Simulation Modelling Practice and Theory最新文献_第10页

Multi-intersection platoon ecological speed planning strategy and method for autonomous driving simulation testing 自动驾驶仿真测试中多交叉口排生态速度规划策略与方法

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

Simulation Modelling Practice and Theory

Pub Date : 2025-06-10 DOI: 10.1016/j.simpat.2025.103166

Chuanxiang Ren , Li Lu , Xiang Liu , Fangfang Fu , Lin Cheng

With the rapid development of Internet of Vehicles (IoV) technology, ecological speed planning has become a critical challenge in eco-driving, particularly in reducing energy consumption and improving the efficiency of autonomous vehicles. A key research focus is how to achieve energy savings and emission reductions by optimizing driving speed under various complex conditions, while simultaneously ensuring driving comfort and traffic efficiency. In view of this, a multi-intersection ecological speed planning strategy and method for autonomous platoon is proposed, aiming to reduce speed fluctuations and energy consumption of autonomous platoon in multiple driving scenarios. Firstly, the scenarios of platoon passing through the current intersection and its downstream intersection are analyzed, and then, the strategies for the platoon to pass through the current and its downstream intersections are proposed, including constant speed strategy (CSS) and segmented speed strategy (SSS). Moreover, the platoon ecological speed planning method is presented, which includes the calculation of the passage period, the capacity in the passage period of the intersections, and the platoon ecological speed. Finally, different simulation situations are designed in view of different ecological speed strategies, and compared with the single intersection platoon speed strategy (SIPSS) and the no speed strategy (NSS). The results indicate that the CSS and the SSS can mitigate the speed fluctuations of the platoon through intersections, reduce the fuel consumption and delay time, and outperform the SIPSS and NSS. Especially in the current intersection with a queuing platoon, the proposed strategy reduces fuel consumption and delay time by up to 67.21 % and 2.74 %, respectively.

随着车联网技术的快速发展，生态速度规划已成为生态驾驶的关键挑战，特别是在降低能源消耗和提高自动驾驶汽车效率方面。如何在各种复杂条件下通过优化行驶速度实现节能减排，同时保证驾驶舒适性和交通效率是一个关键的研究重点。鉴于此，提出了一种多交叉口的自主队列生态速度规划策略和方法，旨在减少自主队列在多驾驶场景下的速度波动和能量消耗。首先分析了车队通过当前交叉口及其下游交叉口的场景，然后提出了车队通过当前交叉口及其下游交叉口的策略，包括恒速策略（CSS）和分段速度策略（SSS）。在此基础上，提出了队列生态速度规划方法，包括交叉口通行周期、通行周期通行能力和队列生态速度的计算。最后，针对不同的生态速度策略设计了不同的仿真场景，并与单交叉口排速策略（SIPSS）和无速度策略（NSS）进行了比较。结果表明，CSS和SSS能有效缓解车辆通过交叉口时的速度波动，降低车辆的油耗和延迟时间，优于sips和NSS。特别是在当前存在排队队列的交叉口，该策略可将燃油消耗和延误时间分别降低67.21%和2.74%。

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引用次数: 0

Optimizing IoMT network performance using gateway placement 使用网关布局优化IoMT网络性能

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

Simulation Modelling Practice and Theory

Pub Date : 2025-06-07 DOI: 10.1016/j.simpat.2025.103164

R. Mahalakshmi, N. Lalithamani

Gateway Device placement for processing patient data, plays a pivotal role in providing accurate and critical care for patient monitoring and assistance in a connected healthcare system. Long Range Wide Area Network (LoRaWAN) is a wide area network protocol used in providing low-power, seamless communication between the patient health sensors, monitors and the health assistance services in a healthcare network using gateway devices. Objective of the research work is to achieve optimization in the gateway by an adaptive device placement strategy using LoRaWAN thereby improving the network performance. The strategy is a multi-objective optimization, which includes Coverage Maximization, connectivity Optimization, and Energy Minimization for improving the network performance. The gateway placement strategy is verified with the simulation results obtained from Omnet++ Framework for LoRa (FLoRa). The network performance improvement is verified with key metric results observed based on the gateway placement strategy, resulted in Average Energy Consumption being 4.44 mJ, Packet Delivery Ratio (PDR) at 69 %, and Throughput reaching to 481 bps.

用于处理患者数据的网关设备在为连接的医疗保健系统中的患者监测和协助提供准确和关键的护理方面发挥着关键作用。远程广域网（LoRaWAN）是一种广域网协议，用于在使用网关设备的医疗保健网络中的患者健康传感器、监视器和健康辅助服务之间提供低功耗、无缝通信。研究工作的目的是通过采用LoRaWAN的自适应设备放置策略来实现网关的优化，从而提高网络性能。该策略是一个多目标优化，包括覆盖最大化、连通性优化和能量最小化，以提高网络性能。利用omnet++框架对LoRa （FLoRa）的仿真结果验证了网关放置策略。通过基于网关放置策略观察到的关键指标结果验证了网络性能的改进，导致平均能耗为4.44 mJ，分组传递率（PDR）为69%，吞吐量达到481 bps。

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引用次数: 0

A novel open-source framework for performing TSN schedules 一个用于执行TSN调度的新颖的开源框架

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

Simulation Modelling Practice and Theory

Pub Date : 2025-06-06 DOI: 10.1016/j.simpat.2025.103147

Aellison C.T. Santos , Renan M. Silva , Ben Schneider , Malte Wilhelm , Iguatemi E. Fonseca , Vivek Nigam

Due to the complexity of deployed networks, as well as its NP-complete traffic scheduling problem (Craciunas et al., 2016b), Time Sensitive Networking (TSN) configuration is error-prone and challenging when done manually. We present TSNsched, an open-source framework for TSN configuration. The proposed framework has workflows that enable the generation, validation, and deployment of TSN schedules. TSNsched takes as input the network logical topology, expressed as flows, its latency and jitter requirements, generating schedules for TSN switches by reducing different variations of traffic scheduling problems to logical theories that can be automatically solved using Satisfiability Modulo Theories (SMT) solvers. TSNsched provides customized network simulators for validation of the generated schedules. We describe by example how these tool workflows can be used to analyze, validate, and deploy TSN configurations.

由于部署网络的复杂性，以及其NP-complete流量调度问题（Craciunas et al., 2016b），时间敏感网络（TSN）配置在手动完成时容易出错且具有挑战性。我们提出TSNsched，一个用于TSN配置的开源框架。建议的框架具有支持TSN计划的生成、验证和部署的工作流。TSNsched将以流表示的网络逻辑拓扑、时延和抖动需求作为输入，通过将流量调度问题的不同变化形式简化为可以使用可满足模理论（Satisfiability Modulo theories， SMT）求解器自动求解的逻辑理论，生成TSN交换机的调度。TSNsched提供定制的网络模拟器来验证生成的时间表。我们通过示例描述如何使用这些工具工作流来分析、验证和部署TSN配置。

引用次数: 0

Enhanced flame detection in virtual tunnels using DEV-YOLOv8 and digital twin systems 使用DEV-YOLOv8和数字孪生系统增强虚拟隧道中的火焰探测

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

Simulation Modelling Practice and Theory

Pub Date : 2025-06-02 DOI: 10.1016/j.simpat.2025.103143

Yongjiang Li , Xingguo Song , Faming Lin , Xu Fang

Highway tunnel fires pose significant safety challenges requiring reliable detection and response strategies. Traditional testing methods face limitations due to insufficient sample diversity and high costs, thus making virtual environments a promising alternative for algorithm development. This study proposes DEV-YOLOv8, an enhanced YOLOv8 model integrated with digital twin technology for flame detection in simulated tunnel environments. The model incorporates three key enhancements: deformable convolutions for adaptive feature extraction, an efficient multi-scale attention mechanism for stable parameter updates, and a cross-stage partial connections module for efficient multi-scale fusion. A Unity-ROS2-based digital twin system enables cost-effective simulation and dataset generation. Experiments demonstrate DEV-YOLOv8’s superiority with 2.2%/1.5%/1.2% improvements in accuracy/recall/[email protected] respectively, while reducing computational cost by 0.8 GFLOPs. The integrated system achieves 98.21% simulation accuracy and 96.19% physical deployment accuracy, with latency differences

<

3.15 ms and RRT delays below 9.91 ms. This work establishes an efficient framework combining deep learning and digital twins for adaptive fire detection in tunnel environments.

公路隧道火灾带来了重大的安全挑战，需要可靠的探测和响应策略。传统的测试方法由于样本多样性不足和成本高而受到限制，因此虚拟环境成为算法开发的一个有希望的替代方案。本研究提出了DEV-YOLOv8，一种集成了数字孪生技术的增强型YOLOv8模型，用于模拟隧道环境中的火焰探测。该模型包含三个关键的改进：用于自适应特征提取的可变形卷积，用于稳定参数更新的高效多尺度注意机制，以及用于高效多尺度融合的跨阶段部分连接模块。基于unity - ros2的数字孪生系统可实现经济高效的模拟和数据集生成。实验证明了DEV-YOLOv8的优势，在准确率/召回率/[email protected]方面分别提高了2.2%/1.5%/1.2%，同时降低了0.8 GFLOPs的计算成本。集成系统的仿真精度为98.21%，物理部署精度为96.19%，时延差异为<；3.15 ms和RRT延迟低于9.91 ms。这项工作建立了一个结合深度学习和数字孪生的有效框架，用于隧道环境中的自适应火灾探测。

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引用次数: 0

Modeling and simulation of L-shaped merging behavior incorporating behavioral characteristic lines 结合行为特征线的l型合并行为建模与仿真

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

Simulation Modelling Practice and Theory

Pub Date : 2025-05-30 DOI: 10.1016/j.simpat.2025.103150

Wenhang Li , Yi Li , Ping Yu , Jianhua Gong , Haonan Ma

Merging behavior is a common phenomenon in real-life environments that may lead to congestion and even stampedes. In this study, we examined merging behavior in l-shaped corridors and identified phenomena such as behavioral characteristic lines and turning urgency. These findings were incorporated to develop a model for l-shaped merging behavior. Comparisons between real and simulated behaviors show that the model successfully replicates both the spatial and temporal characteristics of the turning and merging processes, validating its applicability. Further analysis of various pedestrian and spatial factors affecting l-shaped merging behavior and traffic efficiency via the model reveals that (1) moderate yielding by pedestrians in the main corridor increases traffic efficiency, while no yielding or excessive yielding reduces efficiency; (2) increasing the desired velocity increases efficiency; however, the benefits diminish beyond velocities of 2.0 m/s; (3) interpersonal distances in both corridors influence the overall traffic efficiency, and maintaining pedestrian spacing at or below 2.0 m is recommended; and (4) larger turning areas increase merging efficiency, indicating that small-scale optimization of key local spaces can significantly increase efficiency. These insights are valuable for managing pedestrian behavior and designing l-shaped corridors.

合并行为是现实生活中常见的现象，可能导致拥堵甚至踩踏。在本研究中，我们研究了l形走廊的合并行为，并发现了行为特征线和转弯紧迫性等现象。这些发现被纳入到l型合并行为的模型中。仿真结果表明，该模型成功地复制了转弯和合并过程的时空特征，验证了该模型的适用性。通过该模型进一步分析了影响l型归并行为和交通效率的各种行人因素和空间因素，结果表明：(1)主干道行人适度让让提高了交通效率，行人不让让或过度让让降低了交通效率；(2)期望速度越大，效率越高；然而，当速度超过2.0 m/s时，这种优势就会减弱；(3)两个廊道的人际距离影响整体交通效率，建议保持行人间距在2.0 m以下；(4)转弯面积越大，合并效率越高，说明局部关键空间的小规模优化可以显著提高合并效率。这些见解对于管理行人行为和设计l形走廊很有价值。

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引用次数: 0

A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks 移动物联网边缘计算网络时空延迟优化的多目标深度强化学习算法

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

Simulation Modelling Practice and Theory

Pub Date : 2025-05-27 DOI: 10.1016/j.simpat.2025.103161

Parisa Khoshvaght , Amir Haider , Amir Masoud Rahmani , Farhad Soleimanian Gharehchopogh , Ferzat Anka , Jan Lansky , Mehdi Hosseinzadeh

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms.

移动物联网（IoT）设备的快速增长需要新的计算框架。这些框架必须满足边缘和移动边缘计算（MEC）系统中严格的延迟和能效要求。包括边缘服务器位置和任务调度时间在内的时空动态构成了一个复杂的优化问题。物联网工作负载的异质性和设备移动性所带来的限制进一步加剧了这些挑战。计算开销和通信挑战之间的平衡也是一个问题。为了解决这些问题，需要在移动物联网和边缘计算环境中采用先进的资源管理和动态任务调度方法。在本文中，我们提出了一种深度强化学习（DRL）多目标算法，称为双深度q -学习（DDQN）框架，该框架增强了用于联邦边缘计算网络的时空移动预测、延迟感知任务卸载和能量约束物联网设备轨迹优化。选择DDQN是因为它具有最优的稳定性和较低的q值高估。该框架采用奖励驱动的优化模型，该模型动态地对延迟敏感的任务进行优先级排序，最大限度地减少任务迁移开销，并在设备和边缘服务器之间平衡能源效率。它集成了动态资源分配算法，以解决随机任务到达模式和实时计算需求。仿真表明，与基准算法相比，端到端延迟减少了35%，能源效率提高了28%，截止日期遗漏率降低了20%。

{"title":"A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks","authors":"Parisa Khoshvaght , Amir Haider , Amir Masoud Rahmani , Farhad Soleimanian Gharehchopogh , Ferzat Anka , Jan Lansky , Mehdi Hosseinzadeh","doi":"10.1016/j.simpat.2025.103161","DOIUrl":"10.1016/j.simpat.2025.103161","url":null,"abstract":"<div><div>The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"143 ","pages":"Article 103161"},"PeriodicalIF":3.5,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital twin-driven imbalanced fault diagnosis method based on new distribution discrepancy metric and large margin aware focal loss 基于新分布差异度量和大裕度感知焦损的数字双驱动不平衡故障诊断方法

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

Simulation Modelling Practice and Theory

Pub Date : 2025-05-27 DOI: 10.1016/j.simpat.2025.103160

Xueyi Li , Daiyou Li , Tianyang Wang , Peng Yuan , Tianyu Yu

In actual industrial production, the frequency of gear failures is much lower than that in normal conditions. The scarcity of fault samples leads to a severe data imbalance problem, which significantly limits the performance of deep learning-based fault diagnosis methods. To address this issue, this paper proposes a digital twin-driven imbalanced fault diagnosis method based on a New Distribution Discrepancy Metric (NDDM) and Large Margin aware Focal (LMF) loss. First, a fault virtual data generation strategy based on digital twin technology is proposed. By analyzing the nonlinear dynamic characteristics of the gearbox, an effective virtual model of the gearbox is established, generating a large amount of high-quality virtual fault data to mitigate the data imbalance problem. Then, the NDDM is employed to simultaneously align the marginal distribution and subdomain conditional distribution by reducing the distribution discrepancy between the virtual and actual domains. Finally, the LMF is adopted to further enhance the model's fault diagnosis performance by focusing on hard samples and preserving more inclusive decision boundaries for fault categories. Experimental validation on two datasets demonstrates that the proposed method significantly outperforms other approaches in handling imbalanced data, providing a novel solution for effective gear fault diagnosis under data imbalance.

在实际工业生产中，齿轮故障的发生频率远低于正常情况下。故障样本的稀缺性导致了严重的数据不平衡问题，极大地限制了基于深度学习的故障诊断方法的性能。针对这一问题，提出了一种基于新分布差异度量（NDDM）和大裕度感知焦点（LMF）损失的双驱动不平衡故障诊断方法。首先，提出了基于数字孪生技术的故障虚拟数据生成策略。通过分析齿轮箱的非线性动态特性，建立了有效的齿轮箱虚拟模型，生成了大量高质量的虚拟故障数据，缓解了数据不平衡问题。然后，通过减小虚拟域与实际域之间的分布差异，利用NDDM对边缘分布和子域条件分布进行同步对齐；最后，通过关注硬样本并为故障类别保留更具包容性的决策边界，采用LMF进一步提高模型的故障诊断性能。在两个数据集上的实验验证表明，该方法在处理不平衡数据方面明显优于其他方法，为数据不平衡下齿轮故障的有效诊断提供了一种新的解决方案。

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引用次数: 0

Numerical study on mechanical response and fracture characteristics of deep marble under intermittent disturbance 间歇扰动下深部大理岩力学响应及断裂特性数值研究

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

Simulation Modelling Practice and Theory

Pub Date : 2025-05-27 DOI: 10.1016/j.simpat.2025.103151

Chengyingjian He, Zhiliang Wang, Chenchen Feng, Jingjing Fu

Engineering disturbance, especially intermittent disturbance caused by blasting or drilling, significantly influences the mechanical properties of rocks. In this study, a series of particle flow simulations were conducted using the Parallel-Bond model to investigate the effect of intermittent loads on the disturbance response of marble at depth (deep marble for short), with a focus on its mechanical behavior, failure mechanism, acoustic emission (AE) and energy evolution characteristics. The results indicate that under intermittent disturbance load, there are inflection points and stress plateaus in the stress-strain curves. As the maximum principal stress σ₁ and disturbance amplitude A_d increase, the stress plateaus are elongated, indicating that the plastic deformation in the σ₁ direction increases. The disturbance effect exhibits the complex interaction between initial strengthening mechanism (such as "dynamic confining pressure effect") and subsequent weakening mechanism. The initial strengthening mechanism is dominant at lower A_d, while the weakening mechanism is gradually enhanced with the increase of A_d. The marble shows a mixed failure mode of tension-shear under different σ₁ and A_d conditions. However, as the intermediate principal stress σ₂ increases, the failure mode transitions from tension-shear mixed failure to shear failure. In the disturbance stage, AE events increase with the increase of σ₁ and decrease with the increase of σ₂. In addition, the fluctuation range of pre-peak strain energy increases with the increase of σ₂. The dissipation energy of the rock specimen increases gradually and accelerates at higher A_d.

工程扰动，特别是爆破或钻孔引起的间歇性扰动，对岩石的力学性能影响很大。本文采用Parallel-Bond模型进行了一系列颗粒流模拟，研究了间歇载荷对大理岩深部扰动响应的影响，重点研究了大理岩的力学行为、破坏机制、声发射（AE）和能量演化特征。结果表明：在间歇性扰动荷载作用下，应力-应变曲线存在拐点和应力高原；随着最大主应力σ1和扰动幅值Ad的增大，应力高原被拉长，表明在σ1方向上塑性变形增大。扰动效应表现为初始强化机制（如“动围压效应”）与后续弱化机制之间复杂的相互作用。低Ad时，初始强化机制占优势，随着Ad的增加，弱化机制逐渐增强。大理岩在不同σ1和Ad条件下均表现为拉剪混合破坏模式。随着中间主应力σ2的增大，破坏模式由拉剪混合破坏转变为剪切破坏。在扰动阶段，声发射事件随σ1的增大而增大，随σ2的增大而减小。峰值前应变能的波动范围随着σ2的增大而增大。岩石试样的耗散能逐渐增大，并在较高的Ad处加速。

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引用次数: 0

A credibility evaluation method for digital twin based on improved evidence theory 基于改进证据理论的数字孪生可信度评估方法

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

Simulation Modelling Practice and Theory

Pub Date : 2025-05-26 DOI: 10.1016/j.simpat.2025.103152

Jianfeng Shi , Qian Zhou , Chengsheng Pan

With the rapid development of digital twin technology, the evaluation of its credibility has become a critical issue that needs to be addressed. Existing evaluation methods struggle to effectively cope with the complex and highly dynamic characteristics of digital twins. Therefore, an improved credibility evaluation method based on evidence theory is proposed to enhance the accuracy and reliability of the evaluation results. Firstly, to address the challenge of inconsistent data types across different evaluation metrics in twin models, a cloud model is utilized to generate a unified basic probability assignment, providing a consistent data foundation for the credibility evaluation method. Secondly, to overcome the limitations of traditional evidence theory in handling evidence conflict and uncertainty, an improved evidence theory-based credibility evaluation method is proposed. This method can address conflict resolution and uncertainty expression by incorporating evidence sufficiency and indicator importance. Finally, by effectively integrating multi-source heterogeneous data, this evaluation method enables an accurate evaluation of the credibility of digital twin. Experimental results demonstrate that the proposed method outperforms traditional approaches in handling evidence conflict, providing a more precise evaluation of digital twin credibility, and offering a more reliable basis and evaluation standard for digital twin system design.

随着数字孪生技术的快速发展，其可信性评价成为一个亟待解决的关键问题。现有的评估方法难以有效地应对数字孪生的复杂和高度动态特征。为此，提出了一种改进的基于证据理论的可信度评估方法，以提高评估结果的准确性和可靠性。首先，针对双模型中不同评价指标数据类型不一致的问题，利用云模型生成统一的基本概率赋值，为可信度评价方法提供一致的数据基础；其次，为了克服传统证据理论在处理证据冲突和不确定性方面的局限性，提出了一种改进的基于证据理论的可信度评估方法。该方法通过结合证据充分性和指标重要性来解决冲突解决和不确定性表达。最后，通过对多源异构数据的有效整合，实现了对数字孪生可信度的准确评估。实验结果表明，该方法在处理证据冲突方面优于传统方法，能够更精确地评估数字孪生的可信度，为数字孪生系统设计提供了更可靠的依据和评估标准。

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引用次数: 0

An Intelligent Pedestrian Model with perception distance and speed to better reproduce traffic dynamics in the single-file movement 具有感知距离和速度的智能行人模型，以更好地再现单队列运动中的交通动态

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

Simulation Modelling Practice and Theory

Pub Date : 2025-05-22 DOI: 10.1016/j.simpat.2025.103145

Ning Guo , Changqing Zhang , Xiang Ling , Jiajia Chen , Chaoyun Wu , Qingyi Hao , Kongjin Zhu

In the pedestrian traffic flow, individuals should continuously adjust the movement speed and direction based on the distance to and speed of the surrounding ones. However, during the interaction process, the pedestrian can hardly obtain the real distance and speed instantly, and he/she has to instead rely on perceptual estimates. Current pedestrian flow models typically overlook this crucial relationship between the real and perceived variables. In this paper, we propose an Intelligent Pedestrian model (IPM) incorporating perception headway and speed in the single-file movement scenarios. Experiments to estimate distance and speed are conducted respectively to establish quantitative functions for perception. Individuals tend to overestimate these two variables on average, with perception deviations exhibiting significant variability. Furthermore, it can effectively capture the headway fluctuation characteristics in the stop-and-go flow. Sensitivity analysis reveals that systematic overestimation or underestimation influences the average speed in the flow, while perception heterogeneity predominantly affects the headway fluctuation patterns.

在行人交通流中，个体应根据与周围人群的距离和速度，不断调整移动速度和方向。然而，在交互过程中，行人很难即时获得真实的距离和速度，只能依靠感知估计。目前的行人流量模型通常忽略了真实变量和感知变量之间的重要关系。在本文中，我们提出了一种包含感知车头时距和速度的智能行人模型（IPM）。分别对距离和速度进行估计实验，建立感知的定量函数。平均而言，个体倾向于高估这两个变量，感知偏差表现出显著的可变性。此外，该方法还能有效捕捉车辆走走停停时的车头时距波动特征。敏感性分析表明，系统的估计过高或过低会影响平均速度，而感知异质性主要影响车头时距波动模式。

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引用次数: 0