A Kind of Novel ITS Based on Space-Air-Ground Big-Data | IEEE Journals & Magazine | IEEE Xplore

- Donate
- Personal Sign In

ADVANCED SEARCH

Journals & Magazines >IEEE Intelligent Transportati... >Volume: 8 Issue: 1

A Kind of Novel ITS Based on Space-Air-Ground Big-Data

Download PDF
Download References
Request Permissions
Save to
Alerts

Abstract:

Based on the big-data collected from Space-Air-Ground, i.e. Space means satellite, Air means helicopter, the key technologies of novel ITS (Intelligent Transportation Sys...Show More

Metadata

Abstract:

Based on the big-data collected from Space-Air-Ground, i.e. Space means satellite, Air means helicopter, the key technologies of novel ITS (Intelligent Transportation System) are investigated, including data acquisition sensor, dynamic data transmission, massive data storage, multi-source data fusion, massive data mining and analysis, etc. On this basis, the cloud computing platform of novel ITS is designed, including Space-Air-Ground bigdata acquisition & transmission subsystem, cloud computing platform, intelligent transportation application & service subsystem. With the help of the data visualization, data prediction, and decision making, the complete traffic big-data set including people (passenger, driver), vehicle, and road traffic environment, can create their core addedvalues. The applications of novel ITS include: providing transportation data services for traffic enterprise and business users, such as customized mining, and specific industry analysis; providing accurate transportation information services for the citizen; providing business model for all levels of users, such as data visualization and customized services.

Published in: IEEE Intelligent Transportation Systems Magazine ( Volume: 8, Issue: 1, Spring 2016)

Page(s): 10 - 22

Date of Publication: 18 January 2016

ISSN Information:

DOI: 10.1109/MITS.2015.2503200

References is not available for this document.

I. Introduction

Urban traffic covers a broad variety of subjects or components, including private cars, trucks, subways, bus, taxi, municipal administration, transport hub, integrated information service, traffic infrastructure, etc. Traffic congestion, pollution and accidence are the main problems, which are very complex and important, so many related research institutes and traffic agencies have made their efforts to explore a number of methods and technologies to solve these problems.

Select All

1.

L. Hui, Z. Jun and L. L. Cheng, "Application examples of the network fixed point theory for space-air-ground integrated communication network", Proc. Int. Congr. Ultra-Modern Telecommunications Control Systems Workshops, pp. 989-993, 2010.

2.

M. Maamar, J. Liu and W. Liu, "A new lightweight link quality based reputation model for space-air-ground integrated wireless sensor network (SAGIWSN)", Proc. IEEE Workshop Electronics Computer Applications, pp. 230-236, 2014.

3.

Z. Fengxia, Z. Yaozhong and L. Yingying, "Research on Hybrid Layered Architecture of Command and Control System for Space-air-ground Collaboration", J. Acad. Equip., vol. 24, no. 5, pp. 74-77, Oct. 2013.

4.

H. S. Zhaohui, "Vehicle change detection from aerial imagery using detection response maps", Proc. SPIE Int. Society Optical Engineering Geospatial InfoFusion Video Analytics IV Motion Imagery ISR Situational Awareness II, vol. 9089, 2014.

5.

S. Jiwon and T. Walter, "Future dual-frequency GPS navigation system for intelligent air transportation under strong ionosphere scintillation", IEEE Trans. Intell. Transp. Syst., vol. 15, no. 5, pp. 2224-2236, Oct. 2014.

6.

A. Arghavan, V. R. Monir, J. M. De La Garza and R. M. Buehrer, "Improving GPS-based vehicle positioning for intelligent transportation systems", Proc. IEEE Intelligent Vehicles Symp., pp. 1023-1029, 2014.

7.

L. Han and K. Wu, "Multifunctional Transceiver for Future Intelligent Transportation Systems", IEEE Trans. Microwave Theory Tech., vol. 59, no. 7, pp. 1879-1892, 2011.

CrossRef Google Scholar

8.

P. Anuj, V. Kimon and K. Michael, "Generating traffic statistical profiles using unmanned helicopter-based video data", Proc. IEEE Int. Conf. Robotics Automation, pp. 870-876, 2007.

9.

W. P. Hyoung, Y. Y. Il, J. R. Lee and J. Haengjin, "Study on big data center traffic management based on the separation of large-scale data stream", Proc. Seventh Int. Conf. Innovative Mobile Internet Services Ubiquitous Computing, pp. 591-594, 2013.

10.

Q. Q. Li, B. Lei, Y. Yu and R. Hou, "Real-time highway traffic information extraction based on airborne video", Proc. 12th Int. IEEE Conf. Intelligent Transportation Systems, pp. 1-6, 2009.

CrossRef Google Scholar

11.

Z. Li, C. Chen and K. Wang, "Cloud computing for agent-based urban transportation systems", IEEE Intell. Syst., vol. 26, no. 1, pp. 73-79, 2011.

12.

J. Yu, F. Jiang and T. Zhu, "RTIC-C: A big data system for massive traffic information mining", Proc. Int. Conf. Cloud Computing Big Data, pp. 395-402, 2013.

CrossRef Google Scholar

13.

S. Bitam and A. Mellouk, "ITS-cloud: Cloud computing for intelligent transportation system", Proc. IEEE Global Communications Conf., pp. 2054-2059, 2012.

CrossRef Google Scholar

14.

K. Wang and Z. Shen, "Artificial societies and GPU-based cloud computing for intelligent transportation management", IEEE Intell. Syst., vol. 26, no. 4, pp. 22-28, 2011.

CrossRef Google Scholar

15.

P. Jaworski, T. Edwards, J. Moore and K. Burnham, "Cloud computing concept for intelligent transportation systems", Proc. 14th Int. IEEE Conf. Intelligent Transportation Systems, pp. 391-936, 2011.

CrossRef Google Scholar

16.

R. G. Qiu, K. Wang, S. Li, J. Dong and M. Xie, "Big data technologies in support of real time capturing and understanding of electric vehicle customers dynamics", Proc. 5th IEEE Int. Conf. Software Engineering Service, pp. 263-267, 2014.

CrossRef Google Scholar

17.

J. Zhang, S. You and L. Gruenwald, "High-performance spatial query processing on big taxi trip data using GPGPUs", Proc. IEEE Int. Congr. Big Data, pp. 72-79, 2014.

CrossRef Google Scholar

18.

L. Feng, F. Yuan-jing, L. Kang and Z. Ming, "Coordinated control of traffic flow in complex-arterial networks under the big data background", Proc. 26th Chinese Control Decision Conf., pp. 1811-1817, 2014.

19.

T. Luo, Y. Liao, G. Chen and Y. Zhang, "P-DOT: A model of computation for big data", Proc. IEEE Int. Conf. Big Data, pp. 31-37, 2013.

CrossRef Google Scholar

20.

M. A. Yazici, C. Kamga and A. Singhal, "A big data driven model for taxi drivers' airport pick-up decisions in New York City", Proc. IEEE Int. Conf. Big Data, pp. 37-44, 2013.

21.

J. Liu, F. Liu and N. Ansari, "Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop", IEEE Network, vol. 28, no. 4, pp. 32-39, 2014.

22.

D. Eckhoff and C. Sommer, "Driving for big data? Privacy concerns in vehicular networking", IEEE Security Privacy, vol. 12, no. 1, pp. 77-79, 2014.

CrossRef Google Scholar

23.

S.-H. Yoon, J.-S. Park, M.-S. Kim, C. Lim and J. Cho, "Behavior signature for big data traffic identification", Proc. Int. Conf. Big Data Smart Computing, pp. 261-266, 2014.

CrossRef Google Scholar

24.

L. Chunli, "Intelligent transportation based on the Internet of Things", Proc. 2nd Int. Conf. Consumer Electronics Communications Networks, pp. 360-362, 2012.

25.

D. Ai-min, T. Feng, M. Lang and C. Jia, "Comparative study on the development of the Internet of Things in Intelligent Transportation between China and abroad", Proc. 8th Int. Conf. Service Systems Service Management, pp. 1-4, 2011.

26.

Z. Yongjun, Z. Xueli, Z. Shuxian and G. shenghui, "Intelligent transportation system based on Internet of Things", Proc. World Automation Congr., pp. 1-3, 2012.

References is not available for this document.