Active CTDaaS: A Data Service Framework Based on Transparent IoD in City Traffic

被引:7
作者
Du, Bowen [1 ,2 ]
Huang, Runhe [3 ,4 ]
Chen, Xi [1 ,2 ]
Xie, Zhipu [1 ,2 ]
Liang, Ye [1 ,2 ]
Lv, Weifeng [1 ,2 ]
Ma, Jianhua [3 ,4 ]
机构
[1] Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China
[2] Jiangsu Prov Collaborat Innovat Ctr Modern Urban, SiPaiLou 2, Nanjing 210096, Jiangsu, Peoples R China
[3] Hosei Univ, Fac Comp & Informat Sci, Tokyo, Japan
[4] Ctr South Univ, Sch Informat & Engn, Changsha, Hunan, Peoples R China
基金
日本学术振兴会; 中国国家自然科学基金;
关键词
IoTDS; intelligent transportation system; transparent computing; data service; DATA FUSION;
D O I
10.1109/TC.2016.2529623
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Transport infrastructure generates a huge amount of city transportation data due to the significant increasing of advanced devices, such as sensing devices, mobile devices and real-time monitors. However, transportation big data cannot be fully analyzed and utilized by urban traffic data services currently. This paper proposes a novel City Traffic Data-as-a-Service (CTDaaS), which fuses data from distributed providers. Initially, we build an Internet of Traffic Data Service (IoTDS) model to identify associations and relationships among data resources. Then a CTDaaS agent is developed under Transparent Computing paradigm and service oriented architecture. It receives user requests, fuses knowledge from a variety of data sources according to different computing models, and responses differentiated Quality of Data (QoD). Finally, an application scenario, named Park and Ride (P+R), is implemented and evaluated to demonstrate how the service works using existing dynamic city traffic data.
引用
收藏
页码:3524 / 3536
页数:13
相关论文
共 34 条
  • [11] Data fusion in intelligent transportation systems: Progress and challenges - A survey
    El Faouzi, Nour-Eddin
    Leung, Henry
    Kurian, Ajeesh
    [J]. INFORMATION FUSION, 2011, 12 (01) : 4 - 10
  • [12] Improving Travel Time Estimates from Inductive Loop and Toll Collection Data with Dempster-Shafer Data Fusion
    El Faouzi, Nour-Eddin
    Klein, Lawrence A.
    De Mouzon, Olivier
    [J]. TRANSPORTATION RESEARCH RECORD, 2009, (2129) : 73 - 80
  • [13] The utilization of artificial neural networks for multisensor system integration in navigation and positioning instruments
    El-Sheimy, Naser
    Chiang, Kai-Wei
    Noureldin, Aboelmagd
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2006, 55 (05) : 1606 - 1615
  • [14] Fu Yanjie., 2014, P 2014 SIAM INT C DA, P470
  • [15] Cloud-Based Intelligent Transportation Systems Using Model Predictive Control
    Heilig, Leonard
    Negenborn, Rudy R.
    Voss, Stefan
    [J]. COMPUTATIONAL LOGISTICS (ICCL 2015), 2015, 9335 : 464 - 477
  • [16] Multisensor data fusion: A review of the state-of-the-art
    Khaleghi, Bahador
    Khamis, Alaa
    Karray, Fakhreddine O.
    Razavi, Saiedeh N.
    [J]. INFORMATION FUSION, 2013, 14 (01) : 28 - 44
  • [17] Use of Contextual Information by Bayesian Networks for Multi-Object Tracking in Scanning Laser Range Data
    Lherbier, Regis
    Jida, Bassem
    Noyer, Jean-Charles
    Wahl, Martine
    [J]. ITST: 2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORT SYSTEMS TELECOMMUNICATIONS, 2009, : 97 - 102
  • [18] Exploiting Heterogeneous Human Mobility Patterns for Intelligent Bus Routing
    Liu, Yanchi
    Liu, Chuanren
    Yuan, Nicholas Jing
    Duan, Lian
    Fu, Yanjie
    Xiong, Hui
    Xu, Songhua
    Wu, Junjie
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2014, : 360 - 369
  • [19] A review of data fusion models and systems
    Sidek, Othman
    Quadri, S. A.
    [J]. INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION, 2012, 3 (01) : 3 - 21
  • [20] Context-based Information Fusion: A survey and discussion
    Snidaro, Lauro
    Garcia, Jesus
    Llinas, James
    [J]. INFORMATION FUSION, 2015, 25 : 16 - 31