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Hybrid Printing for the Fabrication of Smart Sensors
Published on: January 31, 2019
Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management.
Luis Cruz-Piris1, Diego Rivera2, Susel Fernandez3
1Departamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, Spain. luis.cruz@uah.es.
This study optimizes sensor placement for intelligent transportation systems using graph centrality. The proposed multi-agent system reduces sensor networks and vehicle trip durations effectively.
Area of Science:
- Transportation Engineering
- Computer Science
- Network Science
Background:
- Vehicular traffic congestion is a major societal challenge.
- Intelligent Transportation Systems (ITS) rely on sensor networks for traffic management.
- Effective sensor deployment is crucial for ITS performance.
Purpose of the Study:
- To optimize sensor placement in traffic networks using graph centrality measurements.
- To develop and evaluate a Multi-Agent System (MAS) for traffic management.
- To reduce vehicle trip duration and communication overhead in traffic networks.
Main Methods:
- Utilizing graph centrality for optimal sensor node localization.
- Implementing a MAS with traffic light, jam detection, and intersection control agents.
- Employing the Simulation of Urban MObility (SUMO) and TAPAS Cologne scenario for validation.
Main Results:
- Reduced sensor network size while maintaining comprehensive environmental data.
- Significant decrease in vehicle trip duration compared to conventional systems.
- Lowered message exchange overhead within the sensor network.
Conclusions:
- Graph centrality is an effective method for sensor network optimization in traffic management.
- The proposed MAS enhances traffic flow efficiency and reduces communication burdens.
- This approach offers a scalable and effective solution for intelligent transportation systems.

