Sensor-based traffic control network with neural network based control system

• Main Authors: Africa, Aaron Don M., Asuncion, Francis Xavier, Tiberio, Janos Lance, Munchua, Raymund Miguel Francisco A.
• Format: text
• Published: Animo Repository 2019
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/896; https://animorepository.dlsu.edu.ph/context/faculty_research/article/1895/type/native/viewcontent
• Institution: De La Salle University

© 2019, World Academy of Research in Science and Engineering. All rights reserved. Vehicle traffic congestion is one of the major problems in today’s society. It produces negative effects such as pollution and disorganized management of traffic flow. This paper provides research on a traffic control system using sensors and a neural network. It utilizes vision-based sensors to monitor intersection congestion data and sends this data to the surrounding stoplights to optimize traffic flow. The neural network will be trained to intercept the data collected in each stoplight and control the stoplight signals to direct the cars in the most efficient way possible. The neural net will be trained via simulation and be optimized based on the average travel time of each simulated vehicle tor rate its performance.