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GeoInformatica

, 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
Article
  • 194 Downloads

Abstract

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.

Keywords

Emergency evacuation Dynamic navigation Path selection Vector Large-scale event 

Notes

Acknowledgments

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.

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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

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