Forskning · Jun 2026
AI and Machine Learning for Sustainable Urban Mobility: A Systematic Review
Systematic review of 14 Q1 IEEE journals on AI and RL methods for sustainable smart urban transport.
This paper is a systematic literature review looking at how artificial intelligence and machine learning are being used to make urban transport smarter and more sustainable. Following the PRISMA 2020 guidelines, the authors searched IEEE Xplore and narrowed down an initial pool of 469 papers to 14 high quality Q1 journal studies published between 2016 and 2026.
The review found that reinforcement learning, and deep Q-network approaches specifically, dominate the field of traffic signal control, consistently beating out traditional fixed-time systems and cutting vehicle waiting times in several reported cases. The paper also traces how the research has evolved over time, moving from single intersection control toward multi-agent coordination and federated learning setups designed to scale across entire cities. Other methods covered include PPO, LSTM based traffic prediction, and hybrid models combining spatial and temporal learning, applied not just to traffic lights but also to things like road anomaly detection and route optimization for buses and general navigation.
A key takeaway is that while these AI driven methods show strong results in simulation, almost all of the reviewed studies rely on simulated environments like SUMO rather than real world deployment, leaving open questions about how well these gains would hold up on actual city streets. The paper closes by pointing to lightweight model design and edge computing as promising directions for closing that gap between research and real world use.
- Machine Learning
- Reinforcement Learning
- Deep Learning