WSEAS Transactions on Business and Economics
Print ISSN: 1109-9526, E-ISSN: 2224-2899
Volume 22, 2025
Optimising Traffic Congestion in Lagos using Machine Learning:
A Case Study on Impacts on Students, Health, Education, and Businesses
Authors: ,
Abstract: The expansion of urban transport systems and the increase in population have led to severe traffic congestion, causing significant inconvenience to people. In Lagos, Nigeria, traffic congestion has long been a persistent problem, affecting students' academic performance, public health, educational outcomes, and business productivity. This study examines the potential of machine learning (ML) techniques to mitigate the adverse effects of traffic congestion in Lagos, with implications for students, health, education, and businesses. The research employs a mixed-methods approach, combining quantitative analysis with qualitative insights from stakeholders. The findings indicate that machine learning models, such as KNN, ARIMA, and GARCH, particularly those utilizing real-time data and predictive analytics, can be used to prevent major traffic jams and reduce congestion impacts both in the short term and long term by altering people's transportation habits. The paper concludes with policy recommendations and suggestions for future research directions.
Search Articles
Pages: 2677-2684
DOI: 10.37394/23207.2025.22.210