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Simulation-based adaptive optimization for passenger flow control measures at metro stations 地铁站客流控制措施的仿真自适应优化
IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-24 DOI: 10.1016/j.simpat.2024.103021
Yiqi Zhou , Maohua Zhong , Zhongwen Li , Xuan Xu , Fucai Hua , Rongliang Pan
Effective passenger flow control measures are essential for the safe operation of metro stations. Existing in-station control measures include adjusting the operation mode of escalators and setting up temporary fences. However, in practice, metro operators often adopt fixed operation modes during fixed periods, indicating that the current passenger flow control measures at metro stations are overly rigidified. Therefore, developing an adaptive control strategy to constantly balance the wildly fluctuating passenger flow and optimize the operation performance is a key issue in current research. In this study, transportation efficiency and congestion risk are selected as evaluation objectives for passenger transportation risk, and passenger flow feature, station structure, and passenger flow control measures are considered key influential factors. Subsequently, an adaptive optimization method integrating simulation and data interpolation is proposed. The software Legion is used to conduct 150 orthogonal simulations, and prediction models for passenger transportation risk are obtained by performing data interpolation on the simulation results. Finally, taking a certain metro station as a case study, the optimal passenger flow control strategy under any passenger flow composition is obtained by scenario acquisition, risk identification, and adaptive decision-making. The results show that setting up temporary fences can reduce the passenger density near the fare gates, while adjusting the running direction of escalators can reduce overcrowding on the platform. Under varying passenger flow composition, the optimal strategy for the current scenario can be obtained, controlling passenger transportation risk within an acceptable range and providing assistance for metro operators in decision-making.
有效的客流控制措施对地铁站的安全运营至关重要。现有的站内控制措施包括调整自动扶梯的运行模式和设置临时围栏。然而,在实际操作中,地铁运营商往往会在固定时段采用固定的运行模式,这表明目前地铁站内的客流控制措施过于僵化。因此,开发一种自适应控制策略来不断平衡剧烈波动的客流并优化运营性能是当前研究的一个关键问题。本研究选择运输效率和拥堵风险作为客流运输风险的评价目标,并将客流特征、车站结构和客流控制措施作为关键影响因素。随后,提出了一种集模拟和数据插值于一体的自适应优化方法。利用软件 Legion 进行 150 次正交模拟,通过对模拟结果进行数据插值,得到客运风险预测模型。最后,以某地铁站为例,通过场景获取、风险识别、自适应决策等方法,得到了任意客流构成下的最优客流控制策略。结果表明,设置临时围栏可以降低检票口附近的客流密度,而调整自动扶梯的运行方向则可以减少站台上的拥挤现象。在客流构成变化的情况下,可以得到当前场景下的最优策略,将客运风险控制在可接受的范围内,为地铁运营商的决策提供帮助。
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引用次数: 0
Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle 车联网中用于多类型缓存放置和多用户计算卸载的双时间尺度资源管理
IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-23 DOI: 10.1016/j.simpat.2024.103025
Dun Cao , Bo Peng , Yubin Wang , Fayez Alqahtani , Jinyu Zhang , Jin Wang
In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.
在车联网(IoV)中,边缘计算可以有效减少任务处理延迟,满足车联网应用的实时需求。然而,由于异构车辆请求对缓存和计算资源的要求各不相同,这对三层云-边缘-端架构的资源管理提出了新的挑战,尤其是当多用户同时卸载任务时。我们的工作综合考虑了多用户部署多种缓存类型的各种场景,以及卸载和更新的不同时间尺度,然后建立了一个联合优化缓存放置、计算卸载和计算资源分配的模型,旨在最大限度地减少整体延迟。同时,为了更好地求解该模型,我们提出了多节点协同缓存、卸载和资源分配算法(MCCO-RAA)。MCCO-RAA 利用双时间尺度来优化问题:在大时间尺度上采用基于贝尔曼优化思想的多节点协作贪婪缓存放置策略,在小时间尺度上采用基于双层迭代深度确定性策略梯度(DDPG)和合作博弈的计算卸载和资源分配策略。实验结果表明,与基线方案相比,我们提出的方案使系统整体延迟时间减少了 28%,并且在不同参数下延迟时间的变化更加平滑。
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引用次数: 0
Underground rescue path planning based on a comprehensive risk assessment approach 基于综合风险评估方法的地下救援路径规划
IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-22 DOI: 10.1016/j.simpat.2024.103022
Li Zhou , Jinqiu Zhao , Binglei Xie , Yong Xu
Fire incidents in underground environments, such as subway stations and shopping malls, pose significant hazards due to restricted ventilation and confined spaces. These conditions complicate rescue operations, particularly given the unpredictable nature of fires. Effective integration of fire risk assessment into rescue path planning is essential for ensuring both safety and operational efficiency. However, fire risk is inherently complex, varying across both temporal and spatial dimensions, and accurate assessment depends on precise fire situation inference. Despite advancements in fire simulation technologies, inconsistencies in geometric structures between computational units limit seamless integration with path planning models. Consequently, many existing studies rely on simplistic and less reliable linear fire inference models, compromising the safety of rescue operations. This paper addresses these challenges by proposing an underground rescue path planning method based on a comprehensive fire risk assessment, aimed at enhancing both safety and operational efficiency. A fire risk assessment approach, driven by fire situation inference, is introduced, employing a novel grid-matching transformation to capture the spatio-temporal dynamics of fire conditions using high-precision simulation software. Additionally, an improved A* algorithm is developed for real-time rescue path optimization, minimizing path risk based on the results of the risk assessment. The proposed method is validated through a detailed case study, demonstrating its effectiveness and reliability.
在地铁站和购物中心等地下环境中发生火灾时,由于通风受限和空间狭小,会造成极大的危险。这些条件使救援行动变得更加复杂,特别是考虑到火灾的不可预测性。将火灾风险评估有效纳入救援路径规划对于确保安全和运营效率至关重要。然而,火灾风险本身就很复杂,在时间和空间维度上都各不相同,准确的评估取决于精确的火灾情况推断。尽管火灾模拟技术不断进步,但计算单元之间几何结构的不一致性限制了与路径规划模型的无缝集成。因此,许多现有研究都依赖于简单且可靠性较低的线性火灾推断模型,从而影响了救援行动的安全性。本文针对这些挑战,提出了一种基于全面火灾风险评估的地下救援路径规划方法,旨在提高安全性和操作效率。本文介绍了一种由火灾情况推断驱动的火灾风险评估方法,采用了一种新颖的网格匹配变换,利用高精度模拟软件捕捉火灾情况的时空动态。此外,还开发了一种改进的 A* 算法,用于实时优化救援路径,根据风险评估结果最大限度地降低路径风险。通过详细的案例研究对所提出的方法进行了验证,证明了其有效性和可靠性。
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引用次数: 0
Security-enabled optimal placement of drone-assisted intelligent transportation systems in mission-critical zones 无人机辅助智能交通系统在关键任务区的安全优化布局
IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-21 DOI: 10.1016/j.simpat.2024.103023
Anu Monisha, K. Murugan
A massive increase in highly dynamic vehicular nodes has resulted in network instability. Owing to the heterogeneous vehicular environment requires a multi-objective solution using a meta-heuristic optimization algorithm in the event of mission-critical zones with poor signal and secured quick decision-making system. Developed a security-enabled optimal placement of Drones or unmanned aerial vehicles (UAV) in mission-critical zones aims to achieve two primary objectives: 1) Maximizing the effectiveness of the intelligent transportation system (ITS) for traffic management and ubiquitous connectivity in mission-critical zones. 2) Ensuring robust security measures to protect sensitive data and infrastructure. This approach represents a cutting-edge solution for optimizing transportation systems in high-risk environments while safeguarding against potential security threats. The pre-deployment of drones and vehicles (VOBU) parameter occurs during the registration phase, and then the mission-critical zone (MCZ) is identified and stored. The optimal position for drones in MCZs is determined by mathematically modeling a golden eagle optimization (GEO), which is inspired by varying the speed at different stages along their spiral trajectory for cruising and hunting. Furthermore, the robustness of the sensitive data and the real identity is ensured by using a biometric-based AKA algorithm utilizing the prevalent real-or-random (ROR) model and the formal security analysis. Based on a comparison of the simulation results, the proposed SDV-GEOAKA scheme outperforms the existing system- STPTC-A2 G, IoDAV, and IMOC with 99.36 % of PDR approximately, whereas, SDV-GEOAKA has maintained a load balancing factor with 0.01 to 0.1 when the transmission range between 0 and 60. When it comes to network coverage, proposed work outperforms with 99.95 % during the transmission range of 50 mW means it uses a minimum number of drones with maximum connectivity within the coverage range and also has significantly reduced the computation overhead and an increase in anomaly detection rate.
高动态车辆节点的大量增加导致了网络的不稳定性。由于车辆环境的异质性,在信号不佳的关键任务区和安全的快速决策系统中,需要使用元启发式优化算法的多目标解决方案。在任务关键区内开发一种支持安全的无人机或无人驾驶飞行器(UAV)优化放置方法,旨在实现两个主要目标:1) 最大限度地提高智能交通系统(ITS)在关键任务区交通管理和泛在连接方面的效率。2) 确保采取强有力的安全措施,保护敏感数据和基础设施。这种方法是在高风险环境中优化交通系统,同时防范潜在安全威胁的尖端解决方案。无人机和车辆(VOBU)的预部署参数发生在注册阶段,然后任务关键区(MCZ)被识别和存储。无人机在 MCZ 中的最佳位置是通过对金雕优化(GEO)进行数学建模确定的,其灵感来自于沿螺旋轨迹在巡航和狩猎的不同阶段改变速度。此外,利用流行的真实或随机(ROR)模型和形式安全分析,通过基于生物特征的 AKA 算法确保敏感数据和真实身份的稳健性。根据仿真结果比较,拟议的 SDV-GEOAKA 方案的 PDR 约为 99.36%,优于现有系统--STPTC-A2 G、IoDAV 和 IMOC,而当传输范围在 0 到 60 之间时,SDV-GEOAKA 保持了 0.01 到 0.1 的负载平衡系数。在网络覆盖方面,拟议的工作在 50 mW 的传输范围内以 99.95 % 的优异成绩胜出,这意味着它使用了最少数量的无人机,在覆盖范围内实现了最大的连通性,同时还显著降低了计算开销,提高了异常检测率。
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引用次数: 0
Driving simulator validation studies: A systematic review 驾驶模拟器验证研究:系统回顾
IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-14 DOI: 10.1016/j.simpat.2024.103020
Siyang Zhang , Chi Zhao , Zherui Zhang , Yecheng Lv
Driving simulators (DS) serve as pivotal platforms for the rigorous testing of transportation systems and vehicles, offering a safe, controllable experimental environment with features like design visualization, scenario virtualization, and test data quantification. The validation of simulator experiments relies on the realism of the driving experience and scenario fidelity, crucial for assessing data reliability and result credibility. With the advent of autonomous driving technologies, the frequency of DS utilization has seen a marked expansion. Nonetheless, the discourse surrounding DS validation remains nascent, lacking a consolidated framework of standards and evaluative methodologies. This review endeavors to synthesize existing scholarly discourse and reports on the validation of driving simulators, further probing into the suitability of various driving scenarios and tasks. Common scenarios include car-following, lane-changing, and acceleration/deceleration, while tasks encompass human-machine co-piloting, takeover scenarios, and emergency evasion, considering driver conditions such as fatigue and distraction. Extracting universal indicators from various scenarios, including longitudinal and lateral velocities, accelerations, and trajectories, the paper summarizes the experimental workflow and commonly used statistical testing methods and psychophysiological monitoring devices for driving simulator validation. Considering the multidimensional factors influencing validation, this study discusses the relationships between simulation fidelity, degrees of freedom (DOF), and simulator sickness, proposing reference standards for driving simulator validation. This effort aims to advance the establishment of evaluation norms for simulation-based transportation and vehicle research, ensuring scientific rigor and empirical validity.
驾驶模拟器(DS)是对运输系统和车辆进行严格测试的关键平台,它提供了一个安全、可控的实验环境,具有设计可视化、场景虚拟化和测试数据量化等功能。模拟器实验的验证依赖于驾驶体验的真实性和场景的保真度,这对于评估数据可靠性和结果可信度至关重要。随着自动驾驶技术的出现,模拟器的使用频率明显增加。然而,围绕自动驾驶系统验证的讨论仍处于起步阶段,缺乏标准和评估方法的综合框架。本综述试图综合现有关于驾驶模拟器验证的学术论述和报告,进一步探究各种驾驶场景和任务的适用性。常见的场景包括跟车、变道和加速/减速,而任务则包括人机协同驾驶、接管场景和紧急避险,并考虑到驾驶员的疲劳和分心等情况。本文从各种场景中提取通用指标,包括纵向和横向速度、加速度和轨迹,总结了用于驾驶模拟器验证的实验工作流程、常用统计测试方法和心理生理监测设备。考虑到影响验证的多维因素,本研究讨论了模拟保真度、自由度(DOF)和模拟器病态之间的关系,提出了驾驶模拟器验证的参考标准。这项工作旨在推动建立基于模拟的交通和车辆研究的评估规范,确保科学的严谨性和经验的有效性。
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引用次数: 0
Machine learning-assisted microscopic public transportation simulation: Two coupling strategies 机器学习辅助的微观公共交通仿真:两种耦合策略
IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-19 DOI: 10.1016/j.simpat.2024.103019
Younes Delhoum, Olivier Cardin, Maroua Nouiri, Mounira Harzallah
Evaluating the performance of public transportation, such as bus lines for example, is a major issue for Public Transportation operators. To be able to integrate specific and local behaviors, microscopic line simulations, modeling each buses on a daily basis, provide actual added value in terms of precision and quality. Carrying out more realistic and accurate simulations requires the use of appropriate parameters. To achieve this, machine learning models trained on real-world data can be used to feed and parameterize simulation models. To address this scientific question, it is necessary to determine how to efficiently integrate machine learning and simulation models. This study aims to couple machine learning and microscopic simulation models using various strategies, evaluate their accuracy and performance and discuss the advantages and drawbacks of each. A case study involving three bus lines was conducted, with results validated against real-world data, showing a good fit for both online and offline strategies. With the best simulation time, good accuracy and adequate travel times and bus punctuality, an offline strategy seems to stand out from other coupling strategies.
对于公共交通运营商来说,评估公共交通(例如公交线路)的性能是一个重要问题。为了能够整合特定的本地行为,微观线路模拟(对每辆公交车进行日常建模)在精度和质量方面提供了实际的附加值。要进行更真实、更准确的模拟,就必须使用适当的参数。为了实现这一目标,可以使用在真实世界数据上训练的机器学习模型来为仿真模型提供信息和参数。为了解决这一科学问题,有必要确定如何有效地整合机器学习和仿真模型。本研究旨在利用各种策略将机器学习和微观仿真模型结合起来,评估它们的准确性和性能,并讨论各自的优缺点。本研究进行了一项涉及三条公交线路的案例研究,研究结果与真实世界的数据进行了验证,显示在线和离线策略都非常适合。离线策略具有最佳的模拟时间、良好的准确性以及充足的旅行时间和公交准点率,似乎在其他耦合策略中脱颖而出。
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引用次数: 0
A novel energy-efficient and cost-effective task offloading approach for UAV-enabled MEC with LEO enhancement in Internet of Remote Things networks 在远程物联网网络中,针对具有低地轨道增强功能的无人机 MEC,采用新型节能、经济高效的任务卸载方法
IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-13 DOI: 10.1016/j.simpat.2024.103018
Amir Masoud Rahmani , Amir Haider , Shtwai Alsubai , Abdullah Alqahtani , Abed Alanazi , Mehdi Hosseinzadeh
The Internet of Remote Things (IoRT) involves networks of devices deployed in extensive and often remote areas, collecting data for transmission and processing. In such networks, Unmanned Aerial Vehicles (UAVs) gather data, which is then sent to Low Earth Orbit (LEO) satellites for processing. These systems often face significant challenges, particularly in task offloading. Conventional methods typically rely on static routing and scheduling algorithms that do not adapt to changing conditions and usually overlook the complexity of dynamic decision-making in harsh or isolated environments, thus failing to address the critical challenges of energy efficiency and latency. In this paper, we introduce a method comprised of a three-layer architecture. The first layer, the IoRT computing layer, uses Deep Q-Network (DQN) to optimize local decisions based on device constraints and task urgency. The second layer features UAVs serve as Mobile Edge Computing (MEC), which not only processes data but also decides whether to process tasks locally or offload them to LEO satellites, utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for this decision-making process. The third LEO Satellite Layer has a high computational capacity to handle offloaded tasks. Simulation results demonstrate notable improvements: compared to another method, the proposed model shows a 14.73 % reduction in energy consumption and a 23.13 % decrease in latency while reducing execution costs by an average of 28.7 %.
远程物联网(IoRT)涉及部署在广阔且往往偏远地区的设备网络,收集数据并进行传输和处理。在这些网络中,无人飞行器(UAV)收集数据,然后发送到低地轨道(LEO)卫星进行处理。这些系统往往面临巨大挑战,特别是在任务卸载方面。传统方法通常依赖于静态路由和调度算法,这种算法不能适应不断变化的条件,通常会忽略恶劣或孤立环境中动态决策的复杂性,因此无法解决能效和延迟等关键挑战。在本文中,我们介绍了一种由三层架构组成的方法。第一层是 IoRT 计算层,使用深度 Q 网络(DQN)根据设备限制和任务紧迫性优化本地决策。第二层的特点是无人机作为移动边缘计算(MEC),它不仅处理数据,还决定是在本地处理任务还是将任务卸载到低地轨道卫星上,在此决策过程中使用了与理想解决方案相似度排序偏好技术(TOPSIS)。第三个低地轨道卫星层具有很高的计算能力,可处理卸载任务。仿真结果显示了显著的改进:与另一种方法相比,所提出的模型显示能耗降低了 14.73%,延迟降低了 23.13%,同时执行成本平均降低了 28.7%。
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引用次数: 0
An AI-driven solution to prevent adversarial attacks on mobile Vehicle-to-Microgrid services 人工智能驱动的解决方案,防止对移动车对微电网服务的恶意攻击
IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-10 DOI: 10.1016/j.simpat.2024.103016
Ahmed Omara, Burak Kantarci

With the increasing integration of Artificial Intelligence (AI) in microgrid control systems, there is a risk that malicious actors may exploit vulnerabilities in machine learning algorithms to disrupt power generation and distribution. In this work, we study the potential impacts of adversarial attacks on Vehicle-to-Microgrid (V2M), and discuss potential defensive countermeasures to prevent these risks. Our analysis shows that the decentralized and adaptive nature of microgrids makes them particularly vulnerable to adversarial attacks, and highlights the need for robust security measures to protect against such threats. We propose a framework to detect and prevent adversarial attacks on V2M services using Generative Adversarial Network (GAN) model and a Machine Learning (ML) classifier. We focus on two adversarial attacks, namely inference and evasion attacks. We test our proposed framework under three attack scenarios to ensure the robustness of our solution. As the adversary’s knowledge of a system determines the success of the executed attacks, we study four gray-box cases where the adversary has access to different percentages of the victim’s training dataset. Moreover, we compare our proposed detection method against four benchmark detectors. Furthermore, we evaluate the effectiveness of our proposed method to detect three benchmark evasion attack. Through simulations, we show that all benchmark detectors fail to successfully detect adversarial attacks, particularly when the attacks are intelligently augmented, obtaining an Adversarial Detection Rate (ADR) of up to 60.4%. On the other hand, our proposed framework outperforms the other detectors and achieves an ADR of 92.5%.

随着人工智能(AI)越来越多地融入微电网控制系统,恶意行为者有可能利用机器学习算法中的漏洞破坏发电和配电。在这项工作中,我们研究了对抗性攻击对车对微电网(V2M)的潜在影响,并讨论了防止这些风险的潜在防御对策。我们的分析表明,微电网的分散性和自适应性使其特别容易受到恶意攻击,并强调了采取强有力的安全措施防范此类威胁的必要性。我们提出了一个框架,利用生成式对抗网络(GAN)模型和机器学习(ML)分类器来检测和预防针对 V2M 服务的对抗性攻击。我们重点关注两种对抗性攻击,即推理攻击和规避攻击。我们在三种攻击场景下测试了我们提出的框架,以确保我们的解决方案的鲁棒性。由于对手对系统的了解程度决定了所实施攻击的成功与否,因此我们研究了四种灰盒情况,即对手可以访问受害者训练数据集的不同百分比。此外,我们还将我们提出的检测方法与四种基准检测器进行了比较。此外,我们还评估了我们提出的方法在检测三种基准规避攻击方面的有效性。通过模拟,我们发现所有基准检测器都无法成功检测出对抗性攻击,尤其是当攻击被智能增强时,对抗性检测率(ADR)高达 60.4%。另一方面,我们提出的框架优于其他检测器,ADR 高达 92.5%。
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引用次数: 0
Advancements in traffic simulation for enhanced road safety: A review 交通仿真技术在加强道路安全方面的进展:综述
IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-10 DOI: 10.1016/j.simpat.2024.103017
Aliyu Mustapha , Ahmad Majdi Abdul-Rani , Noorhayati Saad , Mazli Mustapha

Traffic simulation techniques play a crucial role in transportation engineering, offering a sophisticated framework for analysing the intricate dynamics within transportation systems. This paper thoroughly reviews the latest developments in traffic simulation techniques and their applications in improving road safety. Drawing from a comprehensive analysis of recent literature from the Scopus database spanning 2014 to 2024, this review highlights the various analytic methods employed in traffic simulation and their practical applications. Focusing mainly on microsimulation techniques, the study underscores their ability to provide proactive and reactive surrogate safety measures, offering stakeholders valuable insights into traffic safety dynamics. Leveraging methodologies such as microsimulation modelling, surrogate safety measures, statistical model creation, simulation-based conflict prediction, and sensitivity analysis, contemporary research aims to address safety concerns comprehensively. However, the absence of comprehensive crash simulation models presents a significant challenge, raising doubts about the efficacy of traffic simulation in road safety assessment. To overcome this challenge, interdisciplinary research is essential to develop practical solutions that harness technological advancements and foster collaboration across domains. By overcoming existing limitations and refining methodologies, researchers can pave the way for more robust and comprehensive approaches to traffic safety evaluation, contributing significantly to the global goal of enhancing road safety.

交通模拟技术在交通工程中发挥着至关重要的作用,为分析交通系统内部错综复杂的动态提供了一个复杂的框架。本文全面回顾了交通仿真技术的最新发展及其在改善道路安全方面的应用。通过对 Scopus 数据库中 2014 年至 2024 年的最新文献进行全面分析,本综述重点介绍了交通模拟中采用的各种分析方法及其实际应用。本研究主要关注微观模拟技术,强调其提供主动和被动替代安全措施的能力,为利益相关者提供有关交通安全动态的宝贵见解。利用微观模拟建模、替代安全措施、统计模型创建、基于模拟的冲突预测和敏感性分析等方法,当代研究旨在全面解决安全问题。然而,缺乏全面的碰撞模拟模型是一个重大挑战,使人们对交通模拟在道路安全评估中的功效产生怀疑。为了克服这一挑战,必须开展跨学科研究,利用技术进步和跨领域合作开发实用的解决方案。通过克服现有的局限性和改进方法,研究人员可以为更稳健、更全面的交通安全评估方法铺平道路,为实现加强道路安全的全球目标做出重大贡献。
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引用次数: 0
Investigation on directional rock fracture mechanism under instantaneous expansion from the perspective of damage mechanics: A 3-D simulation 从损伤力学角度研究瞬时膨胀下的定向岩石断裂机制三维模拟
IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-05 DOI: 10.1016/j.simpat.2024.103013
Shan Guo , Seokwon Jeon , Quan Zhang , Manchao He , Jianning Liu , Chao Wang , Qun Sui

With developments in geotechnical engineering, directional rock-breaking technology has been applied in large quantities. As a novel non-explosive rock-breaking technology, Instantaneous Expansion with a Single Crack (IESC) has been studied and applied to some extent in the past few years. IESC uses expansion gas to fracture rock mass in the predetermined direction by a special energy-gathering tube, which has the advantages of high safety, strong directional ability, and easy to operate. At present, there is a lack of in-depth investigation on the directional fracture mechanism of rock under the action of IESC. According to damage mechanics, the fundamental reason for rock fracture is due to the initiation, expansion, and penetration of internal cracks. In this study, a 3-D numerical model based on the theory of progressive failure is established to study the directional rock fracture mechanism of IESC, while a Conventional Expansion (CE) model without energy-gathering tube is established for comparative research. The maximum tensile stress criterion and unified strength criterion are used to identify damage failure of the element. The evolution processes of four key parameters are simulated, the types and degrees of tensile/compressive damage of the unit are analyzed, which aims to decipher the model's directional fracture mechanism under IESC loading. The established 3-D numerical models are validated by comparing with experimental results. The research results can contribute to further understanding the directional rock fracture mechanism of IESC and provide a theoretical basis for the application of IESC in the field.

随着岩土工程的发展,定向破岩技术得到了大量应用。作为一种新型的非爆炸性破岩技术,单裂缝瞬时膨胀技术(IESC)在过去几年中得到了一定程度的研究和应用。单裂缝瞬时膨胀破岩技术是利用膨胀气体通过特殊的集能管使岩体按预定方向破裂,具有安全性高、定向能力强、操作简便等优点。目前,对 IESC 作用下岩石定向断裂机理还缺乏深入研究。根据损伤力学,岩石断裂的根本原因是内部裂缝的产生、扩展和渗透。本研究建立了基于渐进破坏理论的三维数值模型来研究 IESC 作用下岩石的定向断裂机理,同时建立了不含集能管的常规膨胀(CE)模型进行对比研究。采用最大拉应力准则和统一强度准则来识别该元件的破坏失效。模拟了四个关键参数的演变过程,分析了单元的拉伸/压缩损伤类型和程度,旨在破解模型在 IESC 载荷作用下的定向断裂机理。通过与实验结果对比,验证了所建立的三维数值模型。研究成果有助于进一步理解 IESC 的定向岩石断裂机理,为 IESC 在野外的应用提供理论依据。
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Simulation Modelling Practice and Theory
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