An overview of Hadoop applications in transportation big data

IF 7.4 2区 工程技术 Q1 ENGINEERING, CIVIL Journal of Traffic and Transportation Engineering-English Edition Pub Date : 2023-10-01 DOI:10.1016/j.jtte.2023.05.003
Changxi Ma , Mingxi Zhao , Yongpeng Zhao
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Abstract

As an open-source cloud computing platform, Hadoop is extensively employed in a variety of sectors because of its high dependability, high scalability, and considerable benefits in processing and analyzing massive amounts of data. Consequently, to derive valuable insights from transportation big data, it is essential to leverage the Hadoop big data platform for analysis and mining. To summarize the latest research progress on the application of Hadoop to transportation big data, we conducted a comprehensive review of 98 relevant articles published from 2012 to the present. Firstly, a bibliometric analysis was performed using VOSviewer software to identify the evolution trend of keywords. Secondly, we introduced the core components of Hadoop. Subsequently, we systematically reviewed the 98 articles, identified the latest research progress, and classified the main application scenarios of Hadoop and its optimization framework. Based on our analysis, we identified the research gaps and future work in this area. Our review of the available research highlights that Hadoop has played a significant role in transportation big data research over the past decade. Specifically, the focus has been on transportation infrastructure monitoring, taxi operation management, travel feature analysis, traffic flow prediction, transportation big data analysis platform, traffic event monitoring and status discrimination, license plate recognition, and the shortest path. Additionally, the optimization framework of Hadoop has been studied in two main areas: the optimization of the computational model of Hadoop and the optimization of Hadoop combined with Spark. Several research results have been achieved in the field of transportation big data. However, there is less systematic research on the core technology of Hadoop, and the breadth and depth of the integration development of Hadoop and transportation big data are not sufficient. In the future, it is suggested that Hadoop may be combined with other big data frameworks such as Storm and Flink that process real-time data sources to improve the real-time processing and analysis of transportation big data. Simultaneously, the research on multi-source heterogeneous transportation big data is still a key focus. Improving existing big data technology to enable the analysis and even data compression of transportation big data can lead to new breakthroughs for intelligent transportation.

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Hadoop在交通大数据中的应用概述
Hadoop作为一个开源云计算平台,由于其高可靠性、高可扩展性以及在处理和分析大量数据方面的巨大优势,被广泛应用于各个领域。因此,要想从交通大数据中获得有价值的见解,就必须利用Hadoop大数据平台进行分析和挖掘。为了总结Hadoop在交通大数据应用方面的最新研究进展,我们对2012年至今发表的98篇相关文章进行了全面综述。首先,使用VOSviewer软件进行文献计量分析,以确定关键词的演变趋势。其次,介绍了Hadoop的核心组件。随后,我们系统回顾了这98篇文章,确定了最新的研究进展,并对Hadoop及其优化框架的主要应用场景进行了分类。根据我们的分析,我们确定了这一领域的研究空白和未来的工作。我们对现有研究的回顾强调,Hadoop在过去十年的交通大数据研究中发挥了重要作用。具体而言,重点是交通基础设施监测、出租车运营管理、出行特征分析、交通流量预测、交通大数据分析平台、交通事件监测和状态识别、车牌识别和最短路径。此外,Hadoop的优化框架主要从两个方面进行了研究:Hadoop计算模型的优化和Hadoop与Spark的结合优化。在交通大数据领域已经取得了一些研究成果。然而,对Hadoop核心技术的系统研究较少,Hadoop与交通大数据融合发展的广度和深度也不够。未来,建议Hadoop可以与Storm、Flink等处理实时数据源的大数据框架相结合,提高交通大数据的实时处理和分析能力。同时,多源异构交通大数据的研究仍是重点。改进现有的大数据技术,实现交通大数据的分析甚至数据压缩,可以为智能交通带来新的突破。
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来源期刊
CiteScore
13.60
自引率
6.30%
发文量
402
审稿时长
15 weeks
期刊介绍: The Journal of Traffic and Transportation Engineering (English Edition) serves as a renowned academic platform facilitating the exchange and exploration of innovative ideas in the realm of transportation. Our journal aims to foster theoretical and experimental research in transportation and welcomes the submission of exceptional peer-reviewed papers on engineering, planning, management, and information technology. We are dedicated to expediting the peer review process and ensuring timely publication of top-notch research in this field.
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