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大数据背景下的智能交通系统应用与平台构建 被引量:3

Application and Platform Construction of Intelligent Transportation System in the Context of Big Data
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摘要 随着我国科学技术的不断进步和发展,在我国的交通工作中,已经广泛的应用到了智能交通系统,智能交通系统的出现,对于我国发生的交通问题进行了有效的解决,为城市的发展奠定了坚实的基础,作出了巨大的贡献。然而,在信息技术高度发达的今天,在大数据的背景之下,智能交通系统面临着新的挑战,也在发展的过程中,出现了一些问题和不足,如何在大数据时代下充分的发挥出智能交通系统的作用和价值,成为了人们日益关注的研究中。因此,文章针对大数据背景下的智能交通系统进行了深入的探究和分析,从智能交通系统中大数据的应用情况入手,提出了智能交通系统中存在的问题和不足,并提出了在大数据背景下构建智能交通系统的相关策略,为日后智能交通系统工作的研究提供了一定的理论基础和科学依据。 With the continuous progress and development of science and technology in our country, it has been widely used inthe transportation work of our country, and the emergence of intelligent transportation system has effectively solved the traffic problems in our country. It has laid a solid foundation for the development of the city and made great contributions. However, with thedevelopment of information technology, under the background of big data, Intelligent Transportation system is facing new challenges,and in the process of development, there are some problems and deficiencies. How to give full play to the role and value of trafficsystem in the era of big data has become an increasingly concerned research. Therefore, this paper makes a deep research and analysis on the intelligent transportation system under the background of big data, starting with the application of big data in the intelligent transportation system, puts forward the problems and shortcomings in the intelligent transportation system, and provides forwardthe related strategy of constructing intelligent transportation system under the background of big data, which provides a certain theoretical basis and scientific basis for the future research of intelligent transportation system.
出处 《科技创新与应用》 2018年第16期171-172,共2页 Technology Innovation and Application
关键词 大数据 智能交通 应用 构建 big data intelligent transportation application construction
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  • 1赵金山,狄增如,王大辉.北京市公共汽车交通网络几何性质的实证研究[J].复杂系统与复杂性科学,2005,2(2):45-48. 被引量:45
  • 2陆化普,石冶.Complexity of Public Transport Networks[J].Tsinghua Science and Technology,2007,12(2):204-213. 被引量:13
  • 3李英,周伟,郭世进.上海公共交通网络复杂性分析[J].系统工程,2007,25(1):38-41. 被引量:65
  • 4童隆俊,陈铭.构建[智慧南京]提升城市功能[M/OL].2010.
  • 5HANDDJ.MANNILA H,SMYTH P. Principles of data mining[M]. MIT press,2001.
  • 6JIWEI H,KAMBER M.數据挖掘概念与技术[M].北京:机械工业出版社,2006.
  • 7MAYER-SCHONBERGER V;CUKIER K;盛杨燕;周涛.大数据时代:生活、工作与思维的大变革[M]杭州:浙江人民出版社,2012.
  • 8Schlimmer J C,Granger R H Jr. Incremental Learning from Noisy Data[J].Machine Learning,1986,(03):317-354.
  • 9Gerhard W,Kubat M. Effective Learning in Dynamic Environments by Explicit Context Tracking[A].Berlin,Germany:Springer-Verlag,1993.
  • 10Last M. Online Classification of Nonstationary Data Streams[J].lntelligent Data Analysis,2002,(02):129-147.

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