The Spatiotemporal Correlation Analysis of Urban Traffic Characteristics Using an Adaptive Partitioning Method

Jing Zhang; Gang Ren; Hang Jiang

doi:10.4028/www.scientific.net/AMR.1030-1032.2152

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1030-1032The Spatiotemporal Correlation Analysis of Urban...

The Spatiotemporal Correlation Analysis of Urban Traffic Characteristics Using an Adaptive Partitioning Method

Abstract:

Non-stationary and nonlinear features in the dynamic trajectories of traffic parameters may stop rich and hidden multi-dimensional knowledge from being extracted, contributing a correlation result with false judgments or ignorable significance. As the fundamental improvement for a dynamic congestion analyzing method, the paper decomposes a time series of traffic data into 2 components based on a polynomial approximation method, called the “trend” and “detail” components. In order to simplify the selection of the fitting function to obtain the trend component, the paper proposes an adaptive partitioning method based on the sensitivity search technology. The given examples prove the effectiveness of the proposed methods for a spatiotemporal traffic congestion study.

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Periodical:

Advanced Materials Research (Volumes 1030-1032)

Pages:

2152-2156

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1030-1032.2152

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Online since:

September 2014

Authors:

Jing Zhang*, Gang Ren, Hang Jiang

Keywords:

Correlation Analysis, Pearson’s Correlation Coefficient, Spatio-Temporal Analysis, Time Series Data Mining

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References

[1] J. Benjamin: Transportation Research Part A: General, Vol. 20 (1986), p.51.

Google Scholar

[2] G. G. He and S. F. Ma: 5th International Conference on Intelligent Transportation Systems (Singapore, 2002), Vol. 2, p.731.

Google Scholar

[3] R. K. Oswald, W. T. Scherer and B. L. Smith: Research Report No. UVACTS-15-13-7, Center for Transportation Studies at the University of Virginia, USA (2001).

Google Scholar

[4] V. Prasannakumar, H. Vijith and R. Charutha: Procedia-Social and Behavioral Sciences, Vol. 21 (2011), p.317.

DOI: 10.1016/j.sbspro.2011.07.020

Google Scholar

[5] S. Chen, W. Wei, B. Mao and W. Guan: Acta Phys. Sin. China, Vol. 62 (2013), No. 14.

Google Scholar

[6] T. Zhao, Y. Zhang, Y. Zhou and S. Feng: Journal of Tsinghua Univ (Sci. & Tech. ), Vol. 51 (2011), No. 3, p.313.

Google Scholar

[7] P. E. Pfeifer and S.J. Deutsch: Technometrics, Vol. 22 (1980), No. 3, p.397.

Google Scholar

[8] I. Okutani and Y. Stephanades: Transportation Research, Vol. 18(1984), No. 1, p.1.

Google Scholar

[9] Y. Yang and Y. AGO: Environment and Planning, Vol. 35 (2008), No. 5, p.762.

Google Scholar

[10] L. Zhao, J. Wang, M. Deng and J. Huang: Journal of Central South University (Sci. & Tech. ), Vol. 43 (2012), No. 10, p.4114.

Google Scholar