Award Date
12-1-2024
Degree Type
Thesis
Degree Name
Master of Science in Engineering (MSE)
Department
Electrical and Computer Engineering
First Committee Member
Brendan Morris
Second Committee Member
Emma Regentova
Third Committee Member
Venkatesan Muthukumar
Fourth Committee Member
Shaikh Arifuzzaman
Fifth Committee Member
Shashi Nambisan
Number of Pages
92
Abstract
This thesis introduces an integrated framework for advanced traffic prediction and real-time emission estimation, designed to aid urban planning and environmental monitoring. Utilizing a graph-based transformer model, it predicts traffic conditions across the Las Vegas road network, drawing on spatial and temporal data from a large-scale sensor network. The study significantly expands the dataset from 26 to approximately 900 sensors, enhancing predictive accuracy and regional coverage. Inspired by masking techniques and strategies tailored to incomplete datasets, the model effectively handles real-world, noisy data without relying on resource-intensive imputation. Innovative training approaches enable robust traffic flow predictions despite missing or imperfect data. Additionally, traffic predictions are seamlessly integrated with the EMFAC model to provide high-resolution, real-time emission estimates for pollutants such as CO2, NOx, and PM2.5. This system delivers actionable insights into the environmental impacts of traffic, equipping policymakers and urban planners with tools to mitigate air pollution and optimize traffic management.
Keywords
Environmental Impact Assessment; Graph-Based Transformer Model; Las Vegas Urban Traffic Management; Noisy Real-World Data Handling; Real-Time Emission Estimation; Traffic Prediction
Disciplines
Adult and Continuing Education Administration | Computer Engineering | Education | Electrical and Computer Engineering
File Format
File Size
12500 KB
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Zahid, Tarek Bin, "Integrating Deep Traffic Prediction and Environmental Impact Assessment Using Noisy Real-World Data in Las Vegas" (2024). UNLV Theses, Dissertations, Professional Papers, and Capstones. 5217.
https://digitalscholarship.unlv.edu/thesesdissertations/5217
Rights
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/
Included in
Adult and Continuing Education Administration Commons, Computer Engineering Commons, Electrical and Computer Engineering Commons
