Propuesta de un modelo para la predicción de ruido por tráfico rodado a partir de aprendizaje automático que ofrezca información potencialmente útil en los procesos de gestión ambiental del ruido en entornos urbanos de Bogotá

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dc.contributor.advisorMontenegro Marín, Carlos Enrrique
dc.contributor.authorAcosta Agudelo, Óscar Esneider
dc.date.accessioned2024-10-29T21:01:53Z
dc.date.available2024-10-29T21:01:53Z
dc.date.created2024-09-06
dc.descriptionEn la presente investigación se propuso un modelo basado en aprendizaje automático para la predicción de ruido por tráfico rodado en Bogotá. En su planteamiento, se consideran condiciones propias del tránsito vehicular de la ciudad. Las variables de entrada del modelo fueron: aforos vehiculares, rapidez, tipo de flujo y número de carriles. La obtención de los datos de entrada se realizó a través de campañas de medición en las cuales se tomaron grabaciones de audio y video. Las capturas de audio, efectuadas con un micrófono de medición calibrado a 4 metros de altura, permitieron calcular los niveles de ruido mediante el procesamiento por software. Por otro lado, con los datos de video se organizó el conteo y la clasificación del aforo vehicular en cuatro categorías: motos, vehículos livianos, medianos y pesados. El mencionado proceso se llevó a cabo empleando un clasificador entrenado con imágenes de vehículos tomados en campo y de bases de datos libres. Asimismo, con los datos de video se estimó la rapidez vehicular con un algoritmo de procesamiento basado en clasificador de imágenes. Luego, se analizaron las mediciones para unos puntos de medición caracterizando la emisión de ruido de las categorías vehiculares, las vías arterias e intermedias, las situaciones de tránsito y los pavimentos. Posteriormente, con el análisis exploratorio de los datos se encontraron las correlaciones y se hizo un estudio de regresión entre el nivel de ruido y las variables predictoras. Asimismo, con el objetivo de establecer el algoritmo de aprendizaje automático a usar, se compararon cinco modelos que fueron configurados con sus respectivos hiperparámetros, logrados a través de búsqueda de malla. Los resultados determinaron que la regresión basada en perceptrón multicapa (MLP) presentó el mejor ajuste, con un MAE de 0,86 dB para los datos de prueba. Finalmente, el regresor MLP propuesto fue comparado con algunos modelos estadísticos clásicos utilizados para la predicción de ruido por tráfico rodado. Como conclusión principal se destaca que el regresor MLP obtuvo los mejores indicadores de error y ajuste con respecto a los modelos estadísticos.
dc.description.abstractThis research proposed a model based on machine learning for predicting road traffic noise for the city of Bogota. The model considers conditions typical of vehicular traffic in the city. The input variables of the model were: vehicle capacity, speed, type of flow and number of lanes. Measurement campaigns involving audio and video recordings were carried out to obtain the input data. The audio recordings allowed the calculation of the noise levels through software processing, since they were taken with a measuring microphone calibrated at a height of 4 meters. On the other hand, the video data were used to count and classify the number of vehicles in four categories: motorcycles, light, medium and heavy vehicles. This process was done using a classifier trained with images of vehicles taken in the field and from free databases. Similarly, a processing algorithm based on an image classifier was used to estimate the speed of the vehicles from the video data. Then, the analysis of the measurements was carried out for some measuring points characterizing the noise emission of vehicle categories, arterial and secondary roads, traffic situations and pavements. Then, through exploratory data analysis, correlations were found, and a regression study was performed between noise levels and predictor variables. To determine the machine learning algorithm to be used, five models were compared, configured with their respective hyperparameters obtained through mesh search. The results showed that the Multilayer Perceptron (MLP) regression had the best fit with MAE = 0,86 dB for the test dataset. Finally, the proposed MLP regressor was compared with classical statistical models for traffic noise prediction. In conclusion, the MLP regressor obtained the best error and fit indicators with respect to statistical models.
dc.format.mimetypepdf
dc.identifier.urihttp://hdl.handle.net/11349/42510
dc.language.isospa
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dc.rights.accesoAbierto (Texto Completo)
dc.rights.accessrightsOpenAccess
dc.subjectRuido
dc.subjectTrafico
dc.subjectRodado
dc.subjectAprendizaje
dc.subjectAutomatico
dc.subjectRegresión
dc.subjectRedes
dc.subjectNeuronales
dc.subjectArtificiales
dc.subject.keywordNoise
dc.subject.keywordRoad traffic
dc.subject.keywordMachine learning
dc.subject.keywordRegression
dc.subject.keywordArtificial Neural Networks
dc.subject.keywordLearning
dc.subject.keywordNetworks
dc.subject.lembDoctorado en Ingeniería -- Tesis y disertaciones académicas
dc.subject.lembPredicción de ruido por tráfico rodado
dc.subject.lembAprendizaje automático en gestión ambiental
dc.subject.lembModelos de regresión para predicción de ruido
dc.titlePropuesta de un modelo para la predicción de ruido por tráfico rodado a partir de aprendizaje automático que ofrezca información potencialmente útil en los procesos de gestión ambiental del ruido en entornos urbanos de Bogotá
dc.title.titleenglishProposal of a model for predicting road traffic noise through machine learning, providing potentially useful information in the processes of environmental management of noise in urban environments of Bogota
dc.typedoctoralThesis
dc.type.coarhttp://purl.org/coar/resource_type/c_db06
dc.type.degreeInvestigación-Innovación
dc.type.driverinfo:eu-repo/semantics/doctoralThesis

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