Detección de baches y su severidad usando el Video VBOX Lite y teléfonos inteligentes
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Date
Subject
smartphone
transport safety
road
asphalt
transport policy
teléfono inteligente
seguridad vial
carretera
asfalto
política del transporte
transport safety
road
asphalt
transport policy
teléfono inteligente
seguridad vial
carretera
asfalto
política del transporte
Language:
Journal Title
Journal ISSN
Volume Title
Publisher
Instituto Tecnológico de Santo Domingo (INTEC)
Los baches son un problema común en pavimentos deteriorados. Estos desniveles disminuyen la comodidad de conducción y pueden llegar a causar siniestros viales. El geolocalizar los baches y su grado severidad permitirán a los usuarios ajustar su velocidad y trayectoria en la vía deteriorada. Además, las entidades del Estado pueden planificar las intervenciones de mantenimiento en los sitios con más deterioro. Esto se podría resolver utilizando sensores como los que tienen los teléfonos celulares o el Video VBOX Lite, que tiene mayor precisión. La información recolectada por estos equipos por sí sola, no permite la geolocalización del bache o determinar su severidad; es necesario entender, procesar y evaluar esos datos para lograrlo. Por lo tanto, el objetivo de este estudio es proponer un procedimiento para detectar baches y su severidad usando el Video VBOX Lite y dos teléfonos inteligentes. Para ello, la recolección de datos se hizo en dos fases. En la fase 1, se recolectaron los baches de manera manual (posición, profundidad y diámetro). Mientras que en la fase 2 se recolectaron los datos de los sensores de los equipos colocados en un vehículo liviano. Se circuló a velocidades entre 20 a 50 km/h. Basado en estos datos, se propuso dos procedimientos uno para el Video VBOX Lite y otro para los teléfonos celulares. La precisión de los procedimientos llegó a detectar entre 71-90 % de los baches. Este procedimiento se puede adaptar como crowdsourcing para generar datos de las redes viales locales.
Potholes are a common problem on deteriorated pavements. This difference in level reduces driving comfort and can lead to road accidents. Geolocating potholes and their severity will allow users to adjust their speed and trajectory on the damaged road. In addition, the government can plan maintenance interventions in the most deteriorated sites. This could be solved by using sensors like those found in smartphones or the Video VBOX Lite, which is more accurate. The information collected by these devices does not allow the geolocation of the pothole or determine its severity; it is necessary to understand, process, and evaluate this data to achieve it. Therefore, the objective of this study is to propose a procedure to detect potholes and their severity using the Video VBOX Lite and two smartphones. Data collection was done in two phases. In phase 1, potholes were collected manually (location, depth, and diameter). In phase 2, the device's sensors, placed in a vehicle, collected the data. The information was collected at speeds between 20 to 50 km/h. Based on these data, two procedures were proposed for the devices. The precision of the procedures detected between 71-90% of potholes. This procedure can be adapted as crowdsourcing to generate data from local road networks.
Potholes are a common problem on deteriorated pavements. This difference in level reduces driving comfort and can lead to road accidents. Geolocating potholes and their severity will allow users to adjust their speed and trajectory on the damaged road. In addition, the government can plan maintenance interventions in the most deteriorated sites. This could be solved by using sensors like those found in smartphones or the Video VBOX Lite, which is more accurate. The information collected by these devices does not allow the geolocation of the pothole or determine its severity; it is necessary to understand, process, and evaluate this data to achieve it. Therefore, the objective of this study is to propose a procedure to detect potholes and their severity using the Video VBOX Lite and two smartphones. Data collection was done in two phases. In phase 1, potholes were collected manually (location, depth, and diameter). In phase 2, the device's sensors, placed in a vehicle, collected the data. The information was collected at speeds between 20 to 50 km/h. Based on these data, two procedures were proposed for the devices. The precision of the procedures detected between 71-90% of potholes. This procedure can be adapted as crowdsourcing to generate data from local road networks.
Description
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info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Art.
info:eu-repo/semantics/publishedVersion
Art.
Source
Science, Engineering and Applications; Vol. 5 No. 1 (2022): Science, Engineering and Applications; 77-107
Ciencia, Ingenierías y Aplicaciones; Vol. 5 Núm. 1 (2022): Ciencia, Ingenierías y Aplicaciones; 77-107
2636-2171
2636-218X
10.22206/cyap.2022.v5i1
Ciencia, Ingenierías y Aplicaciones; Vol. 5 Núm. 1 (2022): Ciencia, Ingenierías y Aplicaciones; 77-107
2636-2171
2636-218X
10.22206/cyap.2022.v5i1