dc.contributor.author | Namulyowa, Berna | |
dc.date.accessioned | 2021-04-28T14:18:41Z | |
dc.date.available | 2021-04-28T14:18:41Z | |
dc.date.issued | 2020-12-10 | |
dc.identifier.citation | Namulyowa, B. (2020). Design of a machine learning based traffic control system. (Unpublished undergraduate dissertation). Makerere University, Kampala, Uganda. | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12281/10445 | |
dc.description | A research report submitted in partial fulfillment of the requirements for the award of the degree of Bachelor of Science in Computer Engineering | en_US |
dc.description.abstract | This report investigates the problem of traffic jam at one of the junctions in Kampala Uganda
which is Wandegeya junction. At Wandegeya junction, the existing traffic light control causes
long delay, air pollution, energy waste, accident and many more complications. The Government of Uganda through Kampala Capital City Authority (KCCA) has tried to solve this
problem using different technologies like radar but all those solutions did not help. In this
project, we studied the traffic signal’s duration based on the data we collected manually by
counting cars and data we got from KCCA. We developed a machine learning-based model to
control the traffic light(agent). In the model, Q-learning algorithm was used which is a free
reinforcement algorithm to learn the actions of the agent and powers neural network to predict
better actions to take. The model was evaluated via a simulator that is Simulation of Urban
Mobility (SUMO) in a vehicular network, and the simulation results show the efficiency of our
model in controlling traffic lights. | en_US |
dc.language.iso | en | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Neutral networks | en_US |
dc.subject | Machine learning-based | en_US |
dc.subject | Traffic control system | en_US |
dc.title | Design of a Machine Learning Based Traffic Control System | en_US |
dc.type | Thesis | en_US |