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Research of traffic flow multi-objectives intelligent control method for junction network

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Abstract

Junction network is a special type of roadwork pattern that scatters and distributes around the specific zone of metropolitan and in that contains different grade and functional roads of arterial road, urban freeway and expressway. Intelligent control is new development where the control problem is to find the combination of control measures that result for the best road performance and control effectiveness. The problems of multi-objective coordinated metering and evaluation for local ramp is considered. This paper discusses the optimal coordination of mainline and ramp, a modified ramp latency model is posed using the method of queuing theory, and a ramp control with better mechanism compare to artificial neural network using radical based function-support vector machine algorithm is designed. With in-situ traffic flow data of Beijing ring and radial freeway during high-density period, three known and the designed novel methodologies are compared, the intensive simulations show the effectiveness of the proposed approach, particularly at the aspect of minimize reduplicated waiting time for junction network. using these methodologies demonstrates the comparative control efficiency and accuracy.

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References

  1. The Minnesota Department of Transportation (Mn/DOT) & Cambridge Systematics, Inc. (2001). Twin cities ramp meter evaluation final report.

  2. Papageorgiou, M., & Kotsialos, A. (2002). Freeway ramp metering: an overview. IEEE Transactions on Intelligent Transportation Systems, 3, 271–281.

    Article  Google Scholar 

  3. Ben-Akiva, M., Cuneo, D., Hasan, M., et al. (2003). Evaluation of freeway control using a microscopic simulation laboratory. Transportation Research. Part C, Emerging Technologies, 11, 29–50.

    Article  Google Scholar 

  4. Papamichail, I., Kotsialos, A., Margonis, I., et al. (2010). Coordinated ramp metering for freeway networks—a model-predictive hierarchical control approach. Transportation Research. Part C, Emerging Technologies, 18, 311–331.

    Article  Google Scholar 

  5. Papageorgiou, M., Hadj-Salem, H., & Middleham, F. (1997). ALINEA local ramp metering: summary of the field results. Transportation Research Record, 1603, 90–98.

    Article  Google Scholar 

  6. Kotsialos, A., & Papageorgiou, M. (2004). Efficiency and equity properties of freeway network-wide ramp metering with AMOC. Transportation Research. Part C, Emerging Technologies, 12, 401–420.

    Article  Google Scholar 

  7. Kotsialos, A., Papagorgiou, M., Mangeas, M., et al. (2002). Coordinated and integrated control of motorway networks via non-linear optimal control. Transportation Research. Part C, Emerging Technologies, 10, 65–84.

    Article  Google Scholar 

  8. Yuan, L., & Kreer, J. (1971). Adjustment of freeway ramp metering rates to balance entrance ramp queues. Transportation Research. Part A, Policy and Practice, 5, 127–133.

    Google Scholar 

  9. Gzara, F., & Erkut, E. (2011). Telecommunications network design with multiple technologies. Telecommunication Systems, 46(2).

  10. Abdel-Aty, M., Dhindsa, A., & Gayah, V. (2007). Considering various ALINEA ramp metering strategies for crash risk mitigation on freeways under congested regime. Transportation Research. Part C, Emerging Technologies, 15, 113–134.

    Article  Google Scholar 

  11. Bellemans, T., Schutter, B. D., & Moor, B. D. (2006). Model predictive control for ramp metering of motorway traffic: a case study. Control Engineering, 14, 757–767.

    Article  Google Scholar 

  12. Rahka, H., & Aerde, M. V. (1997). Statistical analysis of day to day variations in real-time traffic flow data. Transportation Research Record, 1510, 26–34.

    Google Scholar 

  13. Hou, Z. (1997). The model-free learning adaptive control of a class of SISO nonlinear system. In Proceedings of American control conference (pp. 343–344). New York: IEEE.

    Google Scholar 

  14. Mei, C.-q., Huang, H.-j., & Tang, T.-q. (2008). A cellular automation model for studying the on-ramp control of highway. Acta Physica Sinica, 57(8), 4786–4793.

    Google Scholar 

  15. Pooran, F. J., & Lieu, H. C. (1994). Evaluation of system operating strategies for ramp metering and traffic signal coordination. In Proceedings of the IVHS America annual meeting, America (Vol. 2, pp. 528–534).

    Google Scholar 

  16. Srivastava, S. (2011). Quality of service based protection in optical networks using diversity constraint. Telecommunications Systems, 46(4), 317–331.

    Article  Google Scholar 

  17. Tian, Z. (2007). Modeling and implementation of an integrated ramp metering-diamond interchange control system. Journal of Transportation System Engineering and Information, 7(1), 61–72.

    Article  Google Scholar 

  18. Yang, X.-g. (1999). An approach to dynamic traffic control system for urban expressway with constraint of queue length of on-ramp & off-ramp. Journal of Xi’an Highway University, 19(2), 20–26.

    Google Scholar 

  19. Dai, H., Yang, Z.-s., & Xiao, P.-p. (2006). Application of genetic algorithm in coordination optimization model between traffic flow guidance and traffic control. Journal of Jilin University (Engineering and Technology Edition), 36(Suppl.), 157–160.

    Google Scholar 

  20. Zhang, X.-x. (2005). Simulation evaluation and optimization of on-ramp control algorithm in freeway. Doctoral dissertation, Jilin University, Changchun.

  21. Qi, C., Hou, Z.-s., & Bu, X.-h. (2009). Analysis on excessive queue on ramp and its solution. Journal of Transportation System Engineering and Information Technology, 9(2), 38–43.

    Google Scholar 

  22. Zheng, F., Du, Y. C., & Sun, L. J. (2009). ALINEA based traffic responsive local-metering strategy for expressway in Shanghai. Journal of Tongji University (Natural Science), 37(6), 766–771.

    Google Scholar 

  23. Lighthill, M. J., & Whitham, G. B. (1955). On kinematic waves II. A theory of traffic flow on long crowded roads. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 22, 317–345.

    Google Scholar 

  24. Richards, P. I. (1956). Shock waves on the highway. Operations Research, 4(1), 42–51.

    Article  Google Scholar 

  25. Papagorgiou, M. (1990). Modelling and realtime control of traffic flow on the southern part of boulevard peripherique in Paris: part: modelling. Transportation Research. Part A, Policy and Practice, 5, 345–359.

    Google Scholar 

  26. Du, Y. X., & Wu, T. J. (2003). Support vector machine for pattern recognition. Journal of Zhejiang University, 37(5), 521–527.

    Google Scholar 

  27. Zhang, X. G. (2000). Introduction to statistical learning theory and support vector machine. Acta Automation Sinica, 26(1), 32–42.

    Google Scholar 

  28. Abdel-Aty, M., Dhindsa, A., & Gayah, V. (2007). Considering various ALINEA ramp metering strategies for crash risk mitigation on freeways under congested regime. Transportation Research. Part C, Emerging Technologies, 15, 113–134.

    Article  Google Scholar 

  29. Zhao, N. L., Yu, L., Geng, Y. B., et al. (2007). Support vector machine-based approach to data-layer multi-source ITS data fusion. Journal of Transportation Systems Engineering and Information Technology, 7(2), 32–38.

    Article  Google Scholar 

  30. Wang, Y. B., Papageorgiou, M., Messmer, A., et al. (2009). An adaptive freeway traffic state estimator. Automatica, 45, 10–24.

    Article  Google Scholar 

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Acknowledgements

The work is supported by (1): Chinese National High-Tech Research and Development Plan (“863 Program”, 2007AA11Z213) under the title Dynamic Information Sharing Platform and Coordinated Control Technology Research for Freeway and related Urban Expressway; and (2): Science and Technology Research Innovation Foundation for Excellent Ph.D. Candidate of Beijing Jiaotong University (2008-141052522) under the title Coordinated Multi-ramps Metering Modelling and Micro-simulation Techniques Research for Urban Periphery Freeway Network using DDGIS (Dynamic and Distributed Geography Information Systems).

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Authors and Affiliations

  1. Signal & Communication Research Institute, China Academy of Railway Sciences, Beijing, China

    Feng Chen & Qi Zhang

  2. School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China

    Yuanhua Jia & Jian Li

Authors
  1. Feng Chen
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  2. Qi Zhang
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  3. Yuanhua Jia
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  4. Jian Li
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Corresponding author

Correspondence to Feng Chen.

Additional information

The authors gratefully acknowledge the support of the Chinese National High-Tech Research and Development Plan.

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Chen, F., Zhang, Q., Jia, Y. et al. Research of traffic flow multi-objectives intelligent control method for junction network. Telecommun Syst 53, 77–84 (2013). https://doi.org/10.1007/s11235-013-9679-0

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  • DOI: https://doi.org/10.1007/s11235-013-9679-0

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