Personalization in Public Transport Passenger Information Systems: A Systematic Review and Framework

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2025-03-06 DOI:10.1145/3721478
Marloes Vredenborg, Anouk van Kasteren, Judith Masthoff
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Abstract

In recent years, efforts to promote public transport usage have emphasized the integration of personalization into passenger information systems. Whilst numerous studies have explored this topic, a comprehensive overview of the state-of-the-art remains absent. To address this gap, we systematically reviewed 91 research papers published between 2000 and October 2024. Based on our review, we introduce a comprehensive framework that organizes the field along three key dimensions: personalization objects, attributes, and evaluation. Furthermore, we provide valuable insights for researchers and practitioners engaged in the study, design, and development of personalized passenger information systems and identify research opportunities to guide future advancements in the field.
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公共交通乘客信息系统中的个性化:系统回顾与框架
近年来,促进公共交通使用的努力强调将个性化融入乘客信息系统。虽然许多研究都探讨了这一主题,但对最新技术的全面概述仍然缺失。为了解决这一差距,我们系统地回顾了2000年至2024年10月期间发表的91篇研究论文。基于我们的回顾,我们介绍了一个综合框架,该框架沿着三个关键维度组织该领域:个性化对象、属性和评估。此外,我们为从事个性化乘客信息系统研究、设计和开发的研究人员和实践者提供有价值的见解,并确定研究机会,以指导该领域的未来发展。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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