Examining safe spaces for pedestrians and e-bicyclists at urban crosswalks: An analysis based on drone-captured video

Wang, Yongjie; Jia, Yuqi; Chen, Wenqiang; Wang, Tao; Zhang, Airen

doi:10.1016/j.aap.2023.107365

Accident Analysis & Prevention

2024

DOI: 10.1016/j.aap.2023.107365

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Examining safe spaces for pedestrians and e-bicyclists at urban crosswalks: An analysis based on drone-captured video

Yongjie Wang,

Yuqi Jia,

Wenqiang Chen

et al.

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Cited by 5 publications

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A computer vision system for detecting motorcycle violations in pedestrian zones

Hernández-Díaz,

Peñaloza,

Rios

et al. 2024

Multimed Tools Appl

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This paper presents a system that relies on computer vision to identify instances of motorcycle violations in crosswalks utilizing CNNs. The system was trained and evaluated on a novel public dataset published by the authors, which contains traffic images classified into four categories: motorcycles in crosswalks, motorcycles outside crosswalks, pedestrians in crosswalks, and only motorbike outside. We demonstrate the viability of leveraging deep learning models such as YOLOv8 for this purpose and provide details on the training and performance of the model. This system has the potential to enable intelligent traffic enforcement to mitigate accidents in pedestrian zones; to develop the system, a dataset comprising over 6,000 images was amassed from publicly available traffic cameras and subsequently annotated. Several models, including YOLOv8, SSD, and MobileNet, were trained on this dataset. The YOLOv8 model attained the highest performance with a mean average precision of 84.6% across classes. The study presents the system architecture and training process. Results illustrate the potential of utilizing deep learning to detect traffic violations in pedestrian zones, which can promote intelligent traffic enforcement and improved safety.

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A computer vision system for detecting motorcycle violations in pedestrian zones

Hernández-Díaz,

Peñaloza,

Rios

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

A spatio-temporal deep learning approach to simulating conflict risk propagation on freeways with trajectory data

Wang,

Ge,

Wang

et al. 2024

Accident Analysis & Prevention

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A novel model for real-time risk evaluation of vehicle–pedestrian interactions at intersections

Wang,

Ge,

Wang

et al. 2024

Accident Analysis & Prevention

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Examining safe spaces for pedestrians and e-bicyclists at urban crosswalks: An analysis based on drone-captured video

Cited by 5 publications

References 58 publications

A computer vision system for detecting motorcycle violations in pedestrian zones

A computer vision system for detecting motorcycle violations in pedestrian zones

A spatio-temporal deep learning approach to simulating conflict risk propagation on freeways with trajectory data

A novel model for real-time risk evaluation of vehicle–pedestrian interactions at intersections

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