The number of vehicles on the road continuously increases, revealing a lack of robust
and effective traffic management systems in urban settings. Urban traffic makes up
a substantial portion of the total traffic problem, and current traffic light architecture has been limiting the traffic flow noticeably. This thesis focuses on developing
an artificial intelligence-based smart traffic management system using a double duelling deep Q network (DDDQN), validated through a user-controlled 3D simulation,
determining the system’s effectiveness.
This work leverages current fisheye camera architecture to present a system that
can be implemented into current architecture with little intrusion. The challenges
surrounding large computer vision datasets, and the challenges and limitations surrounding fisheye cameras are discussed. The data and conditions required to replicate
these features in a simulated environment are identified. Finally, a baseline traffic flow
and traffic light phase model is created using camera data from the City of Hamilton.
A DDDQN optimization algorithm used to reduce individual traffic light queue
length and wait times is developed using the SUMO traffic simulator. The algorithm
is trained over different maps and is then deployed onto a large map of various streets
in the City of Hamilton. The algorithm is tested through a user-controlled driving
simulator, observing excellent performance results over long routes. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/30175 |
| Date | 08 1900 |
| Creators | Sioldea, Daniel |
| Contributors | Emadi, Ali, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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