Abstract
With the increase in population and in particular urban population. The traffic and travel times in between cities and inside cities has increased due to more and more people using private means of transportation. Due to this need arose for tackling the increase in traffic by managing it using various means. For this we look towards The Intelligent Traffic Management System (ITMS). ITMS is an AI-powered solution designed to optimize traffic flow, reduce congestion, and improve overall road safety. The system will monitor real-time traffic data using a combination of cameras and sensors, identify traffic jams, and send alerts to traffic authorities and police officers. This paper reviews the key components of ITMS. Which includes traffic flow prediction, image classification and incident or accident detection through cameras, sensors, and inductive loop detectors. Comparison and evaluation metrics of different algorithms and models are presented. Tables show the accuracy rates and best features of various techniques. After that future work is presented in which a framework is presented to write and combine the best practices of each component together to create a useful ITMS. Which can be used in future smart cities, big urban centers and could also be used as a guide. Keywords: Intelligent Traffic management systems, inductive loop detectors, optimize traffic flow, reduce congestion.
Advisor
Subah Alkushayni
Committee Member
Naseef Mansoor
Date of Degree
2024
Language
english
Document Type
Thesis
Degree
Master of Science (MS)
Program of Study
Data Science
Department
Mathematics and Statistics
College
Science, Engineering and Technology
Recommended Citation
Mazhar, Mohammad. (2024). Intelligent Traffic Management Systems [Master’s thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/1454/
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