The Rise of Machine Learning in Traffic Management: a Journey Through Innovation and Urban Transformation
Year: 2024
Type: article
Study design: Systematic Review
Abstract: This systematic review evaluates the implementation and effectiveness of machine learning (ML) techniques in traffic management systems through analysis of 286 peer-reviewed articles published between 2015 and 2024. Our comprehensive analysis encompasses 45 metropolitan implementations across 23 countries, focusing on real-world applications, methodological approaches, and quantifiable outcomes. The findings demonstrate that ML-based traffic management systems consistently outperform traditional methods, achieving travel time reductions ranging from 15% to 40% and operational cost savings between 20% and 35%. This review provides an in-depth analysis of current implementations, technical frameworks, challenges, and future directions in the field.
Source: AI and Data Science Journal
DOI: 10.61784/adsj3002
Authors: Daniel Chukwu, Adanna Uche
Institution: University of Nigeria
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405336477