Artificial Intelligence as a Catalyst for Modernizing Transport Networks

Urban transportation systems are facing unprecedented pressure due to rapid urbanization, infrastructure strain, and persistent mobility inequalities. As congestion, environmental degradation, and service unreliability intensify, there is a growing need for transformative approaches that go beyond incremental reforms. This study explores the role of Artificial Intelligence (AI) as a catalyst for modernizing transport networks by enhancing operational efficiency, advancing transport equity, and supporting climate resilience. Utilizing a systematic review grounded in the PRISMA framework, the research synthesizes insights from 43 peer-reviewed sources and policy reports spanning 2018–2025, with comparative analysis of three diverse urban contexts: Singapore, Shanghai, and Lagos. The study investigates AI applications in traffic optimization, public transit planning, freight logistics, and predictive infrastructure maintenance. Findings reveal measurable gains, such as a 30% reduction in congestion and up to 20% improvement in logistics efficiency. However, these outcomes vary widely depending on digital infrastructure, institutional capacity, and local data availability. Importantly, the analysis highlights underexplored dimensions of AI implementation, including gender-based mobility disparities, affordability constraints, and the risk of algorithmic bias in underserved regions. The research concludes that AI holds immense potential as a tool for enabling more inclusive, adaptive, and environmentally sustainable transport systems. Yet its successful deployment requires ethical governance, phased investment strategies, gender-responsive design, and stakeholder engagement, especially in emerging cities where resource constraints are significant. The paper offers actionable policy recommendations tailored to diverse urban settings, positioning AI not as a luxury technology but as a strategic lever for equitable urban mobility transformation.