Journal of King Saud University-Computer and Information Sciences最新文献_第6页

Quantum computing enhanced knowledge tracing: Personalized KT research for mitigating data sparsity 量子计算增强知识追踪：缓解数据稀疏性的个性化 KT 研究

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 Epub Date: 2024-10-24 DOI: 10.1016/j.jksuci.2024.102224

Chengke Bao , Qianxi Wu , Weidong Ji , Min Wang , Haoyu Wang

With the development of artificial intelligence in education, knowledge tracing (KT) has become a current research hotspot and is the key to the success of personalized instruction. However, data sparsity remains a significant challenge in the KT domain. To address this challenge, this paper applies quantum computing (QC) technology to KT for the first time. It proposes two personalized KT models incorporating quantum mechanics (QM): quantum convolutional enhanced knowledge tracing (QCE-KT) and quantum variational enhanced knowledge tracing (QVE-KT). Through quantum superposition and entanglement properties, QCE-KT and QVE-KT effectively alleviate the data sparsity problem in the KT domain through quantum convolutional layers and variational quantum circuits, respectively, and significantly improve the quality of the representation and prediction accuracy of students’ knowledge states. Experiments on three datasets show that our models outperform ten benchmark models. On the most sparse dataset, QCE-KT and QVE-KT improve their performance by 16.44% and 14.78%, respectively, compared to DKT. Although QC is still in the developmental stage, this study reveals the great potential of QM in personalized KT, which provides new perspectives for solving personalized instruction problems and opens up new directions for applying QC in education.

随着人工智能在教育领域的发展，知识追踪（KT）已成为当前的研究热点，也是个性化教学成功的关键。然而，数据稀疏性仍然是知识追踪领域的一个重大挑战。为应对这一挑战，本文首次将量子计算（QC）技术应用于 KT。它提出了两种结合量子力学（QM）的个性化知识追踪模型：量子卷积增强知识追踪（QCE-KT）和量子变分增强知识追踪（QVE-KT）。通过量子叠加和纠缠特性，QCE-KT 和 QVE-KT 分别通过量子卷积层和量子变分电路有效缓解了知识追踪领域的数据稀疏性问题，显著提高了学生知识状态的表征质量和预测精度。三个数据集的实验表明，我们的模型优于十个基准模型。在最稀疏的数据集上，QCE-KT 和 QVE-KT 的性能比 DKT 分别提高了 16.44% 和 14.78%。虽然 QC 仍处于发展阶段，但本研究揭示了 QM 在个性化 KT 中的巨大潜力，为解决个性化教学问题提供了新的视角，也为 QC 在教育领域的应用开辟了新的方向。

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引用次数: 0

DA-Net: A classification-guided network for dental anomaly detection from dental and maxillofacial images DA-Net：从牙科和颌面部图像中检测牙科异常的分类指导网络

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 Epub Date: 2024-10-30 DOI: 10.1016/j.jksuci.2024.102229

Jiaxing Li

Dental abnormalities (DA) are frequent signs of disorders of the mouth that cause discomfort, infection, and loss of teeth. Early and reasonably priced treatment may be possible if defective teeth in the oral cavity are automatically detected. Several research works have endeavored to create a potent deep learning model capable of identifying DA from pictures. However, because of the following problems, aberrant teeth from the oral cavity are difficult to detect: 1) Normal teeth and crowded dentition frequently overlap; 2) The lesion area on the tooth surface is tiny. This paper proposes a professional dental anomaly detection network (DA-Net) to address such issues. First, a multi-scale dense connection module (MSDC) is designed to distinguish crowded teeth from normal teeth by learning multi-scale spatial information of dentition. Then, a pixel differential convolution (PDC) module is designed to perform pathological tooth recognition by extracting small lesion features. Finally, a multi-stage convolutional attention module (MSCA) is developed to integrate spatial information and channel information to obtain abnormal teeth in small areas. Experiments on benchmarks show that DA-Net performs well in dental anomaly detection and can further assist doctors in making treatment plans. Specifically, the DA-Net method performs best on multiple detection evaluation metrics: IoU, PRE, REC, and mAP. In terms of REC and mAP indicators, the proposed DA-Net method is 1.1% and 1.3% higher than the second-ranked YOLOv7 method.

牙齿异常（DA）是口腔疾病的常见征兆，会引起不适、感染和牙齿脱落。如果能自动检测出口腔中存在缺陷的牙齿，就可以及早进行价格合理的治疗。一些研究工作致力于创建一个强大的深度学习模型，能够从图片中识别牙齿缺损。然而，由于以下问题，口腔畸形牙难以检测：1）正常牙齿和拥挤牙经常重叠；2）牙齿表面的病变面积很小。针对这些问题，本文提出了一种专业的牙齿异常检测网络（DA-Net）。首先，设计了一个多尺度密集连接模块（MSDC），通过学习牙列的多尺度空间信息来区分拥挤牙和正常牙。然后，设计了一个像素差分卷积（PDC）模块，通过提取小病变特征来进行病牙识别。最后，开发了多级卷积注意力模块（MSCA），以整合空间信息和通道信息，从而获得小区域的异常牙齿。基准实验表明，DA-Net 在牙齿异常检测方面表现出色，可以进一步帮助医生制定治疗方案。具体来说，DA-Net 方法在多个检测评估指标上表现最佳：IoU、PRE、REC 和 mAP。在 REC 和 mAP 指标上，DA-Net 方法比排名第二的 YOLOv7 方法分别高出 1.1% 和 1.3%。

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引用次数: 0

Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis 增强型 UrduAspectNet：利用双峰注意力实现卓越的基于方面的情感分析

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 Epub Date: 2024-10-23 DOI: 10.1016/j.jksuci.2024.102221

Kamran Aziz , Naveed Ahmed , Hassan Jalil Hadi , Aizihaierjiang Yusufu , Mohammaed Ali Alshara , Yasir Javed , Donghong Ji

Urdu, with its rich linguistic complexity, poses significant challenges for computational sentiment analysis. This study presents an enhanced version of UrduAspectNet, specifically designed for Aspect-Based Sentiment Analysis (ABSA) in Urdu. We introduce key innovations including the incorporation of Biaffine Attention into the model architecture, which synergizes XLM-R embeddings, a bidirectional LSTM (BiLSTM), and dual Graph Convolutional Networks (GCNs). Additionally, we utilize dependency parsing to create the adjacency matrix for the GCNs, capturing syntactic dependencies to enhance relational representation. The improved model, termed Enhanced UrduAspectNet, integrates POS and lemma embeddings, processed through BiLSTM and GCN layers, with Biaffine Attention enhancing the extraction of intricate aspect and sentiment relationships. We also introduce the use of BIO tags for aspect term identification, improving the granularity of aspect extraction. Experimental results demonstrate significant improvements in both aspect extraction and sentiment classification accuracy. This research advances Urdu sentiment analysis and sets a precedent for leveraging sophisticated NLP techniques in underrepresented languages.

乌尔都语具有丰富的语言复杂性，给计算情感分析带来了巨大挑战。本研究介绍了 UrduAspectNet 的增强版，该版本专为基于方面的乌尔都语情感分析 (ABSA) 而设计。我们引入了一些关键的创新，包括在模型架构中加入 Biaffine Attention，使 XLM-R 嵌入、双向 LSTM（BiLSTM）和双图卷积网络（GCN）协同增效。此外，我们还利用依赖性解析为 GCNs 创建邻接矩阵，捕捉句法依赖性以增强关系表示。改进后的模型被称为 "增强型 UrduAspectNet"，它将通过 BiLSTM 和 GCN 层处理的 POS 和词素嵌入与 Biaffine Attention 整合在一起，从而增强了对错综复杂的方面和情感关系的提取。我们还引入了 BIO 标签用于方面术语识别，从而提高了方面提取的粒度。实验结果表明，方面提取和情感分类的准确性都有显著提高。这项研究推动了乌尔都语情感分析的发展，为在代表性不足的语言中利用复杂的 NLP 技术开创了先例。

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引用次数: 0

Dual-stream dynamic graph structure network for document-level relation extraction 用于文档级关系提取的双流动态图结构网络

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 Epub Date: 2024-10-03 DOI: 10.1016/j.jksuci.2024.102202

Yu Zhong, Bo Shen

Extracting structured information from unstructured text is crucial for knowledge management and utilization, which is the goal of document-level relation extraction. Existing graph-based methods face issues with information confusion and integration, limiting the reasoning capabilities of the model. To tackle this problem, a dual-stream dynamic graph structural network is proposed to model documents from various perspectives. Leveraging the richness of document information, a static document heterogeneous graph is constructed. A dynamic heterogeneous document graph is then induced based on this foundation to facilitate global information aggregation for entity representation learning. Additionally, the static document graph is decomposed into multi-level static semantic graphs, and multi-layer dynamic semantic graphs are further induced, explicitly segregating information from different levels. Information from different streams is effectively integrated via an information integrator. To mitigate the interference of noise during the reasoning process, a noise regularization mechanism is also designed. The experimental results on three extensively utilized publicly accessible datasets for document-level relation extraction demonstrate that our model achieves F1 scores of 62.56%, 71.1%, and 86.9% on the DocRED, CDR, and GDA datasets, respectively, significantly outperforming the baselines. Further analysis also demonstrates the effectiveness of the model in multi-entity scenarios.

从非结构化文本中提取结构化信息对于知识管理和利用至关重要，这也是文档级关系提取的目标。现有的基于图的方法面临着信息混淆和整合的问题，限制了模型的推理能力。为解决这一问题，我们提出了一种双流动态图结构网络，从不同角度对文档进行建模。利用丰富的文档信息，构建静态文档异构图。然后在此基础上诱导出动态异构文档图，以促进实体表征学习的全局信息聚合。此外，静态文档图被分解成多层次的静态语义图，并进一步诱导出多层次的动态语义图，明确分离来自不同层次的信息。来自不同信息流的信息通过信息集成器进行有效集成。为了减少推理过程中的噪声干扰，还设计了噪声正则化机制。在三个广泛使用的公开文档级关系提取数据集上的实验结果表明，我们的模型在 DocRED、CDR 和 GDA 数据集上的 F1 分数分别达到了 62.56%、71.1% 和 86.9%，明显优于基线模型。进一步的分析还证明了该模型在多实体场景中的有效性。

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引用次数: 0

The evolution of the flip-it game in cybersecurity: Insights from the past to the future 网络安全翻转游戏的演变：从过去到未来的启示

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 Epub Date: 2024-09-25 DOI: 10.1016/j.jksuci.2024.102195

Mousa Tayseer Jafar , Lu-Xing Yang , Gang Li , Xiaofan Yang

Cybercrime statistics highlight the severe and growing impact of digital threats on individuals and organizations, with financial losses escalating rapidly. As cybersecurity becomes a central challenge, several modern cyber defense strategies prove insufficient for effectively countering the threats posed by sophisticated attackers. Despite advancements in cybersecurity, many existing frameworks often lack the capacity to address the evolving tactics of adept adversaries. With cyber threats growing in sophistication and diversity, there is a growing acknowledgment of the shortcomings within current defense strategies, underscoring the need for more robust and innovative solutions. To develop resilient cyber defense strategies, it remains essential to simulate the dynamic interaction between sophisticated attackers and system defenders. Such simulations enable organizations to anticipate and effectively counter emerging threats. The Flip-It game is recognized as an intelligent simulation game for capturing the dynamic interplay between sophisticated attackers and system defenders. It provides the capability to emulate intricate cyber scenarios, allowing organizations to assess their defensive capabilities against evolving threats, analyze vulnerabilities, and improve their response strategies by simulating real-world cyber scenarios. This paper provides a comprehensive analysis of the Flip-It game in the context of cybersecurity, tracing its development from inception to future prospects. It highlights significant contributions and identifies potential future research avenues for scholars in the field. This study aims to deliver a thorough understanding of the Flip-It game’s progression, serving as a valuable resource for researchers and practitioners involved in cybersecurity strategy and defense mechanisms.

网络犯罪统计数据凸显了数字威胁对个人和组织的严重影响，而且这种影响还在不断加剧，经济损失也在迅速攀升。随着网络安全成为一项核心挑战，一些现代网络防御战略被证明不足以有效应对复杂攻击者带来的威胁。尽管网络安全技术在不断进步，但许多现有框架往往无法应对精明对手不断变化的战术。随着网络威胁的复杂性和多样性不断增加，人们越来越认识到当前防御战略的不足之处，强调需要更强大和创新的解决方案。要制定有弹性的网络防御战略，模拟复杂的攻击者和系统防御者之间的动态互动仍然至关重要。这种模拟使组织能够预测并有效应对新出现的威胁。Flip-It 游戏是公认的捕捉复杂攻击者和系统防御者之间动态互动的智能模拟游戏。它能够模拟错综复杂的网络场景，使企业能够通过模拟真实世界的网络场景，评估其针对不断演变的威胁的防御能力、分析漏洞并改进应对策略。本文全面分析了网络安全背景下的 "Flip-It "游戏，追溯了它从诞生到未来的发展前景。论文强调了该游戏的重大贡献，并为该领域的学者指出了潜在的未来研究途径。本研究旨在全面了解 Flip-It 游戏的发展过程，为网络安全战略和防御机制方面的研究人员和从业人员提供有价值的资源。

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引用次数: 0

A truthful randomized mechanism for task allocation with multi-attributes in mobile edge computing 移动边缘计算中多属性任务分配的真实随机机制

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 Epub Date: 2024-09-26 DOI: 10.1016/j.jksuci.2024.102196

Xi Liu , Jun Liu

Mobile Edge Computing (MEC) aims at decreasing the response time and energy consumption of running mobile applications by offloading the tasks of mobile devices (MDs) to the MEC servers located at the edge of the network. The demands are multi-attribute, where the distances between MDs and access points lead to differences in required resources and transmission energy consumption. Unfortunately, the existing works have not considered both task allocation and energy consumption problems. Motivated by this, this paper considers the problem of task allocation with multi-attributes, where the problem consists of the winner determination and offloading decision problems. First, the problem is formulated as the auction-based model to provide flexible service. Then, a randomized mechanism is designed and is truthful in expectation. This drives the system into an equilibrium where no MD has incentives to increase the utility by declaring an untrue value. In addition, an approximation algorithm is proposed to minimize remote energy consumption and is a polynomial-time approximation scheme. Therefore, it achieves a tradeoff between optimality loss and time complexity. Simulation results reveal that the proposed mechanism gets the near-optimal allocation. Furthermore, compared with the baseline methods, the proposed mechanism can effectively increase social welfare and bring higher revenue to edge server providers.

移动边缘计算（MEC）旨在将移动设备（MD）的任务卸载到位于网络边缘的 MEC 服务器上，从而缩短移动应用程序的响应时间并降低能耗。需求是多属性的，移动设备和接入点之间的距离会导致所需资源和传输能耗的差异。遗憾的是，现有研究并未同时考虑任务分配和能耗问题。受此启发，本文考虑了多属性的任务分配问题，该问题包括获胜者确定和卸载决策问题。首先，将问题表述为基于拍卖的模型，以提供灵活的服务。然后，设计了一种随机机制，该机制在预期中是真实的。这就促使系统进入一个均衡状态，在此状态下，任何 MD 都没有动机通过宣布一个不真实的值来增加效用。此外，还提出了一种近似算法，以尽量减少远程能耗，这是一种多项式时间近似方案。因此，它实现了优化损失和时间复杂性之间的权衡。仿真结果表明，提出的机制获得了接近最优的分配。此外，与基线方法相比，建议的机制能有效提高社会福利，并为边缘服务器提供商带来更高的收益。

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引用次数: 0

Firefly forest: A swarm iteration-free swarm intelligence clustering algorithm 萤火虫森林无迭代群集智能聚类算法

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 Epub Date: 2024-10-18 DOI: 10.1016/j.jksuci.2024.102219

Shijie Zeng , Yuefei Wang , Yukun Wen , Xi Yu , Binxiong Li , Zixu Wang

The Firefly Forest algorithm is a novel bio-inspired clustering method designed to address key challenges in traditional clustering techniques, such as the need to set a fixed number of neighbors, predefine cluster numbers, and rely on computationally intensive swarm iterative processes. The algorithm begins by using an adaptive neighbor estimation, refined to filter outliers, to determine the brightness of each firefly. This brightness guides the formation of firefly trees, which are then merged into cohesive firefly forests, completing the clustering process. This approach allows the algorithm to dynamically capture both local and global patterns, eliminate the need for predefined cluster numbers, and operate with low computational complexity. Experiments involving 14 established clustering algorithms across 19 diverse datasets, using 8 evaluative metrics, demonstrate the Firefly Forest algorithm’s superior accuracy and robustness. These results highlight its potential as a powerful tool for real-world clustering applications. Our code is available at: https://github.com/firesaku/FireflyForest.

萤火虫森林算法是一种新颖的生物启发聚类方法，旨在解决传统聚类技术面临的主要挑战，如需要设置固定的邻居数量、预先确定聚类数量，以及依赖计算密集型的蜂群迭代过程。该算法首先使用自适应邻居估计，并对其进行改进以过滤异常值，从而确定每个萤火虫的亮度。这种亮度会引导萤火虫树的形成，然后将其合并成有凝聚力的萤火虫森林，完成聚类过程。这种方法允许算法动态捕捉局部和全局模式，无需预定义的聚类数量，并且计算复杂度低。在 19 个不同的数据集上使用 14 种成熟的聚类算法，并使用 8 个评估指标进行实验，结果表明萤火虫森林算法具有卓越的准确性和鲁棒性。这些结果凸显了萤火虫森林算法作为现实世界聚类应用的强大工具的潜力。我们的代码可在以下网址获取：https://github.com/firesaku/FireflyForest。

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引用次数: 0

Software requirement engineering over the federated environment in distributed software development process 分布式软件开发过程中联合环境下的软件需求工程

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-11-01 Epub Date: 2024-09-28 DOI: 10.1016/j.jksuci.2024.102201

Abdulaziz Alhumam, Shakeel Ahmed

In the recent past, the distributed software development (DSD) process has become increasingly prevalent with the rapid evolution of the software development process. This transformation would necessitate a robust framework for software requirement engineering (SRE) to work in federated environments. Using the federated environment, multiple independent software entities would work together to develop software, often across organizations and geographical borders. The decentralized structure of the federated architecture makes requirement elicitation, analysis, specification, validation, and administration more effective. The proposed model emphasizes flexibility and agility, leveraging the collaboration of multiple localized models within a diversified development framework. This collaborative approach is designed to integrate the strengths of each local process, ultimately resulting in the creation of a robust software prototype. The performance of the proposed DSD model is evaluated using two case studies on the E-Commerce website and the Learning Management system. The proposed model is analyzed by considering divergent functional and non-functional requirements for each of the case studies and analyzing the performance using standardized metrics like mean square error (MSE), mean absolute error (MAE), and Pearson Correlation Coefficient (PCC). It is observed that the proposed model exhibited a reasonable performance with an MSE value of 0.12 and 0.153 for both functional and non-functional requirements, respectively, and an MAE value of 0.222 and 0.232 for both functional and non-functional requirements, respectively.

近年来，随着软件开发流程的快速发展，分布式软件开发（DSD）流程变得越来越普遍。这种转变需要一个强大的软件需求工程（SRE）框架，以便在联合环境中工作。利用联盟环境，多个独立的软件实体将共同开发软件，而且往往跨越组织和地理边界。联合架构的分散结构使需求激发、分析、规范、验证和管理更加有效。建议的模式强调灵活性和敏捷性，在一个多样化的开发框架内利用多个本地化模型的协作。这种协作方法旨在整合每个本地流程的优势，最终创建一个强大的软件原型。通过对电子商务网站和学习管理系统的两个案例研究，对所提出的 DSD 模型的性能进行了评估。通过考虑每个案例研究的不同功能和非功能需求，并使用均方误差 (MSE)、平均绝对误差 (MAE) 和皮尔逊相关系数 (PCC) 等标准化指标分析了所提出模型的性能。结果表明，所提出的模型表现出合理的性能，对功能性和非功能性需求的 MSE 值分别为 0.12 和 0.153，对功能性和非功能性需求的 MAE 值分别为 0.222 和 0.232。

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引用次数: 0

Systematic review of deep learning solutions for malware detection and forensic analysis in IoT 对用于物联网恶意软件检测和取证分析的深度学习解决方案进行系统审查

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-10-01 Epub Date: 2024-08-27 DOI: 10.1016/j.jksuci.2024.102164

Siraj Uddin Qureshi , Jingsha He , Saima Tunio , Nafei Zhu , Ahsan Nazir , Ahsan Wajahat , Faheem Ullah , Abdul Wadud

The swift proliferation of Internet of Things (IoT) devices has presented considerable challenges in maintaining cybersecurity. As IoT ecosystems expand, they increasingly attract malware attacks, necessitating advanced detection and forensic analysis methods. This systematic review explores the application of deep learning techniques for malware detection and forensic analysis within IoT environments. The literature is organized into four distinct categories: IoT Security, Malware Forensics, Deep Learning, and Anti-Forensics. Each group was analyzed individually to identify common methodologies, techniques, and outcomes. Conducted a combined analysis to synthesize the findings across these categories, highlighting overarching trends and insights.This systematic review identifies several research gaps, including the need for comprehensive IoT-specific datasets, the integration of interdisciplinary methods, scalable real-time detection solutions, and advanced countermeasures against anti-forensic techniques. The primary issue addressed is the complexity of IoT malware and the limitations of current forensic methodologies. Through a robust methodological framework, this review synthesizes findings across these categories, highlighting common methodologies and outcomes. Identifying critical areas for future investigation, this review contributes to the advancement of cybersecurity in IoT environments, offering a comprehensive framework to guide future research and practice in developing more robust and effective security solutions.

物联网（IoT）设备的迅速扩散给维护网络安全带来了巨大挑战。随着物联网生态系统的扩展，它们越来越多地吸引恶意软件攻击，因此需要先进的检测和取证分析方法。本系统综述探讨了深度学习技术在物联网环境下恶意软件检测和取证分析中的应用。文献分为四个不同的类别：物联网安全、恶意软件取证、深度学习和反取证。对每一组进行了单独分析，以确定共同的方法、技术和结果。本系统综述确定了几项研究空白，包括需要全面的物联网特定数据集、跨学科方法的整合、可扩展的实时检测解决方案以及针对反取证技术的先进对策。研究的主要问题是物联网恶意软件的复杂性和当前取证方法的局限性。通过一个强大的方法论框架，本综述综合了这些类别的研究结果，突出了共同的方法和成果。本综述确定了未来调查的关键领域，为推进物联网环境中的网络安全做出了贡献，提供了一个全面的框架，指导未来的研究和实践，以开发更强大、更有效的安全解决方案。

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引用次数: 0

Flow prediction of mountain cities arterial road network for real-time regulation 山区城市干线路网流量预测与实时调控

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-10-01 Epub Date: 2024-09-25 DOI: 10.1016/j.jksuci.2024.102190

Xiaoyu Cai , Zimu Li , Jiajia Dai , Liang Lv , Bo Peng

This study aims to enhance the understanding of vehicle path selection behavior within arterial road networks by investigating the influencing factors and analyzing spatial and temporal traffic flow distributions. Using radio frequency identification (RFID) travel data, key factors such as travel duration, route familiarity, route length, expressway ratio, arterial road ratio, and ramp ratio were identified. We then proposed an origin–destination path acquisition method and developed a route-selection prediction model based on a multinomial logit model with sample weights. Additionally, the study linked the traffic control scheme with travel time using the Bureau of Public Roads function—a model that illustrates the relationship between network-wide travel time and traffic demand—and developed an arterial road network traffic forecasting model. Verification showed that the prediction accuracy of the improved multinomial logit model increased from 92.55 % to 97.87 %. Furthermore, reducing the green time ratio for multilane merging from 0.75 to 0.5 significantly decreased the likelihood of vehicles choosing this route and reduced the number of vehicles passing through the ramp. The flow prediction model achieved a 97.9 % accuracy, accurately reflecting actual volume changes and ensuring smooth operation of the main airport road. This provides a strong foundation for developing effective traffic control plans.

本研究旨在通过调查影响因素和分析时空交通流分布，加深对干道网络内车辆路径选择行为的理解。利用无线射频识别（RFID）出行数据，确定了出行时长、路线熟悉程度、路线长度、快速路比例、干道比例和匝道比例等关键因素。然后，我们提出了一种起点-终点路径获取方法，并开发了一个基于带样本权重的多叉 Logit 模型的路线选择预测模型。此外，该研究还利用公共道路局函数将交通管制方案与旅行时间联系起来--该函数模型说明了整个网络的旅行时间与交通需求之间的关系，并开发了一个干道网络交通量预测模型。验证结果表明，改进后的多叉 logit 模型的预测准确率从 92.55% 提高到 97.87%。此外，将多车道并线的绿灯时间比从 0.75 降低到 0.5，大大降低了车辆选择该路线的可能性，并减少了通过匝道的车辆数量。流量预测模型的准确率达到 97.9%，准确反映了实际流量变化，确保了机场主干道的顺畅运行。这为制定有效的交通管制计划奠定了坚实的基础。

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引用次数: 0