Traffic congestion detection and optimizing traffic flow using object detection, optical flow and fluid dynamics
Abstract
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.
Keywords
AITS; ITSC; Deep learning; Image recognition; Self-adaptive traffic system; ATS; City Traffic; Fluid dynamics; Numerical simulation; Traffic simulation; Object detection; Optical flow; Traffic flowLC Subject Headings
Image processing -- Digital techniques.; Machine learning; Cognitive learning theory (Deep learning); Fluid dynamics -- Computer programs.Description
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.Department
Department of Computer Science and Engineering, Brac UniversityType
ThesisCollections
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