A smart algorithm for traffic lights intersections control in developing countries
Traffic jam is a problem that directly affects the quality of life of the population in large cities. This problem exacerbates at road intersections, where obsolete traffic control systems based on a static set of rules remain in use. We propose an algorithm that improves vehicular flow control at t...
- Autores:
-
Olaya Quiñones, José D.
Perafán Villota, Juan Carlos
- Tipo de recurso:
- Conferencia (Ponencia)
- Fecha de publicación:
- 2021
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/13995
- Acceso en línea:
- https://hdl.handle.net/10614/13995
https://red.uao.edu.co/
- Palabra clave:
- Lógica difusa
Ciudades inteligentes
Aprendizaje profundo (Aprendizaje automático)
Smart city
Traffic jam
Deep learning
Fuzzy Logic
Yolo
Unit3D
- Rights
- openAccess
- License
- Derechos reservados - Universidad Autónoma de Occidente, 2021
| Summary: | Traffic jam is a problem that directly affects the quality of life of the population in large cities. This problem exacerbates at road intersections, where obsolete traffic control systems based on a static set of rules remain in use. We propose an algorithm that improves vehicular flow control at traffic-light intersections by optimizing a dynamic allocation of times. We train our own YOLO detector using a set of images captured from traffic cameras installed at a cross-road. Based on the number of vehicles detected in each intersection road, one set of rules was created and used by a fuzzy control. Since, at the local level, there are few traffic cameras installed on intersections. We build a simulated environment both to train our detector system and verify the efficiency of our algorithm |
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