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Pedestrian movement trajectories and collision avoidance strategies in interweaving pedestrian flow experiment

Citation

Liu, Shaobo (2022), Pedestrian movement trajectories and collision avoidance strategies in interweaving pedestrian flow experiment, Dryad, Dataset, https://doi.org/10.5061/dryad.c2fqz619v

Abstract

The mechanisms of Collision Avoidance(CA) behaviors in interweaving pedestrian flow movements are important for pedestrian space planning and emergency management but not well understood yet. A series of controlled interweaving pedestrian flow experiments with different pedestrian flow densities are carried out to investigate the CA behaviors, especially CA strategy choices. This dataset consists the movement trajectory and the CA strategy choices information of the participants in the experiment and was provided as a supplementary material of a journal paper submitted to “Royal Society Open Science”.

All these experiments were conducted in an outdoor public square in the campus of Wuhan University of Technology. A total of 40 students aged 18-23 years participated these experiments. The experiment includes three “groups”, representing low, medium and high density levels of interweaving pedestrian flow. Each group of experiment was repeated three times to increase the reliability of the observations. Therefore, there are totally 9 data files in this dataset, each file contains the data of one experiment.

After the experiment, the PeTrack software was used to extract pedestrians’ trajectories by identifying and tracking the coordinates of participants’ heads in the video records. This dataset provides the extracted trajectories of all the pedestrians in each experiment. Trajectory of each RP is marked as Collision Avoidance Segment (CAS) and Normal Walking Segment (NWS). During the CAS, pedestrians have shown collision avoidance strategies. Four types of CA strategies, including “deceleration”, “acceleration”, “detour”, and “stop” are manually identified in these experiments and marked in the dataset. More details of the data and the results of the study could be found in the associated journal paper. This dataset could be useful for researchers in this field.

Methods

Description of methods used for collection of data:

This dataset was provided as a supplementary material of a journal paper submitted to “Royal Society Open Science”. The citation information will be updated in this readme file when it is ready. During the pedestrian flow experiment, a high definition video camera was placed at a height of 15.7m above the site to record the experiment at 25 frames/s. The entire experiment site was recorded in an approximate top-down manner to ensure that everyone's trajectory can be clearly seen and can be extracted by pedestrian trajectory recognition algorithms.

Methods for processing the data: 

After the experiment, the PeTrack software was used to extract pedestrians’ trajectories by identifying and tracking the coordinates of participants’ heads in the video records. The PeTrack software can generate pedestrian movement trajectory information including ID of pedestrian, time, X coordinate, Y coordinate. The PeTrack software has been widely used in pedestrian empirical studies for pedestrian movement trajectory data collection and data analyses, and details of the software can be found from: Boltes M, Seyfried A. Collecting pedestrian trajectories[J]. Neurocomputing,2013, 100(JAN.16):127-133. After the trajectory data extraction, the collision avoidance strategies of each pedestrian were labeled in the data records based on manually observation of the video records.

Usage Notes

Filename: Experiment group number – repeat time number 

Column 1 – Unique ID of pedestrians in this experiment

Column 2 – “RP” or “YP” labels of pedestrians

Column 3 - time [s:frame] (25 frames per second)

Column 4 - X coordinate [cm]

Column 5 - Y coordinate [cm]

Column 6 – labels of collision avoidance strategies (NWS: Normal Walking Segment ; A:acceleration; C:deceleration; T:detour; S:stop)

Funding

National Natural Science Foundation of China, Award: 52172308

National Railway Administration of People's Republic of China, Award: KF2021-011

Shenzhen Science and Technology Innovation Committee, Award: CJGJZD20200617102602006