Article(id=1242137475683000519, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242137460445098003, articleNumber=null, orderNo=11, doi=10.3981/j.issn.1000-7857.2019.06.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1505664000000, receivedDateStr=2017-09-18, revisedDate=1545667200000, revisedDateStr=2018-12-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1554799288813, onlineDateStr=2019-04-09, pubDate=1553702400000, pubDateStr=2019-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1554799288813, onlineIssueDateStr=2019-04-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078251998, creator=sys-migrate, updateTime=1774078251998, updator=sys-migrate, issue=Issue{id=1242137460445098003, tenantId=1146029695717560320, journalId=1146031591421210625, year='2019', volume='37', issue='6', pageStart='1', pageEnd='128', issueExtLink='null', onlineDate='null', pubDate='1553702400000', pubDateStr='2019-03-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078248364, creator='sys-migrate', updateTime=1774078248364, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=66, endPage=72, ext={EN=ArticleExt(id=1242137480334487706, articleId=1242137475683000519, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Application of big data of transportation, columnId=1242137471564197995, journalTitle=Science & Technology Review, columnName=Exclusive: Intelligent Transport, runingTitle=null, highlight=null, articleAbstract=With the wide applications of information and intelligence in transportation services and management, large and rich data resources are accumulated in the industry. This paper analyzes the characteristics, the types and the data volume of the transportation big data, and briefly describes the structure, the function and the application of the integrated traffic travel big data open cloud platform, and analyzes application cases in several aspects, such as the traffic big data in the traffic congestion control, the highway intelligent inspection, the passenger flow statistics and the civil aviation management. Finally, it is suggested that the transportation data are very hybrid in nature, and the processing results may involve contradictory conclusions. As the data amount increases, the influence of inaccuracy on the whole analysis conclusion becomes smaller. When the data volume is expanded, simple algorithms may be more practical., authors=YANG Qi1 , LIU Dongmei2 , authorsList=YANG Qi, LIU Dongmei, authorCompany=1. Research Institute of Highway, Ministry of Transport, Beijing 100088, China;1 , 刘冬梅2 , authorsList=杨琪, 刘冬梅, authorCompany=1. 交通运输部公路科学研究院, 北京 100088;
科技导报
| 专题：智能交通 2019, 37(6): 66-72
交通运输大数据应用进展
全屏
杨琪1 , 刘冬梅2
作者信息
1. 交通运输部公路科学研究院, 北京 100088;
Application of big data of transportation
Affiliations
出版时间: 2019-03-28
doi: 10.3981/j.issn.1000-7857.2019.06.009
文章导航
随着交通运输服务与管理信息化、智能化的发展，行业内集中了大量丰富的数据资源。分析了交通运输大数据的特点、类型及数据量，简述了综合交通出行大数据开放云平台的构架、功能和应用效果。通过研究交通运输大数据在交通拥堵治理、高速公路智能稽查、客流统计及民航空管等方面的应用案例，指出交通运输大数据发展及应用趋势混杂性更强，分析处理结果可能会有相互矛盾的情况，随着数据量的增加，不精确性对整个分析结论的影响力变小；当数据量扩大后，简单算法可能更加实用等。
With the wide applications of information and intelligence in transportation services and management, large and rich data resources are accumulated in the industry. This paper analyzes the characteristics, the types and the data volume of the transportation big data, and briefly describes the structure, the function and the application of the integrated traffic travel big data open cloud platform, and analyzes application cases in several aspects, such as the traffic big data in the traffic congestion control, the highway intelligent inspection, the passenger flow statistics and the civil aviation management. Finally, it is suggested that the transportation data are very hybrid in nature, and the processing results may involve contradictory conclusions. As the data amount increases, the influence of inaccuracy on the whole analysis conclusion becomes smaller. When the data volume is expanded, simple algorithms may be more practical.
transportation
/
big data
/
open cloud platform
杨琪, 刘冬梅.
交通运输大数据应用进展.
科技导报,
2019
, 37
(6)
: 66
-72
.
DOI: 10.3981/j.issn.1000-7857.2019.06.009
YANG Qi, LIU Dongmei.
Application of big data of transportation[J].
Science & Technology Review ,
2019
, 37
(6)
: 66
-72
.
DOI: 10.3981/j.issn.1000-7857.2019.06.009
2019年第37卷第6期
PDF下载
232
32
引用本文
BibTeX
文章信息
doi: 10.3981/j.issn.1000-7857.2019.06.009
接收时间：2017-09-18
首发时间：2019-04-09
出版时间：2019-03-28
收稿日期：2017-09-18
修回日期：2018-12-25
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2019.06.009
引用本文
BibTeX
2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
关闭全屏
BibTeX
EndNote
RefWorks
TxT
PDF下载
全屏
232
32
关闭全屏
北京市海淀区学院南路86号
010-62199257
qkjq@cast.org.cn
