Customization and application of an automated data collection tool of pedestrian crossing at signalized intersections

Authors

DOI:

https://doi.org/10.58922/transportes.v32i3.2961

Keywords:

YOLO. Computer vision. Signalized intersections. Active transport.

Abstract

Pedestrian crossing during vehicular green time is a problem that still requires better understanding and investigation given the complexity of the variables involved and their interrelationships. Automated collection tools can be important for observing and for analyzing these variables and interrelationships. The main objective of this study is to customize and apply an automated tool to collect data of important variables in studies of pedestrian crossings at signalized intersections, such as vehicle headways, pedestrian delays, vehicle speeds, vehicle types, and crossing times, per lane. The tool, applied to a video of a signalized intersection in Fortaleza, consisted of the YOLOv7 and StrongSORT algorithms. The tool training mAP was approximately 90%. In total, 9427 vehicles and 723 pedestrians were tracked; the headways showed great amplitude, the average speed of vehicles was 28 km/h, and the average delay for pedestrians was 18 s. Validation with a collection tool (RUBA) showed that there were no significant differences in the two methods regarding the vehicle passage times and headways. For vehicle speeds, the differences were circa ± 6 km/h, and for the pedestrian variables, the mean of differences were up to 0.2 sec.

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Published

2024-10-15

How to Cite

de Abreu e Trez, J., Albuquerque de Sousa, C., Macêdo de Araújo, A., & Mendonça de Castro Neto, M. (2024). Customization and application of an automated data collection tool of pedestrian crossing at signalized intersections. TRANSPORTES, 32(3), e2961. https://doi.org/10.58922/transportes.v32i3.2961

Issue

Vol. 32 No. 3 (2024)

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Artigos

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Copyright (c) 2024 Juliana de Abreu e Trez, Cornélio Albuquerque de Sousa, Alessandro Macêdo de Araújo, Manoel Mendonça de Castro Neto

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