Zhen Zhou , Ziyuan Gu , Anfeng Jiang , Zhiyuan Liu , Yi Zhao , Hongzhe Liu
{"title":"智能移动系统中基于增强的时空异常检测原型","authors":"Zhen Zhou , Ziyuan Gu , Anfeng Jiang , Zhiyuan Liu , Yi Zhao , Hongzhe Liu","doi":"10.1016/j.tre.2024.103815","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"193 ","pages":"Article 103815"},"PeriodicalIF":8.3000,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prototype augmentation-based spatiotemporal anomaly detection in smart mobility systems\",\"authors\":\"Zhen Zhou , Ziyuan Gu , Anfeng Jiang , Zhiyuan Liu , Yi Zhao , Hongzhe Liu\",\"doi\":\"10.1016/j.tre.2024.103815\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":49418,\"journal\":{\"name\":\"Transportation Research Part E-Logistics and Transportation Review\",\"volume\":\"193 \",\"pages\":\"Article 103815\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2024-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Part E-Logistics and Transportation Review\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S136655452400406X\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part E-Logistics and Transportation Review","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S136655452400406X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
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.
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.