Cluster Computing

, Volume 22, Supplement 3, pp 5919–5929 | Cite as

The simulation model on delay time of road accessibility based on intelligent traffic control system

  • Zhichao LiEmail author
  • Ru Jia
  • Jilin Huang


After the Chinese government launched the policy of opening residential, the effects of easing traffic congestion have been widely discussed. In this paper, the impact of the open area on the surrounding road capacity is studied and analyzed quantitatively, and some feasible suggestions are put forward. First of all, we use the road congestion indicators to reflect the road traffic situation. And according to the three indexes to build an evaluation index system that can evaluate the influence of opening residential quarter on the surrounding road capacity. After determining the final evaluation indexes, the analytic hierarchy process is used to determine their standard weights. Second, build a mathematical model of vehicle traffic. The crossing time of vehicles is used to measure the traffic condition. Last but not least, suppose that after opening the residential quarter, there will be more vehicles, with the increase of the number of vehicles, the road saturation will increase, which will lead to the increase of traffic time. In the paper, three typical opening road structures are established to specific analyze the changes of the crossing time of vehicles, and then, use model one and model two to solve it. Opening residential quarter has a certain positive effect on the surrounding road capacity, on the condition of the number of connections between the opening residential quarter and the surrounding roads is not more than two. Otherwise, it will increase road congestion. In spite of a certain positive effect that opening residential quarter brings to the surrounding road capacity, blindly increase the number of opening residential quarter roads, road congestion may be increased.


Opening residential quarter Smart traffic control system Delay time Road structure Road capacity 


  1. 1.
    Hansen, W.G.: How accessibility shapes land use. J. Am. Inst. Plan. 25(2), 73–76 (1959)CrossRefGoogle Scholar
  2. 2.
    Hägerstrand, T.: What about people in regional science. Papers Reg. Sci. Assoc. 24, 7–21 (1970)CrossRefGoogle Scholar
  3. 3.
    Alisdair, R.: A Dictionary of Human Geography. Oxford University Press, Oxford (2013)Google Scholar
  4. 4.
    Landoman, K.: Gated communities and urban sustainability: taking a closer look at the future. In: Ponencia presentada en la 2nd Southern African Conference on Sustainable Development in the Built Environment. Pretoria, pp. 23–25 (2000)Google Scholar
  5. 5.
    Handy, S., Paterson, R.G., Butler, K.S.: Planning for street connectivity: getting from here to there. Apa Plan. Advis. Serv. Rep. 515, 1–75 (2003)Google Scholar
  6. 6.
    Moore, T., Thorsnes, P., Appleyard, B., et al.: The transportation/land use connection. Apa Plan. Advis. Serv. Rep. 546, 448–449 (1994)Google Scholar
  7. 7.
    Roitman, S.: Gated communities: definitions, causes and consequences. Urban Des. Plan. 163(1), 31–38 (2010)Google Scholar
  8. 8.
    Cai, X., Lu, L., Lu, J.J., et al.: Impacts of access density on traffic capacity of arterial roads. In: ICTE 2013: Safety, Speediness, Intelligence, Low-Carbon, Innovation, pp. 359–364 (2013)Google Scholar
  9. 9.
    He, S., Li, S., Sun, Y., et al.: Effects of residence community opening on road traffic. Int. J. Comput. Eng. pp. 64–66 (2016)Google Scholar
  10. 10.
    Vega, A., Reynolds-Feighan, A.: A methodological framework for the study of residential location and travel-to-work mode choice under central and suburban employment destination patterns. Transp. Res. Part A 43(4), 401–419 (2009)Google Scholar
  11. 11.
    Aktas, C.B., Bilec, M.M.: Impact of lifetime on US residential building LCA results. Int. J. Life Cycle Assess. 17(3), 337–349 (2012)CrossRefGoogle Scholar
  12. 12.
    Kammoun, H.M., Kallel, I., Casillas, J., et al.: Adapt-traf: an adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model. Transp. Res. Part C 42, 147–167 (2014)CrossRefGoogle Scholar
  13. 13.
    Guzman, W., Young, L., Peszynski, K.: Departure side platforms: a road congestion mitigation measure. In: 33rd Australian Institutes of Transport Research Conference (CAITR 2015), Melbourne University, Melbourne, Australia, pp. 12–13 (2015)Google Scholar
  14. 14.
    Xu, J., Sun, W., Shibata, N., et al.: GreenSwirl: combining traffic signal control and route guidance for reducing traffic congestion. In: Vehicular Networking Conference (VNC), 2014 IEEE. IEEE, pp. 175–182 (2014)Google Scholar
  15. 15.
    Li, X.P.: Urban Traffic Congestion Countermeasures Research on Traffic Opening of Enclosed Residential District. Changsha University of Science and Technology, Changsha (2014)Google Scholar
  16. 16.
    Grant-Muller, S., Xu, M.: The role of tradable credit schemes in road traffic congestion management. Transp. Rev. 34(2), 128–149 (2014)CrossRefGoogle Scholar
  17. 17.
    Li, C., Anavatti, S.G., Ray, T.: Analytical hierarchy process using fuzzy inference technique for real-time route guidance system. IEEE Trans. Intell. Transp. Syst. 15(1), 84–93 (2014)CrossRefGoogle Scholar
  18. 18.
    Xinyu, L., Yujiao, Z., Chaoyang, L.: Research on the transportation characteristics of residential quarter in Suzho. J. Appl. Sci. 13(16), 3325–3329 (2013)CrossRefGoogle Scholar
  19. 19.
    Jin, S., Qu, X., Xu, C., et al.: Dynamic characteristics of traffic flow with consideration of pedestrians’ road-crossing behavior. Phys. A 392(18), 3881–3890 (2013)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Kang, M., Bao, P., Cai, Y.: Effect of residential quarters opening on urban traffic from the view of mathematical modeling. Open J. Model. Simul. 5(01), 59–69 (2016)CrossRefGoogle Scholar
  21. 21.
    Lécué, F., Tucker, R., Bicer, V., et al.: Predicting severity of road traffic congestion using semantic web technologies. In: European Semantic Web Conference. Springer International Publishing, pp. 611–627 (2014)Google Scholar
  22. 22.
    De Donato, W., Pescapé, A., Dainotti, A.: Traffic identification engine: an open platform for traffic classification. IEEE Netw. 28(2), 56–64 (2014)CrossRefGoogle Scholar
  23. 23.
    Rothkrantz, L.: Dynamic routing using maximal road capacity. In: Proceedings of the 16th International Conference on Computer Systems and Technologies. ACM, pp. 30–37 (2015)Google Scholar
  24. 24.
    Fan, Y.L., Cheng, B.C., Cao, L.L., et al.: City occupancy and road capacity model. Adv. Mater. Res. 1044, 1538–1540 (2014)CrossRefGoogle Scholar
  25. 25.
    Winters, T.: LITS: Lightweight intelligent traffic simulator, network-based information systems. In: NBIS International Conference, pp. 386–390 (2009)Google Scholar
  26. 26.
    Munden, R.: ASIC and FPGA Verification: A Guide to Component. Academic Press, Cambridge (2001)Google Scholar
  27. 27.
    El-Medany, W.M., Hussain, M.R.: FPGA-bases advanced real traffic light controller system design. In: Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2007: 4th IEEE Workshop, pp. 100–105 (2007)Google Scholar
  28. 28.
    Shaked, A., Sutton, J.: Involuntary unemployment as a perfect equilibrium in a bargaining model. Econometrica 52(6), 1351–1364 (1984)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Von Stackelberg, H.: The Theory of the Market Economy. Oxford University Press, Oxford (1952)Google Scholar
  30. 30.
    Victorian Auditor-General’s Office.: Managing traffic congestion. Victorian Auditor-General’s report. (2013)

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Political Science and Public AdministrationUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations