Modelos de aprendizaje computacional para la predicci´on de siniestralidad vial en Bogotá D.C
Currently road accidents are a problem that negatively worsens the socioeconomic environment of any country or city, which is why all governments seek feasible solutions that allow them to reduce the risk of mortality and accidents. The solutions that many governments have implemented are based on t...
- Autores:
-
Castellanos Cardozo, Sebastian Rodrigo
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad Antonio Nariño
- Repositorio:
- Repositorio UAN
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uan.edu.co:123456789/8949
- Acceso en línea:
- http://repositorio.uan.edu.co/handle/123456789/8949
- Palabra clave:
- Aprendizaje automático
Modelos de predicción
Siniestralidad vial
Redes neuronales
Bosques aleatorios
Machine learning
Prediction models
road accident rate
Neural networks
Random forests
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
| Summary: | Currently road accidents are a problem that negatively worsens the socioeconomic environment of any country or city, which is why all governments seek feasible solutions that allow them to reduce the risk of mortality and accidents. The solutions that many governments have implemented are based on the resolution of article 74/299 “Improvement of road safety”, proclaiming the Decade of Action for Road Safety 2021-2023, held at the United Nations General Assembly |
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