大数据时代:交通能耗排放统计监测面临的机遇与挑战
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摘要
随着城市化进程的加快和机动化水平的提高,交通节能减排已成为城市交通可持续发展难题。党的十九大报告明确提出要建设交通强国,大数据时代的到来为车辆能耗与排放统计监测工作带来了前所未有的机遇和挑战。利用大数据技术充分挖掘我国交通运输行业与车辆能耗排放相关的数据资源潜力,实现数据规模、质量和应用水平同步提升,更好发挥数据资源的战略作用,有效提升政府服务和监管能力。与此同时,日益庞杂的海量数据、不断更新的技术手段、层出不穷的新业态与新模式,以及能源节约和环境保护关注度的提高,也对车辆能耗排放统计监测工作提出了新的挑战。
With the acceleration of urbanization and the improvement of motorization level,traffic energy saving and emission reduction has become the sustainable development problem of urban transportation. Report to the 19 thParty Congress clearly puts forward to build the traffic power. The arrival of the big data age brings unprecedented opportunity and challenge for the statistic monitoring of vehicle energy consumption and emission. By using Big Data technology,we can fully tap the data resource potential of transportation industry and vehicle energy emission in China,improve the scale,quality and application level of data synchronously,give full play to the strategic function of data resources,and effectively improve the government service and supervision ability. At the same time,many things challenge the statistic monitoring work,such as the increasingly complex mass of data,constantly updated technical means,emerging new industry and new models,as well as energy conservation and environmental protection concerns.
With the acceleration of urbanization and the improvement of motorization level,traffic energy saving and emission reduction has become the sustainable development problem of urban transportation. Report to the 19 thParty Congress clearly puts forward to build the traffic power. The arrival of the big data age brings unprecedented opportunity and challenge for the statistic monitoring of vehicle energy consumption and emission. By using Big Data technology,we can fully tap the data resource potential of transportation industry and vehicle energy emission in China,improve the scale,quality and application level of data synchronously,give full play to the strategic function of data resources,and effectively improve the government service and supervision ability. At the same time,many things challenge the statistic monitoring work,such as the increasingly complex mass of data,constantly updated technical means,emerging new industry and new models,as well as energy conservation and environmental protection concerns.
引文
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[2]Gil Press.A Very Short History of Big Data[EB/OL].2013-05-09.http://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data.
[3]S.M.Weiss,N.lndurkhya.Predictive Data Mining:A Practical Guide[M].San Mateo,California:Morgan Kaufmann Publishers,1998:22-30.
[4]Grobelnik M.Big data computing:Creating revolutionary breakthroughs in commerce,science,and society[R/OL].2013-05-10.http://videolectures.net/eswc2012_grobelnik_big_data/.
[5]Mckinsey.Big data:the next frontier for innovation,competition,and productivity[EB/OL].2013-06-24.http://www.mckinsey.com/insights/MGI/Research/technology_and_innovation/big_data_the_next_frontier_for_innovation.
[6]赵光辉,朱谷生.“互联网+”交通[M].北京:人民邮电出版社,2015:22-40.
[7]Boyd D,Grawford K.Critical Questions For Big Data[J].Information,Coummunication&Society,2012(5):662-679.
[8]赵光辉.“互联网+”综合运输服务[M].北京:中国经济出版社,2016:51-60.
[9]中科院,等.大数据导论:关键技术与行业应用最佳实践[M].北京:清华大学出版社,2015:30-35.
[10]国务院办公厅.国务院办公厅关于运用大数据加强对市场主体服务和监管的若干意见[Z].2015-06-24.
[11]国务院.国务院关于印发促进大数据发展行动纲要的通知[Z].2015-08-31.
[12]王大青.大数据环境下数字文件元数据战略研究[J].全国情报学博士生学术论坛,2013:258-268.