Improving Traffic Flow and Reducing Congestion Using Predictive Analytic
Level of Education of Students Involved
Graduate
Faculty Sponsor
Hugh Gong
College
College of Arts & Sciences (CAS)
Discipline(s)
Predictive Analytics and Machine Learning
Presentation Type
Poster Presentation
Symposium Date
Spring 4-24-2025
Abstract
Traffic congestion is a significant issue in urban areas, leading to increased travel times, fuel consumption, and environmental pollution. Traditional traffic management methods, such as fixed traffic signal timings and manual interventions, often fail to adapt dynamically to changing traffic conditions. This study explores the application of predictive analytics and machine learning techniques to optimize traffic flow and reduce congestion. By leveraging historical traffic data, real-time traffic monitoring systems, and external factors such as weather conditions and road incidents, predictive models can be developed to forecast congestion levels and suggest optimal traffic management strategies.
The methodology involves data collection from multiple sources, processioning for quality enhancement, and the application of machine learning algorithms, including decision trees, random forests, and neural networks, to predict traffic patterns. These models will help in dynamic traffic signal control, congestion mitigation, and route optimization. The anticipated outcome is a smart traffic management system that enhances urban mobility, reduces delays, and contributes to sustainable transportation infrastructure. This research can potentially aid city planners, transportation agencies, and policymakers implement data-driven solutions for improving urban traffic efficiency.
Recommended Citation
Revu, Yashwanth, "Improving Traffic Flow and Reducing Congestion Using Predictive Analytic" (2025). Symposium on Undergraduate Research and Creative Expression (SOURCE). 1450.
https://scholar.valpo.edu/cus/1450
Biographical Information about Author(s)
I am an enthusiastic Software Engineer with expertise in database development, data analytics, and business intelligence. I have experience working with leading organizations like AstraZeneca, ASEA Global, and Trans Media Group, specializing in SQL, Python, Power BI, and Tableau. Passionate about Data Science, I excel in designing and optimizing databases, solving performance issues, and developing enterprise applications. With strong analytical skills and adaptability, I am dedicated to delivering data-driven solutions and driving business efficiency.