度强化学习（CDF-MADRL）的协同决策框架。该框架引入了一种动态多目标奖励机制，自适应平衡个体疏散时间和集体效率，以及一种局部观察策略，显著降低了大规模多智能体环境中的计算负担。该模型采用MAPPO算法实现，并在不同种群规模的复杂非对称多出口场景中进行了验证。结果表明，与基线模型相比，CDF-MADRL的训练稳定性提高了70%以上，平均疏散时间减少了2-11%。除了提高疏散效率之外，本研究还展示了强化学习如何系统地嵌入到模拟建模实践中，为疏散分析提供了一个可扩展的智能框架，从而在方法上做出了贡献。这些发现强调了将人工智能与仿真建模相结合以增强复杂建筑环境中的弹性的潜力。

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引用次数: 0

Simulation based sustainable optimization in edge–fog–cloud energy systems 基于仿真的边缘雾云能源系统可持续优化

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-12-01 DOI: 10.1016/j.simpat.2025.103237

Shadi Alzu’bi , Basem Alokush , Leila Jamel , Fahd N. Al-Wesabi , Randa Allafi

Sustainable scheduling and resource management becomes very important in edge–fog–cloud environments, especially when hybrid renewable energy resources have been highly integrated with IoT applications. The work proposed in this paper introduces a simulation system for optimizing sustainable energy in several operating conditions, it highlights intelligent scheduling and resource allocation as well. Generative AI (GenAI) with edge intelligence have been employed in the proposed system to create synthetic simulated data. This enables efficient performance modelling and improves energy scheduling strategies testing. The experiments which are based on Simulation, improve sustainability metrics (over 30% gains in scheduling efficiency), proved higher Mean Time Between Failures (MTBF) which indicates increased reliability, and energy consumption where significantly reduced keeping workload deadlines. These results emphasize the importance of the proposed systems. Moreover, this work highlight the possibility of expanding the proposed system for emerging computing models such as 6G and the Industrial IoT.

可持续调度和资源管理在边缘雾云环境中变得非常重要，特别是当混合可再生能源与物联网应用高度集成时。本文介绍了一种可在多种工况下优化可持续能源的仿真系统，并强调了智能调度和资源分配。该系统采用边缘智能生成人工智能（GenAI）来生成综合模拟数据。这使得高效的性能建模和改进能源调度策略测试成为可能。基于仿真的实验改善了可持续性指标（调度效率提高30%以上），证明了更高的平均故障间隔时间（MTBF），这表明可靠性提高，并且能耗显著降低了保持工作负载的截止日期。这些结果强调了所提出系统的重要性。此外，这项工作强调了将拟议系统扩展到新兴计算模型（如6G和工业物联网）的可能性。

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引用次数: 0

Intelligent system modeling using GenAI: A methodology for automated simulation model generation 使用GenAI的智能系统建模：一种自动仿真模型生成的方法

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-27 DOI: 10.1016/j.simpat.2025.103236

Lin Zhang , Yuteng Zhang , Dusit Niyato , Lei Ren , Pengfei Gu , Zhen Chen , Yuanjun Laili , Wentong Cai , Agostino Bruzzone

Generative AI (GenAI) has demonstrated remarkable capabilities in code generation, and its integration into model-based systems engineering for complex product modeling and simulation code generation can significantly enhance the efficiency of product design and modeling. In this study, we introduce a generative system modeling framework, offering a practical approach for the intelligent generation of simulation models for system physical properties. First, we fine-tune the language model used for simulation model generation on an existing library of simulation models and additional datasets generated through generative modeling. Subsequently, we employ BERT-based inference techniques, generative models, and integrated modeling and simulation languages to construct simulation models for system physical properties of products based on product design documents. Thereafter, we introduce evaluation metrics for the generated simulation models for system physical properties. Finally, we propose a validation and simulation framework for generated simulation models. Our proposed approach to simulation model generation presents the innovative concept of scalable templates for simulation models. Using these templates, GenAI generates simulation models for system physical properties through code completion. The experimental results demonstrate that, for mainstream open-source Transformer-based models, the quality of the simulation model is improved by 21.4% using the simulation model generation method proposed in this paper.

生成式人工智能（GenAI）在代码生成方面表现出了显著的能力，将其集成到基于模型的系统工程中用于复杂产品建模和仿真代码生成，可以显著提高产品设计和建模的效率。在本研究中，我们引入了一个生成式系统建模框架，为系统物理特性仿真模型的智能生成提供了一种实用的方法。首先，我们在现有的仿真模型库和通过生成建模生成的附加数据集上微调用于仿真模型生成的语言模型。随后，我们采用基于bert的推理技术、生成模型和集成建模与仿真语言，基于产品设计文档构建产品系统物理特性仿真模型。然后，我们介绍了对生成的系统物理特性仿真模型的评估指标。最后，我们为生成的仿真模型提出了一个验证和仿真框架。我们提出的仿真模型生成方法提出了仿真模型可扩展模板的创新概念。使用这些模板，GenAI通过代码完成生成系统物理属性的仿真模型。实验结果表明，对于主流的开源变压器模型，采用本文提出的仿真模型生成方法，仿真模型的质量提高了21.4%。

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引用次数: 0

Generative agents for urban mobility: A cognitive framework for realistic travel behavior simulation 城市交通的生成代理：现实出行行为模拟的认知框架

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-27 DOI: 10.1016/j.simpat.2025.103234

Qi Liu, Can Li, Wanjing Ma

Traditional agent-based urban mobility simulations rely on predefined expert rules, limiting their ability to capture the complexity and adaptability of human mobility decisions. This paper introduces GATSim (Generative Agent Transport Simulation), a novel framework that integrates agents powered by large language models (LLM) into urban mobility simulation environments. GATSim employs generative agents with innovative cognitive architectures including hierarchical memory systems, multi-modal retrieval mechanisms, planning, reactive and reflection processes. GATSim is validated at both microscopic and macroscopic levels using a prototype on a stylized transportation network. The results show that the generative agents exhibit peak spreading, route learning, and incident response behaviors that mirror the dynamics of the real world. This work contributes to the paradigm shift from rule-based to intelligence-based urban mobility simulation, providing a more realistic and flexible framework for urban transportation modeling. The code for the prototype implementation is publicly available at: https://GitHub.com/qiliuchn/gatsim.

传统的基于智能体的城市交通模拟依赖于预定义的专家规则，限制了它们捕捉人类交通决策的复杂性和适应性的能力。本文介绍了GATSim（生成代理运输仿真），这是一个将大型语言模型（LLM）驱动的代理集成到城市交通仿真环境中的新框架。GATSim采用具有创新认知架构的生成代理，包括分层记忆系统、多模态检索机制、规划、反应和反射过程。GATSim在微观和宏观层面上使用一个程式化运输网络的原型进行验证。结果表明，生成智能体表现出峰值扩展、路径学习和事件响应行为，反映了现实世界的动态。这项工作有助于从基于规则的城市交通模拟向基于智能的城市交通模拟的范式转变，为城市交通建模提供了一个更加现实和灵活的框架。原型实现的代码可以在：https://GitHub.com/qiliuchn/gatsim上公开获得。

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引用次数: 0

Optimizing process parameters in cold spray additive manufacturing: A data-driven, simulation-based multi-objective approach 冷喷涂增材制造工艺参数优化：数据驱动、基于仿真的多目标方法

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-26 DOI: 10.1016/j.simpat.2025.103235

Hao Chen , Zhilang Zhang , Markus Bambach , Mohamadreza Afrasiabi

Cold spray additive manufacturing (CSAM) is an emerging solid-state deposition technique that utilizes high-velocity gas to propel powdered materials onto a substrate. Analysis of objective functions for process parameter optimization in CSAM requires data that is usually obtained from costly experiments or numerical simulations. Integrating simulations or experiments directly into conventional optimization algorithms can lead to significantly high computational costs. Additionally, these optimization problems typically involve multiple conflicting objectives that should be taken into account simultaneously. In this work, we develop a data-driven, simulation-based multi-objective optimization framework (SMOF) to optimize CSAM process parameters online. The smoothed particle hydrodynamics (SPH) method is used to perform CSAM simulations. A new optimal grid mutation-based infill criterion (OIC) is proposed to enhance the surrogate-assisted search in SMOF. Subsequently, numerical simulations are replaced by an ensemble of surrogates with high prediction robustness. We assess the effectiveness of the proposed OIC on two benchmark test problems and further optimize multiple powder impact problems. The optimization results demonstrate that the present SMOF can identify desired process parameter combinations for the CSAM process. Based on the proposed SMOF, refined multi-objective process parameter windows are established for the first time to analyze the evolution of CSAM process parameters.

冷喷涂增材制造（CSAM）是一种新兴的固态沉积技术，它利用高速气体将粉末材料推进到基板上。CSAM过程参数优化的目标函数分析通常需要从昂贵的实验或数值模拟中获得数据。将模拟或实验直接集成到传统的优化算法中可能会导致非常高的计算成本。此外，这些优化问题通常涉及多个应该同时考虑的相互冲突的目标。在这项工作中，我们开发了一个数据驱动的，基于仿真的多目标优化框架（SMOF）来在线优化CSAM工艺参数。采用光滑粒子流体力学（SPH）方法进行了CSAM模拟。提出了一种新的基于最优网格突变的填充准则（OIC），以增强SMOF中的代理辅助搜索。随后，用具有高预测鲁棒性的代理集合代替数值模拟。我们评估了所提出的OIC在两个基准测试问题上的有效性，并进一步优化了多个粉末冲击问题。优化结果表明，该方法能够识别出CSAM工艺所需的工艺参数组合。在此基础上，首次建立了精细化的多目标工艺参数窗口来分析CSAM工艺参数的演化。

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引用次数: 0

A GIS-integrated agent-based simulation framework for modeling and evaluation of police patrol operations 基于gis集成agent的警察巡逻行动建模与评估仿真框架

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-25 DOI: 10.1016/j.simpat.2025.103233

Yasaman Ghasemi , Yuan Zhou , Sina Zare , Victoria C.P. Chen

This study presents a Geographic Information Systems (GIS)-integrated agent-based simulation (ABS) framework designed to evaluate police patrol deployment and shift scheduling under realistic operational constraints. The model integrates empirical Intergraph Computer-Aided Dispatch (I/CAD) data, GIS-based travel-time routing, and shift-level scheduling logic within a unified ABS environment. It captures dynamic interactions among patrol units, incident locations, and time-varying service demand.

A series of scenario-based experiments investigate the effects of key operational parameters, shift length (8-hour vs. 10-hour), patrol force size, and routing logic (shortest vs. fastest path) on system performance indicators such as response time, officer utilization, and workload balance. Results show that 10-hour shifts consistently improve response efficiency compared to 8-hour shifts, while larger patrol sizes enhance workload equity without significantly reducing delays. The model also quantifies the trade-offs between workforce expansion and scheduling strategy.

The simulation is calibrated using real-world patrol data from the Arlington Police Department, Texas, and validated through both historical benchmarks and synthetic call-arrival profiles. The model offers a configurable and adaptable simulation-based planning framework for urban public-service operations. The proposed framework demonstrates how agent-based simulation, enriched with spatial routing and empirical scheduling data, can support tactical decision-making in complex, service-driven systems.

本研究提出了一个地理信息系统（GIS）集成的基于agent的仿真（ABS）框架，旨在评估现实操作约束下的警察巡逻部署和轮班调度。该模型在统一的ABS环境中集成了经验的Intergraph计算机辅助调度（I/CAD）数据、基于gis的行程时间路由和班次级调度逻辑。它捕获巡逻单位、事件地点和时变服务需求之间的动态交互。一系列基于场景的实验研究了关键操作参数、轮班长度（8小时vs 10小时）、巡逻部队规模和路由逻辑（最短vs最快路径）对系统性能指标（如响应时间、警官利用率和工作负载平衡）的影响。结果表明，与8小时轮班相比，10小时轮班可以持续提高响应效率，而更大的巡逻规模可以提高工作量公平，但不会显著减少延误。该模型还量化了劳动力扩张和调度策略之间的权衡。模拟使用来自德克萨斯州阿灵顿警察局的真实巡逻数据进行校准，并通过历史基准和合成呼叫到达配置文件进行验证。该模型为城市公共服务运营提供了一个可配置和适应性强的基于仿真的规划框架。提出的框架展示了基于智能体的仿真，丰富了空间路由和经验调度数据，如何在复杂的服务驱动系统中支持战术决策。

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引用次数: 0

Optimization of urban mobility processes through the integration of process mining 通过一体化流程挖掘优化城市交通流程

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-25 DOI: 10.1016/j.simpat.2025.103232

Selsabil Ines Bouhidel, Nabil Belala

We introduce a dual-log process mining approach for jointly modeling and optimizing behaviors in Vehicular Ad Hoc Networks (VANETs) and urban road traffic. Simulation event logs from SUMO (traffic dynamics) and NS2 (network communications) are synchronized, preprocessed, and mined using Fuzzy Miner and Petri-net discovery in the ProM tool to produce interpretable process models. These models uncover critical anomalies, congestion hotspots, CO

_{2}

emissions peaks, and packet-delivery bottlenecks and drive a continuous feedback loop that adaptively tunes routing protocols and eco-driving strategies in real-time. Experimental evaluation demonstrated the framework’s effectiveness in identifying recurring high-emission behaviors, communication bottlenecks, and incomplete packet flows across a large-scale VANET and traffic simulation dataset. The process models significantly improved behavioral interpretability and reduced the time required for manual analysis and anomaly tracing. Future work will extend this approach with predictive modules and online mining capabilities for enhanced adaptability in dynamic VANET environments.

我们介绍了一种双日志过程挖掘方法，用于联合建模和优化车辆自组织网络（VANETs）和城市道路交通中的行为。来自SUMO（流量动态）和NS2（网络通信）的仿真事件日志被同步、预处理，并使用ProM工具中的模糊Miner和Petri-net发现进行挖掘，以产生可解释的过程模型。这些模型揭示了关键异常、拥堵热点、二氧化碳排放峰值和数据包传输瓶颈，并驱动了一个持续的反馈循环，自适应地实时调整路由协议和生态驾驶策略。实验评估证明了该框架在识别大规模VANET和流量模拟数据集上反复出现的高排放行为、通信瓶颈和不完整数据包流方面的有效性。过程模型显著提高了行为的可解释性，减少了手工分析和异常跟踪所需的时间。未来的工作将通过预测模块和在线挖掘功能扩展这种方法，以增强动态VANET环境的适应性。

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引用次数: 0