Data mining for pedestrian movement in large-scale experiments

Konferenz: ISCTT 2021 - 6th International Conference on Information Science, Computer Technology and Transportation
26.11.2021 - 28.11.2021 in Xishuangbanna, China

Tagungsband: ISCTT 2021

Seiten: 9Sprache: EnglischTyp: PDF

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Jin, Chengjie; Shi, Keda (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China)

In order to study the essence of pedestrian flow under high densities, we organized one large-scale pedestrian flow experiment, and the extreme density as high as 9ped/m2 was reached. To get more understandings, the microscopic pedestrian data in the experiment, including velocities and trajectories, are particularly studied by data mining technology in this paper. For uni-directional flow, we introduced the typical examples of hyper-congested regime and over-congested regime. For bi-directional flow, the “temporary deadlock” before the lane formation and the loop oscillation after the lane formation are discussed. The dynamics revealed from the microscopic results can be a great help for related computer simulations.