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A kind of coordinated evolution measurement model for traffic network based on complexity degree

    Qizhou Hu Affiliation
    ; Minjia Tan Affiliation

Abstract

Coordinated evolution is a process with complexity, temporality, spatiality, and continuity. The existed methods cannot relevantly satisfy and measure the degree of coordinated evolution in real conditions. Aiming at solving the coordinated evolution problems for the urban traffic network, the information complexity must be evaluated, this paper uses the multi-dimensional connection number for compressing the factors of traffic network. Firstly, the basic characteristics of traffic network are analysed on the definition of traffic information complexity. The traffic network measurement model is established based on the information entropy, and the coordinated evolution process of the multi-layer urban traffic network is analysed for defining the ordered parameters of the traffic network. Then the coordinated measurement model for the multi-layer traffic network is constructed by the ordered parameters. In addition, we set up a coordinated evolution model according to the proposed estimation criteria of the ordered parameters and the theory of the multi-dimensional connection numbers. The case analysis shows that the order degree of Hangzhou traffic network is 0.7929, which approaches to 1 as while the comprehensive coordinated index of Hangzhou multi-layer traffic network is 0.3323, which clearly and intuitively gives a measurement value for the multi-layer urban traffic network. The result is also effectively verified the validity of the proposed models.

Keyword : urban traffic, traffic network, traffic information complexity, coordinated evolution, complexity degree

How to Cite
Hu, Q., & Tan, M. (2020). A kind of coordinated evolution measurement model for traffic network based on complexity degree. Transport, 35(4), 389-400. https://doi.org/10.3846/transport.2020.13626
Published in Issue
Oct 6, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Batishcheva, O.; Ganichev, A. 2018. Measures to improve traffic safety at road junctions, Transportation Research Procedia 36: 37–43. https://doi.org/10.1016/j.trpro.2018.12.040

Brito, A. J.; De Almeida, A. T. 2012. Modeling a multi-attribute utility newsvendor with partial backlogging, European Journal of Operational Research 220(3): 820–830. https://doi.org/10.1016/j.ejor.2012.02.027

Chen, C. 2013. Road public transportation planning based on rail transit network in Shanghai, Urban Mass Transit 16(9): 87–90. (in Chinese). https://doi.org/10.3969/j.issn.1007-869X.2013.09.021

Chen, S.; Liu, D.; Jiang, Y. 2013. The optimization methodology of bus network around city-metro corridors based on the key performance indicator analysis, Modern Urban Research (1): 23–28. (in Chinese). https://doi.org/10.3969/j.issn.1009-6000.2013.01.007

Codina, E.; Marín, Á.; Cadarso, L. 2014. Robust infrastructure design in rapid transit rail systems, Transportation Research Procedia 3: 660–669. https://doi.org/10.1016/j.trpro.2014.10.045

Ferreira, S.; Couto, A. 2013. Urban road network safety model at the transportation planning process, Journal of Transportation Safety & Security 5(1): 46–65. https://doi.org/10.1080/19439962.2012.710300

Hassannayebi, E.; Sajedinejad, A.; Mardani, S. 2014. Urban rail transit planning using a two-stage simulation-based optimization approach, Simulation Modelling Practice and Theory 49: 151–166. https://doi.org/10.1016/j.simpat.2014.09.004

Hu, Q.-Z.; Sun, X. 2013. Model for traffic congestion state monitor in urban road network based on multi-dimension connection number, China Journal of Highway and Transport 26(6): 143–149. (in Chinese). https://doi.org/10.3969/j.issn.1001-7372.2013.06.020

Jiang, G.-X. 2014. High efficient traffic planning based on guidance of multi-vector network, Computer Simulation 31(4): 192–195. (in Chinese). https://doi.org/10.3969/j.issn.1006-9348.2014.04.044

Krstić Simić, T.; Babić, O. 2015. Airport traffic complexity and environment efficiency metrics for evaluation of ATM measures, Journal of Air Transport Management 42: 260–271. https://doi.org/10.1016/j.jairtraman.2014.11.008

Li, Y.; Guo, H. L.; Li, H.; Xu, G. H.; Wang, Z. R.; Kong, C. W. 2010. Transit-oriented land planning model considering sustainability of mass rail transit, Journal of Urban Planning and Development 136(3): 243–248. https://doi.org/10.1061/(ASCE)0733-9488(2010)136:3(243)

Li, Z.; Chen, X.; Ji, S. 2013. Transit network optimization method based on BRT attracting region – a case study of Changzhou, Journal of Wuhan University of Technology (Transportation Science & Engineering) 37(3): 491–495. (in Chinese). https://doi.org/10.3963/j.issn.2095-3844.2013.03.010

Lindau, L. A.; Hidalgo, D.; De Almeida Lobo, A. 2014. Barriers to planning and implementing bus rapid transit systems, Research in Transportation Economics 48: 9–15. https://doi.org/10.1016/j.retrec.2014.09.026

Liu, W.-B. 2011. Road public traffic network optimization model based on rail transit network, Journal of Xihua University (Natural Science Edition) 30(1): 12–15. (in Chinese). https://doi.org/10.3969/j.issn.1673-159X.2011.01.004

Liu, Y.; Zhu, N.; Ma, S.-F. 2015. Simultaneous optimization of transit network and public bicycle station network, Journal of Central South University 22(4): 1574–1584. https://doi.org/10.1007/s11771-015-2674-8

Radanovic, M.; Piera-Eroles, M. A.; Koca, T.; Ramos Gonzá lez, J. J. 2018. Surrounding traffic complexity analysis for efficient and stable conflict resolution, Transportation Research Part C: Emerging Technologies 95: 105–124. https://doi.org/10.1016/j.trc.2018.07.017

Shih, H.-S.; Shyur, H.-J.; Lee, E. S. 2007. An extension of TOPSIS for group decision making, Mathematical and Computer Modelling 45(7–8): 801–813. https://doi.org/10.1016/j.mcm.2006.03.023

Sun, F.-P. 2014. Comprehensive evaluation model of multi-step construction of urban rail transit, Transportation Standardization (14): 11–13. (in Chinese). https://doi.org//10.16503/j.cnki.2095-9931.2014.14.004

Suzuki, K.; Kanda, U.; Doi, K.; Tsuchizaki, N. 2012. Proposal and application of a new method for bicycle network planning, Procedia – Social and Behavioral Sciences 43: 558–570. https://doi.org/10.1016/j.sbspro.2012.04.129

Wang, J.; Li, S. 2017. Modeling and simulation of network traffic flow evolution based on incomplete information feedback strategies in the ATIS environment, International Journal of Modeling, Simulation, and Scientific Computing 8(3): 1750031. https://doi.org/10.1142/S1793962317500313

Wang, Q.-P.; Zheng, A.-L. 2005. Tentative plan for building city cycle-traffic network, Urban Problems (6): 85–89. (in Chinese). https://doi.org/10.3969/j.issn.1002-2031.2005.06.019

Yu, X.-X.; Li, Y.; Lu, H.-P. 2013. A model for discrete traffic network design based on real option, Journal of Highway and Transportation Research and Development 30(7): 126–132. (in Chinese). https://doi.org/10.3969/j.issn.1002-0268.2013.07.021

Zeng, M.-H.; Li, X.-M. 2011. Optimal allocation approach of transport network resource based on hierarchical property, Journal of Central South University 42(1): 247–253. (in Chinese).

Zhang, B.; Deng, W. 2012. SVM evaluation method of transportation network in economic circle, Journal of Southeast University (Natural Science Edition) 42(6): 1227–1232. (in Chinese). https://doi.org/10.3969/j.issn.1001-0505.2012.06.037

Zhang, K.; Qin, B.-B.; Liu, Y.-S.; Zhang, Q. 2014. Research on the evaluation of urban rail transit network, Journal of Railway Engineering Society (3): 97–101. (in Chinese). https://doi.org/10.3969/j.issn.1006-2106.2014.03.018

Zhao, Y.; Du, W.; Chen, S. 2009. Application of complex network theory to urban transportation network analysis, Urban Transport of China 7(1): 57–65. (in Chinese). https://doi.org/10.3969/j.issn.1672-5328.2009.01.017

Zhou, Y.; Deng, W.; Hu, Q. 2011. Study on the optimization of public transit network based on genetic algorithm and tabu search algorithm, Journal of Wuhan University of Technology (Transportation Science & Engineering) 35(1): 42–45. (in Chinese). https://doi.org/10.3963/j.issn.1006-2823.2011.01.010

Zhu, W.-T.; Bian, Z.-Y.; Li, H.-P. 2014. The optimization scheme of public traffic network based on information entropy and TOPSIS, Journal of Xihua University (Natural Science Edition) (5): 94–97. (in Chinese). https://doi.org/10.3969/j.issn.1673-159X.2014.05.021