Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively. 1. Introduction Advanced urban traffic signal control systems normally use multilevel hierarchical control mechanism to simplify the network control process. In this mechanism, several key intersections of traffic network will be selected and the network will be divided into several control subareas in which the signal of intersections will be optimized according to the traffic states variation of key intersections. In 1971, Walinchus [1] firstly built the concept of “traffic control subarea.” Stockfisch [2], Pinell et al. [3] and Kell and Fullerton [4] proposed the guideline for computer signal system selection based on intersection traffic state analysis, road segment length, vehicle arrival rate, and so forth. Yagoda et al. [5] and Chang [6] defined the traffic control index and threshold value of algorithm for traffic control subarea division. However, they have not
References
[1]
R. J. Walinchus, “Real-time network decomposition and sub network interfacing,” Highway Research Record, pp. 20–28, 1971.
[2]
C. R. Stockfisch, “Guidelines for computer signal system selection in urban areas,” Traffic Engineering, vol. 43, no. 3, pp. 30–63, 1972.
[3]
C. Pinnell, J. J. DeShazo Jr., and R. L. Wilshire, “Areawide traffic control systems,” Traffic Engineering & Control, vol. 45, no. 4, pp. 16–20, 1975.
[4]
J. H. Kell and I. J. Fullerton, Manual of Traffic Signal Design, Prentice Hall, Englewood Cliffs, NJ, USA, 2nd edition, 1991.
[5]
H. N. Yagoda, E. H. Principe, C. E. Vick, and B. G. Leonard, “Subdivision of signal systems into control areas,” Traffic Engineering, vol. 43, no. 12, pp. 42–46, 1973.
[6]
E. C.-P. Chang, “Evaluation of interconnected arterial traffic signals,” Transportation Planning Journal, vol. 15, no. 1, pp. 137–156, 1986.
[7]
P. B. Hunt, D. I. Robertson, and R. I. Winton, “SCOOT—a traffic responsive method of coordinating signals,” TRL Laboratory Report 1014, 1981.
[8]
ATS Technology (HK) Limited, “SCATS Adaptive Control Introduction,” pp. 17–21, 1992.
[9]
M. Papageorgiou, C. Diakaki, V. Dinopoulou, A. Kotsialos, and Y. Wang, “Review of road traffic control strategies,” Proceedings of the IEEE, vol. 91, no. 12, pp. 2043–2067, 2003.
[10]
D. Bretherton, M. Bodger, N. Baber, and S. Controls, “SCOOT—the future [urban traffic control],” in Procceding of 12th IEEE International Conference on Road Transport Information and Control, pp. 301–306, April 2004.
[11]
A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509–512, 1999.
[12]
A.-M. Kermarrec, E. Le Merrer, B. Sericola, and G. Trédan, “Second order centrality: distributed assessment of nodes criticity in complex networks,” Computer Communications, vol. 34, no. 5, pp. 619–628, 2011.
[13]
B. W. Kernighan and S. Lin, “A efficient heuristic procedure for partitioning graphs,” The Bell Systems Technical Journal, vol. 49, pp. 291–307, 1970.
[14]
A. Pothen, H. D. Simon, and K.-P. Liou, “Partitioning sparse matrices with eigenvectors of graphs,” SIAM Journal on Matrix Analysis and Applications, vol. 11, no. 3, pp. 430–452, 1990.
[15]
M. Girvan and M. E. J. Newman, “Community structure in social and biological networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 12, pp. 7821–7826, 2002.
[16]
F. Wu and B. A. Huberman, “Finding communities in linear time: a physics approach,” European Physical Journal B, vol. 38, no. 2, pp. 331–338, 2004.
[17]
M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty, “A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data,” IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 193–199, 2002.
[18]
M. E. J. Newman, “Fast algorithm for detecting community structure in networks,” Physical Review E, vol. 69, no. 6, Article ID 066133, 5 pages, 2004.
