Estudio de comportamiento peatonal basado en video: Desarrollo y prueba de los métodos

Objective The aim of this paper is to develop a computer algorithm that analyzes pedestrian behavior at an urban site in Bogota, Colombia, considering that the assessment of pedestrian behavior is a road safety priority.Methods Pedestrians were video-taped as they crossed a selected road. An algorit...

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Autores:
Barrero Solano, Lope Hugo
Sánchez Pilonieta, Alfonso
Forero Guzmán, Alejandro
Quiroga Sepúlveda, Julián Armando
Romero, Néstor
Calderón, Francisco Carlos
Felknor, Sarah
Quintana Jiménez, Leonardo Augusto
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/65519
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/65519
http://bdigital.unal.edu.co/66542/
Palabra clave:
36 Problemas y servicios sociales, asociaciones / Social problems and social services
61 Ciencias médicas; Medicina / Medicine and health
Grabación de video
peatones
validez de las pruebas
países en desarrollo
video-based assessment methods
pedestrian behavior
validity
developing nations
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:Objective The aim of this paper is to develop a computer algorithm that analyzes pedestrian behavior at an urban site in Bogota, Colombia, considering that the assessment of pedestrian behavior is a road safety priority.Methods Pedestrians were video-taped as they crossed a selected road. An algorithm was developed in order to record, from these videos, pedestrian and vehicle positions and speeds. This information made possible the identification of hazardous behaviors, which were compared through visual assessments.Results 429 pedestrians crossed the selected road at an average distance of 4.5 meters from vehicles that moved at an average speed of 21 km/h. With a maximum difference of 19 % with respect to visual assessments, the algorithm estimated that 58.5 % pedestrians crossed through non-designated locations; 62.2 % crossed near moving vehicles, and that 41.2 % ran while they were crossing the road.Conclusions Video-based analysis can be used to assess pedestrians’ behavior. Future research work should focus on improving both the accuracy and the number of safety parameters of the algorithm.