Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2025-02-05 DOI:10.1145/3716371
Ahmad Jan Mian, Muhammad Adil, Bouziane Brik, Saad Harous, Sohail Abbas
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Abstract

Intelligent Transportation Systems (ITS) generate massive amounts of Big Data through both sensory and non-sensory platforms. The data support batch processing as well as stream processing, which are essential for reliable operations on the roads and connected vehicles in ITS. Despite the immense potential of Big Data intelligence in ITS, autonomous vehicles are largely confined to testing and trial phases. The research community is working tirelessly to improve the reliability of ITS by designing new protocols, standards and connectivity paradigms. In the recent past, several surveys have been conducted that focus on Big Data Intelligence for ITS, yet none of them have comprehensively addressed the fundamental challenges hindering the widespread adoption of autonomous vehicles on the roads. Our survey aims to help readers better understand the technological advancements by delving deep into Big Data architecture, focusing on data acquisition, data storage and data visualization. We reviewed sensory and non-sensory platforms for data acquisition, data storage repositories for archival and retrieval of large datasets, and data visualization for presenting the processed data in an interactive and comprehensible format. To this end, we discussed the current research progress by comprehensively covering the literature and highlighting challenges that urgently require the attention of research community. Based on the concluding remarks, we argued that these challenges hinder the widespread presence of autonomous vehicles on the roads. Understanding these challenges is important for a more informed discussion on the future of self-driven technology. Moreover, we acknowledge that these challenges not only affect individual layers but also impact the functionality of subsequent layers. Finally, we outline our future work that explores how resolving these challenges could enable the realization of innovations such as smart charging systems on the roads and data centers on wheels.
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智能交通系统中的大数据:当前趋势、挑战和未来方向
智能交通系统(ITS)通过感官和非感官平台产生大量大数据。数据支持批处理和流处理,这对于ITS中道路和联网车辆的可靠运行至关重要。尽管大数据智能在ITS领域具有巨大潜力,但自动驾驶汽车在很大程度上仍局限于测试和试验阶段。研究界正在不懈努力,通过设计新的协议、标准和连接范式来提高ITS的可靠性。在最近的过去,已经进行了几项针对ITS大数据智能的调查,但没有一项调查全面解决了阻碍自动驾驶汽车在道路上广泛采用的根本挑战。我们的调查旨在通过深入研究大数据架构,重点关注数据采集、数据存储和数据可视化,帮助读者更好地了解技术进步。我们回顾了用于数据采集的感官和非感官平台,用于大型数据集存档和检索的数据存储库,以及用于以交互式和可理解的格式呈现处理过的数据的数据可视化。为此,我们对当前的研究进展进行了讨论,对文献进行了全面的梳理,并突出了急需研究界关注的挑战。基于结束语,我们认为这些挑战阻碍了自动驾驶汽车在道路上的广泛存在。了解这些挑战对于更深入地讨论自动驾驶技术的未来非常重要。此外,我们承认这些挑战不仅影响单个层,也影响后续层的功能。最后,我们概述了我们未来的工作,探索如何解决这些挑战,从而实现创新,如道路上的智能充电系统和车轮上的数据中心。
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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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