大数据环境物流云平台“车货携手”数据统一访问与转换关键技术研究
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- 英文论文题名：“Vehicle-Goods Hand-in-hand” Logistics Cloud Platform's Data Unified Access and Conversion Key Technology in the Big Data Environment and conversion
- 论文作者：关金平 ; 卢伟文 ; 关志超
- 英文论文作者：Guan Jinping ; Lu Weiwen ; Guan Zhichao ; Shenzhen Institutes of Advanced Technology ; Chinese Academy of Sciences ; Department of Civil and Environmental Engineering ; Institute of Transportation Studies ; University of California ; Shenzhen Qianhai Xieshou Logistics Technology Co. ; Ltd. ; Shenzhen Comprehensive Transportation Operations Command Center ; Shenzhen Traffic Control and Simulation Engineering Center
- 年：2016
- 作者机构：中国科学院深圳先进技术研究院数字所云计算中心;美国加州大学伯克利分校土木与环境工程系交通研究中心;深圳市前海携手物流科技有限公司;深圳市综合交通运行指挥中心;深圳市交通控制与仿真工程中心;
- 论文关键词：大数据环境 ; 物流云平台 ; 货运电商 ; 车货携手 ; 线上线下O2O ; 数据统一访问与转换
- 英文论文关键词：Big Data Environment ; Logistics Cloud Platform ; Logistic Online Commerce ; Vehicle-goods Hand in Hand ; Online to Offline ; Data Unified Access
- 会议召开时间：2016-11-16
- 会议录名称：第十一届中国智能交通年会大会论文集
- 语种：中文
- 分类号：U495;U492.3
- 学会代码：ZJTX
- 会议名称：第十一届中国智能交通年会
- 会议地点：中国重庆
- 主办单位：中国智能交通协会
- 学会名称：中国智能交通协会
- 页数：8
- 文件大小：1050k
- 原文格式：D
- 会议级别：全国
摘要
当前,城市综合交通信息中心信息源池环境建设基本完成,交通大数据已经接入,生成的大数据规模达到PB级。继"嘀嘀打车"迅猛问世一波未平,深、港跨境电商物流云平台"车货携手"在深圳前海保税区一波又起。现代物流业"货车司机找货难,货主找司机难"问题已迫在眉睫,虽有物流调度公司来发布物流运转信息,但大量信息中介费也间接抬高了物流成本。如何消除信息不透明,让货车司机直接对接货主,借鉴"滴滴打车"方式,在货运风险可控的前提下,为货主和车主搭建直接交易的物流货运电商云平台"车货携手",让货车司机在平台上竞价抢单,实现无中介降低物流成本,降低返程物流货车空载率,打造线上线下O2O模式的新型物流。大数据源池环境在物流云平台数据中统一访问与转换成为当前现代物流应用中的首要任务,深圳前海引入美国加州大学伯克利分校与中科院深圳先进技术研究院联合开展数据统一访问与转换关键技术,取得很好的应用成果。
Pool Environment of Information Sources is established in Urban Comprehensive Transportation Information Center.Transportation Big Data is in place and the generated big data reached PB level.Shenzhen Hongkong Cross boundary online commerce:the logistics cloud platform "vehicle-goods hand in hand" is developed in Shenzhen Qianhai.Freight vehicle cannot find delivery order and customers cannot find delivery service.This is becoming a serious problem.Even though there are a lot of logistic companies are giving out information and advertisements.A large amount of agencies in between raises the logistics cost.How do we make information more transparent and directly connect customers with freight companies or freight vehicle drivers? This "vehicle-goods hand in hand" provides a platform that allows freight vehicle drivers and customers directly connected and bidding online.This achieves no middle agency and reduces logistics cost.It also reduces idle rate of freight vehicles when returning.This is a new logistics online to offline mode.Big data sources pool environment provides data unified access and conversion in the logistics cloud platform.It is the key in the new logistics mode application.We study the data unified access and conversion key technology in University of California,Berkeley and Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences and apply it in Shenzhen Qianhai.It has great results.
Pool Environment of Information Sources is established in Urban Comprehensive Transportation Information Center.Transportation Big Data is in place and the generated big data reached PB level.Shenzhen Hongkong Cross boundary online commerce:the logistics cloud platform "vehicle-goods hand in hand" is developed in Shenzhen Qianhai.Freight vehicle cannot find delivery order and customers cannot find delivery service.This is becoming a serious problem.Even though there are a lot of logistic companies are giving out information and advertisements.A large amount of agencies in between raises the logistics cost.How do we make information more transparent and directly connect customers with freight companies or freight vehicle drivers? This "vehicle-goods hand in hand" provides a platform that allows freight vehicle drivers and customers directly connected and bidding online.This achieves no middle agency and reduces logistics cost.It also reduces idle rate of freight vehicles when returning.This is a new logistics online to offline mode.Big data sources pool environment provides data unified access and conversion in the logistics cloud platform.It is the key in the new logistics mode application.We study the data unified access and conversion key technology in University of California,Berkeley and Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences and apply it in Shenzhen Qianhai.It has great results.
引文
[1]杨东援,段征宇.大数据环境下城市交通分析技术[D].上海:同济大学出版社,2015.
[2]杨东援.连续续数据环境下的交通规划与管理[D].上海:同济大学出版社,2014.
[3]Wang P.Gonzalez M C,Hidalgo C A,et al.Understanding the spreading patterns of mobile phone viruses[J].Science,2009,324(5930):1071-1076.
[4]Li Weifeng,Cheng Xiaoyun,et al.A framework for spatial interaction analysis based on large-scale mobile phone data[J].Computational Intelligence and Neuroscience.2014:1-11.
[5]Francesco Calabrese,Mi Diao,Giusy Di Lorenzo,et al.Understanding individual mobility patterns from urban sensing data,A mobile phone trace example[J].Transportation Research Part C,2013:301-313.
[6]Halevy A,Norvig P,Pereira F.The unreasonable effectiveness of data[J].Intelligent Systems,IEEE,2009,24(2):8-12.
[7]Fosgerau M.How a fast lane may replace a congestion toll[J].Transportation Research Part B,2011,45:845-851.
[8]Nie Y,Liu Y.Existence of self-financing and pareto-improving congestion pricing,Impact of value of time distribution[J].Transportation Research Part A.2010,44:39-51.
[9]Shen W,Zhang H M.Pareto-improving ramp metering strategies for reducing congestion in the morning commute[J].Transportation Research Part A-Policy And Practice,2010,44:676-696.
[10]Zhang X,Zhang H M,Huang H,et al.Competitive,cooperative and Stackelberg congestion pricing for multiple regions in transportation networks[J].Transportmetrica.2011,7(4):297-320.
[11]Isaacman S,Becker B,Caceres R,et al.Ranges of human mobility in los angeies and new york[C]Pervasive Computing and Communications Workshops(PERCOM Workshops).IEEE International Conference on.IEEE,2011:88-93.
[12]Hofleitner A,Herring R,Abbeel P,et al.Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network[J].Intelligent Transportation Systems.IEEE Transactions on.2012,13(4):1679-1693.
[13]Rermias S M,Hainen A M,Day C M,et al.Performance characterization of arterial traffic flow with probe vehicie data[J].Transportation Research Record,Journal of the Transportation Research Board.2013,238(1):10-21.
[2]杨东援.连续续数据环境下的交通规划与管理[D].上海:同济大学出版社,2014.
[3]Wang P.Gonzalez M C,Hidalgo C A,et al.Understanding the spreading patterns of mobile phone viruses[J].Science,2009,324(5930):1071-1076.
[4]Li Weifeng,Cheng Xiaoyun,et al.A framework for spatial interaction analysis based on large-scale mobile phone data[J].Computational Intelligence and Neuroscience.2014:1-11.
[5]Francesco Calabrese,Mi Diao,Giusy Di Lorenzo,et al.Understanding individual mobility patterns from urban sensing data,A mobile phone trace example[J].Transportation Research Part C,2013:301-313.
[6]Halevy A,Norvig P,Pereira F.The unreasonable effectiveness of data[J].Intelligent Systems,IEEE,2009,24(2):8-12.
[7]Fosgerau M.How a fast lane may replace a congestion toll[J].Transportation Research Part B,2011,45:845-851.
[8]Nie Y,Liu Y.Existence of self-financing and pareto-improving congestion pricing,Impact of value of time distribution[J].Transportation Research Part A.2010,44:39-51.
[9]Shen W,Zhang H M.Pareto-improving ramp metering strategies for reducing congestion in the morning commute[J].Transportation Research Part A-Policy And Practice,2010,44:676-696.
[10]Zhang X,Zhang H M,Huang H,et al.Competitive,cooperative and Stackelberg congestion pricing for multiple regions in transportation networks[J].Transportmetrica.2011,7(4):297-320.
[11]Isaacman S,Becker B,Caceres R,et al.Ranges of human mobility in los angeies and new york[C]Pervasive Computing and Communications Workshops(PERCOM Workshops).IEEE International Conference on.IEEE,2011:88-93.
[12]Hofleitner A,Herring R,Abbeel P,et al.Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network[J].Intelligent Transportation Systems.IEEE Transactions on.2012,13(4):1679-1693.
[13]Rermias S M,Hainen A M,Day C M,et al.Performance characterization of arterial traffic flow with probe vehicie data[J].Transportation Research Record,Journal of the Transportation Research Board.2013,238(1):10-21.