智能移动系统中基于增强的时空异常检测原型

IF 8.3 1区 工程技术 Q1 ECONOMICS
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引用次数: 0

摘要

在复杂的交通系统中,时空异常模式的广泛存在给有效的治理和决策带来了巨大挑战。交通异常事件检测就是这一挑战的一个明显例子,异常检测的低准确率和糟糕的场景泛化性能严重影响了异常检测的整体性能。本文介绍了一个基于原型增强的框架,该框架专为智能移动系统中的时空异常检测而定制。该框架利用原型增强技术来提高异常模式的多样性,同时确保保留原始异常信息的完整性。从本质上讲,本框架采用的基于原型增强的异常检测器是一种无监督-监督混合堆叠集合。它利用无监督组件学习器的优势生成伪维度,同时集成了一个监督元检测器,用于评估组件学习器在不同环境背景下的性能。此外,我们还将这一框架具体化,并评估其在检测异常压线事件方面的性能。实证结果表明,与使用真实世界数据集的其他方法相比,我们的框架在检测异常交通事故方面具有更高的准确性和可移植性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prototype augmentation-based spatiotemporal anomaly detection in smart mobility systems
In complex mobility systems, the widespread presence of spatiotemporal anomaly patterns poses substantial challenges to effective governance and decision-making. A notable example of this challenge is evident in traffic anomalous incidents detection, where the combination of low accuracy in anomaly detection and poor scenario generalization performance significantly impacts the overall performance of anomaly detection. This paper introduces a prototype augmentation-based framework tailored for spatiotemporal anomaly detection in the context of smart mobility system. This framework utilizes prototype augmentation technique to enhance the diversity of anomaly patterns, ensuring that the integrity of the original anomaly information is preserved. Essentially, the prototype augmentation-based anomaly detector employed in this framework is a hybrid unsupervised-supervised stacking ensemble. It leverages the strengths of unsupervised component learners to generate pseudo dimensions while integrating a supervised meta-detector for evaluating the component learners’ performance across diverse environmental contexts. Additionally, we materialize this framework and assess its performance in detecting anomalous line-pressing incidents. Empirical results demonstrate our framework’s superior accuracy and transferability in detecting anomalous traffic incidents compared to alternative methods using a real-world dataset.
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来源期刊
CiteScore
16.20
自引率
16.00%
发文量
285
审稿时长
62 days
期刊介绍: Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management. Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.
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