Artificial intelligence in urban transportation systems: A systematic survey and taxonomy

Rapid urbanization and increasing vehicle ownership are intensifying traffic congestion, road safety risks, and environmental pressures in cities worldwide. Conventional rule-based traffic management systems are increasingly inadequate for handling the dynamic and nonlinear nature of modern transportation networks, motivating the adoption of artificial intelligence for congestion prediction, accident risk analysis, adaptive control, and sustainable mobility planning. This study presents a systematic survey, with a regional emphasis, of applications of artificial intelligence in transportation, focusing on traffic congestion, road safety, Intelligent Transportation Systems, and sustainable urban mobility. Following a systematic methodology aligned with PRISMA, 72 peer-reviewed studies published between 2020 and 2026 were analyzed, and a four-dimensional deployment-aware taxonomy is proposed across artificial intelligence paradigms, application domains, data sources, deployment contexts, and evaluation practices. The analysis shows that deep spatio-temporal models frequently report superior predictive accuracy on benchmark datasets, while ensemble and hybrid models offer improved robustness. Key challenges include region-specific infrastructure limitations, limited explainability, and weak integration with operational traffic management. Amman, Jordan, is examined as an emerging city case, highlighting deployment barriers in data-limited environments. The survey provides a comparative evidence base and a practical roadmap for minimizing artificial intelligence-enabled congestion, improving road safety, and sustainable urban mobility.