Study on Performance Measurement of Beijing Urban Expressway Based on Microwave Data

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Abstract:

The research provides an occupancy-based performance measurement for Beijing urban expressway traffic that would be beneficial for further improvement of traffic control. An analysis of the field traffic data shows that the phenomenon of speed transition happens frequently once occupancy reaches to the critical occupancy (30%). Analyzed with speed transition probability and state stability at different occupancy and speed, four traffic states could be defined as stable high-speed flow, unstable high-speed flow, unstable low-speed flow and stable low-speed flow. The performance of each traffic state is measured by transportation efficiency. The result shows that once occupancy changes from 30% to 31%, transportation efficiency drop 27.8%, representing an extra 1/4 time cost for all vehicles on road. Therefore lane occupancy should be controlled under 30% to avoid a deteriorating traffic conditions.

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1014-1022

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January 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Whitham, G. B. Linear and Nonlinear Waves . Wiley, New York. (1974).

Google Scholar

[2] Transportation Research Board. Highway Capacity Manual. Washington, D.C., (2000).

Google Scholar

[3] Kerner, B.S. The Physics of Traffic. Springer, Heidelberg., (2004).

Google Scholar

[4] Francois, M.I., Willis, A. Developing effective congestion management systems. Federal Highway Administration, Technical Report , 1995, No. 8, p.22.

Google Scholar

[5] Lomax, T., Turner, S., Shunk, G., Levinson, H.S., Pratt, R.H., Bay, P.N., Douglas, G.B. Quantifying congestion. Final Report, National Cooperative Highway Research Program, Transportation Research Board, 1997, p.184.

Google Scholar

[6] Schwartz, W.L., Suhrbier, J.H., Gardner, B.J. Data collection and analysis methods to support congestion management systems. ASCE Transportation Congress, Proceedings V. 2, San Diego, CA, 1995, p.2012+(2023).

Google Scholar

[7] Chao Chen, Zhanfeng Jia, Varaiya, P., Causes and cures of highway congestion. Control Systems, IEEE , 2011. 11, Vol. 21, No. 6, p.26, 32.

Google Scholar

[8] Wei Guan, Shuyan He. Statistical Features of Traffic Flow on Urban Freeways. Physica A, 2008, Vol. 387, pp.944-954.

DOI: 10.1016/j.physa.2007.09.036

Google Scholar

[9] Nihan, Nancy L. Evaluation of forced flows on freeways with single-loop detectors. Journal of advanced transportation, 2000, 1997, Vol. 34, No. 2, pp.269-296.

DOI: 10.1002/atr.5670340206

Google Scholar