Advertisement

Research on the Warning Threshold of Rail Transit Passenger Flow by Big Data

  • Tengfei YuanEmail author
  • Xiaoqing Zeng
  • Qipeng Xiong
  • Chaoyang Wu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 127)

Abstract

Recently there are a lot of crowd-related accidents (panic/crowd/stampede) occurring in rail transit hub, which is mainly caused by the large passenger flow. Due to the research of rail transit passenger flow warning is lack of practical value, and the relevant standards cannot be adapted to the local conditions. Therefore, this research adopts the mobile phone signaling, Wi-Fi detection technology and video image processing technology to obtain the accurate passenger flow data. Based on the research of passenger flow warning indicators, the passenger flow density is regarded as the warning indicators, so the precise region area becomes essential. Then the spatial map technology is used to divide the regions of rail transit hub and calculate the region area by the spatial geometric cross processing method. On this foundation, this paper constructs the warning thresholds of rail transit passenger flow which mainly includes the absolute warning threshold, relative warning threshold and abrupt warning threshold. Finally, the warning thresholds are verified in the Hongqiao Rail Transit Hub, which can warn the large passenger flow timely and precisely. In addition, the warning threshold not only can satisfy the demand of the passenger flow management, but also can used in different conditions.

Keywords

Rail transit Passenger flow Warning threshold Big data Spatial map technology 

Notes

Acknowledgement

The project is supported by Tongji University and the project “Research on the Practice and Improvement of the Construction Innovation Technology of Shanghai Rail Transit Line 17” (Number JS-KY18R022-7). The authors are grateful for the reviewer of initial drafts for their helpful comments and suggestions.

References

  1. 1.
    Boyce, D.: Urban transit: operations, planning, and economics, ed. by V.R. Vuchic. J. Reg. Sci. 46(3), 566–568 (2010)CrossRefGoogle Scholar
  2. 2.
    Shao, J., Shi, W.: The rights model of disabilities and shanghai rail transit - current wheelchair accessibility in shanghai rail transit. In: TRANSED 2010: 12th International Conference on Mobility and Transport for Elderly and Disabled Persons (2010)Google Scholar
  3. 3.
    An, S.: Inspiration for rail transit passenger flow forecast from the operation of beijing subway line 5. Urban Rapid Rail Transit (2008)Google Scholar
  4. 4.
    Ma, C.Q., Wang, Y.P., Guo, Y.Y., et al.: Sensitivity analysis on urban rail transit passenger flow forecast. In: International Conference on Electric Technology and Civil Engineering, pp. 1537–1541. IEEE (2011)Google Scholar
  5. 5.
    Zhou, Z., Zhang, Y.: Influence of bus fare adjustment on rail transit passenger flow. Urban Public Transp. (2009)Google Scholar
  6. 6.
    Zhang, J., Zhang, M., Wang, K.: Study on vehicle maintenance technology and equipment of suspension rail transit. Mod. Urban Transit (2016)Google Scholar
  7. 7.
  8. 8.
    Ming-Wei, H.U.: A survey and simulation of passenger flow organization of the Shenzhen urban rail transit station. In: Cota International Conference of Transportation Professionals, pp. 2991–2997 (2011)Google Scholar
  9. 9.
    Li, Z.: 2010 Shanghai world expo passenger flow balance and guidance under background of information. Public Utilities (2010)Google Scholar
  10. 10.
    He, C.L.: Passenger flow forecast based on grey markov model–a case of Xian metro liane 2. Archit. knowl. (2016)Google Scholar
  11. 11.
    Zhang, S.F., Zhang, X.: Analysis on dynamic simulation for passenger flow line organizations and optimizations in Beijing south railway station. Railw. Comput. Appl. (2010)Google Scholar
  12. 12.
    Daamen, W.: Modeling passenger flows in public transport facilities. Free Flow Speeds (2004)Google Scholar
  13. 13.
    Hong-Xia, Q.I., Zhang, M.S., Tang, Z.Q., et al.: Vector quantization based on wavelet transform grid topographic map automatic optimization research. Geomat. Spat. Inf. Technol. (2015)Google Scholar
  14. 14.
    Highway Capacity Manual (2010). http://hcm.trb.org/?qr=1. Accessed 2010

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.The Key Laboratory of Road and Traffic Engineering, Ministry of Education, School of Transportation EngineeringTongji UniversityShanghaiChina

Personalised recommendations