Influence Models of Urban Road Network Operation Performance Base on Spatial Autocorrelation Theory

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In order to better identify the spatial influence between adjacent parts of road networks, the paper introduces the spatial autocorrelation theory in evaluating the operation performance of urban road networks. The research proposes several spatial correlation validation indicators to verify the spatial characteristics among the road networks. Based on the analysis of spatial characteristics, the relationship between operation performance and influencing factors under the impact of spatial effect is examined. Furthermore, a spatial autocorrelation based influence models at three traffic flow levels is developed by using the data from a partial urban road network in Beijing. The model analysis shows that the spatial autocorrelation model is more effective in analyzing the urban road network operation performance under the influence of various factors. This model will be beneficial in identifying traffic network problems and improving traffic operations of the urban road network.

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1922-1929

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December 2012

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Y. Zahavi, Traffic Engineering and Control, Vol. 14, No. 5(1972), pp.1-4.

Google Scholar

[2] Y. Zahavi, Traffic Engineering and Control, Vol. 14, No. 6(1972), pp.1-2.

Google Scholar

[3] D. G. Buckley, and J. G. Wardrop. Australian Road Research, Vol. 10, No. 1 (1980), pp.21-31.

Google Scholar

[4] S. A. Ardekani, The Two-Fluid Characterization of Urban Traffic: Theory, Observation, and Experiment. Ph.D. Dissertation, University of Texas at Austin, (1984).

Google Scholar

[5] M. T. Ayadh, Influence of the City Geometric Features on the Two Fluid Model Parameters. Ph.D. Dissertation, Virginia Polytechnic Institute and State University, (1986).

Google Scholar

[6] S. C. Bhat, Effects of Geometric and Control Features on Network Traffic: A Simulation Study. Ph.D. Dissertation, Arlington: University of Texas at Arlington, (1994).

Google Scholar

[7] J. M. Thomson, Traffic Engineering and Control, Vol. 8, No. 12(1966), pp.721-725.

Google Scholar

[8] J. G. Wardrop, Traffic Engineering and Control, Vol. 9, No. 11(1967), p.528–532.

Google Scholar

[9] S. Lu, H.P. Lu, Z.H. TANG, et al. Journal of Highway and Transportation Research and Development, Vol. 20, No. 1(2003), pp.89-92. (In Chinese).

Google Scholar

[10] Y. Hao, L.J. Sun, H.C. Yu, et al. Computer and Communications, Vol. 4, No. 23(2005), pp.50-54. (In Chinese).

Google Scholar

[11] Y. Lin, X.G. Yang, Y.Y. Ma, Systems Engineering, Vol. 23, No. 10(2005), pp.39-43. (In Chinese).

Google Scholar

[12] H.Y. Yin, L.Q. Xu, X.F. Quan. Soft Science, Vol. 24, No. 10 (2010), pp.122-126.

Google Scholar

[13] G.Q. Chi, J. Zhu,. Popul. Res. Policy. Rev., No. 27 (2008), pp.17-42.

Google Scholar

[14] G. Calderón. Int. Adv. Econ. Res., No. 15 (2009), pp.44-58.

Google Scholar

[15] L.F. Lee., Journal of Econometrics, No. 140 (2007), p.155–189.

Google Scholar

[16] H.H. Kelejian, G. Tavlas, G. Hondroyiannis, Open Economies Review, Vol. 17, No. 4-5 (2006), pp.423-441.

DOI: 10.1007/s11079-006-0357-7

Google Scholar

[17] A. Cliff, J. Ord, Spatial Autocorrelation. (London: Pion, 1973).

Google Scholar

[18] L. Anselin, Spatial Econometrics: Methods and Models. (Boston: Kluwer Academic, 1988).

Google Scholar

[19] LeSage James, Banerjee Sudipto, Manfred M. Fischer et al. Computational Statistics & Data Analysis, Vol. 53, No. 8 (2009), pp.2781-2785.

DOI: 10.1016/j.csda.2008.11.001

Google Scholar

[20] J. K. Ord, Journal of the American Statistical Association, Vol. 70, No. 349, (1975) , p.120–126.

Google Scholar

[21] L. Anselin, and R. Moreno, Regional Science and Urban Economics, Vol. 33, No. 5 (2003), pp.595-618.

Google Scholar