The City Traffic Service Platform Based on MPI Model

Jun Song; Ya Hui Cheng; Xin Zhu Zhang; Ya Fen Zhang

doi:10.4028/www.scientific.net/AMM.548-549.1844

Paper Titles

Evaluation for the Location Selection of Airport Based on WLSM-TOPSIS Method
p.1823

Research on the Calculating Method of Low-Carbon City Public Transit Bearing Rate
p.1828

Study on the Scheme Selection of Large-Scale Highway Transportation
p.1835

The Laws and Measurements Analysis about Urban Rail Transit Noise and Vibration Reduction
p.1839

The City Traffic Service Platform Based on MPI Model
p.1844

The Simulation of Non-Gaussian Vibration Environment on Railway
p.1848

Research of a MapReduce Model to Process the Traffic Big Data
p.1853

Application of Multidimensional Data Model in the Traffic Accident Data Warehouse
p.1857

A Study of the Traffic Flow Predictive Model Based on Mathematical Statistics and Stochastic Process
p.1862

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 548-549The City Traffic Service Platform Based on MPI...

The City Traffic Service Platform Based on MPI Model

Abstract:

“Smart City” equals “Digital City” plus “The Internet of things” and “Cloud Computing”. Vehicle networking (IOV), cloud computing and digital communication constitute the core of the intelligent traffic. Vehicle networking (IOV) refers to a fact that objects (cars and trucks, cars and roads, cars and people, vehicles and sensors, RFID and other devices which communicates with vehicles) consitute a dynamic mobile communication system through network. It can collect and share information among cars and cars, cars and people, cars and roads, vehicles and environment. In this information network platform, the multi-source information will be collected for processing, computing, and sharing as well as providing professional multimedia and mobile Internet applications . And we use MPI model to compute the large amount of information data . In this project, we achieve all the work which will be mentioned in this passage and we aim to use these technologies to resolve the computing problem in data processing and apply these new resolutions in smart city.

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Periodical:

Applied Mechanics and Materials (Volumes 548-549)

Pages:

1844-1847

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.548-549.1844

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Cite this paper

Online since:

April 2014

Authors:

Jun Song*, Ya Hui Cheng, Xin Zhu Zhang, Ya Fen Zhang

Keywords:

Cloud Computing, GIS, RFID, Sensing Technology, Transportation Cloud

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* - Corresponding Author

References

[1] Agrawal R, Imielinski T, Swami Database Mining: A Performance Perspective．IEEE Transaction on Knowledge and Data Engineering，(1993).

Google Scholar

[2] Yoel Tenne; An Optimization Algorithm Employing Multiple Metamodels and Optimizers[J]; International Journal of Automation and Computing. (2013).

DOI: 10.1007/s11633-013-0716-y

Google Scholar

[3] ZhangLe , A genetic Gaussian process regression model based on memetic algorithm[J], Journal of Central South University, (2013).

Google Scholar

[4] Kraus, A.W. (1993). Repayment agreements for employee training costs. Labor Law Journal, 44(1), 49-55.

Google Scholar

[5] Lee, S.K., G.H. Lim, W.S. Lim. 2003. Family Business Succession: Appropriation Risk and Choice of Successor. Academy of Management Review, 28(4): 657-666.

DOI: 10.5465/amr.2003.10899446

Google Scholar