Using Crowdsourcing to Establish the Big Data of the Intelligent Transportation System

Article Preview

Abstract:

The establishment of big data is completed based on the data captured by automotive electronic systems and cameras on the highway. This kind of crowdsourcing model for data gathering can not only accomplish the accurate collection of enormous data, but also solves the problem of high costs. After the collection of data, the isolated data is linked with each other and shared. In this way, the data will be socialized so as to establish the truly big data. With the big data collected, data center makes an analysis and mining to schedule, monitor and manage the transportation intelligently.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 791-793)

Pages:

2118-2121

Citation:

Online since:

September 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Lawrence A. Klein, Sensor Technologies and Data Requirements for ITS, Artech House(2001).

Google Scholar

[2] Joseph M. SussmanPerspectives on Intelligent Transportation Systems Springer-Verlag New York Inc. (2005).

Google Scholar

[3] Zouli, The Intent of things and intelligent traffic, Electronic Industry Press (2012).

Google Scholar

[4] Chen Yanyan, Wang Dongzhu, The Collection, Analysis and Application of Intelligent Traffic Information, China Communications Press (2011).

Google Scholar

[5] Milan Sonka, Vaclav Hlavac, Roger Boyl; Image Processing, Analysis, and Machine Vision(Third Edition).

DOI: 10.1117/12.256634

Google Scholar

[6] Aniket Kittur, Jeffrey V. Nickerson, Michael S. Bernstein, Elizabeth M. Gerber, Aaron Shaw, John Zimmerman, Matthew Lease, and John J. Horton; The Future of Crowd Work; ACM Conference on Computer-Supported Cooperative Work (2013).

DOI: 10.1145/2441776.2441923

Google Scholar

[7] Viktor Mayer-Schonberger, Kenneth Niel Cukier; Big Data: A Revolution That Will Transform How We Live, Work, and Think; Eamon Dolan/Houghton Mifflin Harcourt (2013).

DOI: 10.1377/hlthaff.2014.0581

Google Scholar

[8] Albert-Laszlo Barabasi; Bursts: The Hidden Patterns Behind Everything We Do, from Your E-mail to Bloody Crusades; Plume Books (2011).

Google Scholar

[9] Anand Rajaraman, Jeffrey David Ullman; Mining of Massive Datasets; Cambridge University Press (2011).

Google Scholar

[10] Ronald K. Jurgen; Automobile Electronic Manual (The Second Edition); Electronic Industry Press; (2010).

Google Scholar

[11] Jiawei Han, Micheline Kamber, Jian Pei; Data Mining: Concepts and Techniques, Third EditionMorgan Kaufmann; (2011).

Google Scholar

[12] Sergios Theodoridis, Konstantions Koutroumbas; Pattern Recognition (The Fourth Edition); Electronic Industry Press; (2010).

Google Scholar

[13] Tanglun, Chairong, Dai Cuiqin, Chen Qianbin;Technology and Application of Car Networking;Science Press; (2013).

Google Scholar

[14] Shenjian, Tang Jianrong; A Wise City: the New Thinking of the City; Posts and Telecom Press; (2012).

Google Scholar

[15] Liu Jilin, Song Jiatao, Ding Liya, Ma Hongqing, Li Peihong; Vehicle License Plate Recognition System with High Performance; ACTA AUTOMATICA SINICA; May (2003).

Google Scholar