Traffic congestion detection and optimizing traffic flow using object detection, optical flow and fluid dynamics
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Bangladesh has been suffering a severe traffic congestion issue ever since it has been on a high paced development roadmap. Researches regarding solving such traffic issue has been in the talks but has never reached a proper conclusion and far from implementation. It has slowly grown into a towering challenge to overcome. And with an aim to topple that tower, we propose a 3 layer architecture to solve this problem. The proposed model consists of object detection, speed measurement and decision based on traffic flow. Using neural network object detection algorithms, it will detect congestion and the speed of the congestion. Then, it will use fluid dynamics based model to get the traffic flow, pass data between other signals and provide correct traffic signals. All signals would interact with each other like hive mind to maximize the traffic flow in any intersection. With the working model we had at our hand, we ran rigorous experiments to check whether our model works or not. Our results indicate that our model surpasses all other similarly implemented models by a noticeably large margin.
KeywordsAITS; ITSC; Deep learning; Image recognition; Self-adaptive traffic system; ATS; City Traffic; Fluid dynamics; Numerical simulation; Traffic simulation; Object detection; Optical flow; Traffic flow
LC Subject HeadingsImage processing -- Digital techniques.; Machine learning; Cognitive learning theory (Deep learning); Fluid dynamics -- Computer programs.
DescriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
DepartmentDepartment of Computer Science and Engineering, Brac University
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