, Volume 22, Issue 2, pp 435–462 | Cite as

Multi-vehicles dynamic navigating method for large-scale event crowd evacuations

  • Zhi Cai
  • Fujie Ren
  • Yuanying Chi
  • Xibin Jia
  • Lijuan Duan
  • Zhiming Ding


In recent years, the number of motor vehicles in the country has risen rapidly. Traffic demand has continued to grow, while land resources, capital and energy have become increasingly tense. The development trend of urban transport system is not optimistic. Especially in the face of major events, vehicle evacuation suddenly increased, which is a serious planning for the evacuation of a serious challenge. Reasonable and accurate implementation of the emergency evacuation plan is to make the evacuation time at least, to minimize the major traffic congestion protection. Among them, route selection and traffic flow distribution are the core contents of emergency evacuation plan. In this paper, traffic emergency evacuation of major activities is taken as the research object, and the state vector of each road is introduced into the navigation system according to the linking analysis algorithm thought. The network model of evacuation route selection is established by using the theory of spatial diversity and the theory of minimum cost maximum flow, and an empirical analysis is made on the road network. Based on the research, the emergency evacuation plan for large-scale activities is proposed, and the theory and method system of emergency evacuation are enriched, which can help with formulating more reasonable and effective traffic management policies according to the characteristics of emergency evacuation, meanwhile we provide decision-making basis for urban emergency evacuation transportation planning and management. An experimental evaluation is also conducted with the real data from city of Beijing, in aspects of effectiveness and efficacy.


Emergency evacuation Dynamic navigation Path selection Vector Large-scale event 



The work was partially supported by the National Key R&D Program of China under grant number 2017YFC0-803300, Beijing Natural Science Foundation under Grant 4172004, National Natural Science Foundation of China under grant number 91646201, 91546111, Beijing Municipal Education Commission Science and Technology Program under grant number KZ201610005009 and KM201610005022.


  1. 1.
    Ardakani MK, Sun L (2012) Decremental algorithm for adaptive routing incorporating traveler information. Comput Oper Res 39(12):3012–3020CrossRefGoogle Scholar
  2. 2.
    Campos VBG, Da Silva PAL, Netto POB (2000) Evacuation transportation planning: a method of identify optimal independent routes. Urban Transport V 3:555–564Google Scholar
  3. 3.
    Casasent D (2014) Intelligent robots and computer vision XXXI: algorithms and techniques. Proc SPIE 9025(2):236–236Google Scholar
  4. 4.
    Chen YM, Xiao DY (2008) Real-time traffic management under emergency evacuation based on dynamic traffic assignment. In: IEEE international conference on automation and logistics, pp 1376–1380Google Scholar
  5. 5.
    Cova TJ, Johnson JP (2003) A network flow model for lane-based evacuation routing. Transp Res A Policy Pract 37(7):579–604CrossRefGoogle Scholar
  6. 6.
    Ding C H (2017) Urban traffic emergency evacuation route optimization simulation. Comput Simul 11:99–102Google Scholar
  7. 7.
    Erick L, Brian W (2005) Modeling and performance assessment of contraflow evacuation termination points. J Transp Res Board 1922(16):118–128Google Scholar
  8. 8.
    Fan Y, Wang Q, Lu D, Jiang F (2010) An improved dijkstra algorithm used on vehicle optimization route planning. In: 2010 2nd international conference on computer engineering and technology, pp 693–696Google Scholar
  9. 9.
    Giovanna C, Giuseppe M, Antonio P, Corrado R (2016) Transport models and intelligent transportation system to support urban evacuation planning process. IET Intell Transp Syst 10(4):279–286CrossRefGoogle Scholar
  10. 10.
    Huang H, Zhu D, Ding F (2014) Dynamic task assignment and path planning for multi-auv system in variable ocean current environment. J Intell Robot Syst 74 (3):999–1012CrossRefGoogle Scholar
  11. 11.
    Juang CF, Chang YC (2011) Evolutionary-group-based particle-swarm-optimized fuzzy controller with application to mobile-robot navigation in unknown environments. IEEE Trans Fuzzy Syst 19(2):379–392CrossRefGoogle Scholar
  12. 12.
    Kala R, Shukla A, Tiwari R (2010) Fusion of probabilistic a* algorithm and fuzzy inference system for robotic path planning. Artif Intell Rev 33(4):307–327CrossRefGoogle Scholar
  13. 13.
    Konstantinidou MA, Kepaptsoglou KL, Karlaftis MG, Stathopoulos A (2015) Joint evacuation and emergency traffic management model with consideration of emergency response needs. Transportation Research Record Journal of the Transportation Research Board 2532(4):107–117CrossRefGoogle Scholar
  14. 14.
    Liu HX, Ban JX, Ma W, Mirchandani PB (2006) Model reference adaptive control framework for real-time traffic management under emergency evacuation. In: 85th annual meeting of transportation research board, pp. 221–230.
  15. 15.
    Liu Y, Lai X, Chang G-L (2006) Two-level integrated optimization system for planning of emergency evacuation. J Transp Eng 132(10):800–807CrossRefGoogle Scholar
  16. 16.
    Lv Y, Zhang X, Kang W, Duan Y (2015) Managing emergency traffic evacuation with a partially random destination allocation strategy: a computational-experiment-based optimization approach. IEEE Trans Intell Transp Syst 16(4):2182–2191CrossRefGoogle Scholar
  17. 17.
    Mo H, Tang Q, Meng L (2013) Behavior-based fuzzy control for mobile robot navigation. Math Probl Eng 2013(1):1256–1271Google Scholar
  18. 18.
    Ng M, Waller S (2010) Reliable evacuation planning via demand inflation and supply deflation. Transport Res E-Log 46(6):1086–1094CrossRefGoogle Scholar
  19. 19.
    Wang C, Pan J, Xu H, Jia J, Meng Z (2016) An improved a* algorithm for traffic navigation in real-time environment. In: Advances in databases and information systems, 18–20 Nov. 2015, Proceedings, pp 47–50.
  20. 20.
    Wang L, Yang SX, Biglarbegian M (2012) Bio-inspired navigation of mobile robots. Springer, Berlin, pp 59–68Google Scholar
  21. 21.
    Xu Y, Chen J, Li X, Luo Z (2005) Vector extraction for average total power estimation. In: Proceedings of the ASP-DAC 2005. Asia and South Pacific Design Automation Conference, 2005, pp 1086–1089.
  22. 22.
    Yamada T (2007) A network flow approach to a city emergency evacuation planning. Int J Syst Sci 27(10):931–936CrossRefGoogle Scholar
  23. 23.
    Yan B, Yang D, Ding J, Li K, Lian X (2003) An adaptive algorithm for route guidance system based on dynamic time division traffic network model. Automot Eng 25(6):606–609Google Scholar
  24. 24.
    Yang S, Hamedi M, Haghani A (2005) Online dispatching and routing model for emergency vehicles with area coverage constraints. Transportation Research Record Journal of the Transportation Research Board 1923(1):1–8CrossRefGoogle Scholar
  25. 25.
    Zhu T, Wang X (2008) Towards optimized routing approach for dynamic shortest path selection in traffic networks. In: International conference on advanced computer theory and engineering, pp 543–547Google Scholar
  26. 26.
    Zhu Z, Liu W, Liu L, Cui M, Li J (2011) A simplified real-time road network model considering intersection delay and its application on vehicle navigation. In: Proceedings of the 1st internation conference on mechanical engineering, pp 1226–1232Google Scholar

Copyright information

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

Authors and Affiliations

  • Zhi Cai
    • 1
  • Fujie Ren
    • 2
  • Yuanying Chi
    • 3
  • Xibin Jia
    • 2
  • Lijuan Duan
    • 2
  • Zhiming Ding
    • 2
  1. 1.Beijing Advanced Innovation Center for Future Internet Technology, College of Computer ScienceBeijing University of TechnologyBeijingChina
  2. 2.The College of Computer ScienceBeijing University of TechnologyBeijingChina
  3. 3.Beijing Advanced Innovation Center for Future Internet TechnologyBeijing University of TechnologyBeijingChina

Personalised recommendations