A Pedestrian Crowd Classification Method Based on the AFC Data in the Urban Rail Transit

  • Ji-biao Zhou
  • Sheng DongEmail author
  • Peng-fei Zhao
  • Yong-rui Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)


Crowds are an important feature of high-dense Mass Rail Transit (MRT), assessing its crowding status is a critical step in crowd management. In this chapter, a pedestrian crowd classification method based on an improved ant colony clustering algorithm (ACCA) is developed for MRT systems. First, survey data from Automatic Fare Collection (AFC) regarding three statuses (check-in/check-out and sum). Second, the PCI-influenced factors were also considered in the method, which included average daily ridership intensity, the duration of crowd, and the scope of crowd influence. Third, to classify the pedestrian crowd, an improved ant colony clustering model and its solving algorithm were presented. The results show that, for the two types of time scale, the passengers’ time–space characteristics present a clear image of M, the variation trend of morning and evening peak hour is obvious in the MRT.


Mass rail transit Pedestrian crowd characteristic Pedestrian crowding index Ant colony clustering algorithm Classification 



The work described in this paper was mainly supported by the Public Technology Application Foundation of Zhejiang Province of China (No. 2016C33256), the open fund for the Key Laboratory for Traffic and Transportation Security of Jiangsu Province (No. TTS2016-04, TTS2017-07), Natural Science Foundation of Zhejiang Province, China (LY17E080013), Philosophy and Social Science Program of Zhejiang Province, China (17NDJC130YB), Natural Science Foundation of Ningbo City, China (No. 2015A610298, 2016A610112). The anonymous reviewers are appreciated for their constructive comments and valuable suggestions to improve the quality of the paper.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ji-biao Zhou
    • 1
    • 2
  • Sheng Dong
    • 1
    • 2
    Email author
  • Peng-fei Zhao
    • 3
  • Yong-rui Chen
    • 4
  1. 1.School of Civil and Transportation EngineeringNingbo University of TechnologyNingboChina
  2. 2.Key Laboratory for Traffic and Transportation Security of Jiangsu ProvinceHuaiyin Institute of TechnologyHuaiyinChina
  3. 3.Beijing Key Laboratory of Traffic EngineeringBeijing University of TechnologyBeijingChina
  4. 4.School of HighwayChang’an UniversityXi’anChina

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