The field of urban mobility is moving towards a more sustainable and efficient future. Researchers are focusing on developing innovative solutions to reduce traffic congestion, minimize environmental impact, and optimize public transportation systems. One of the key areas of interest is the use of feedback control and reinforcement learning to manage and regulate urban traffic flow. This includes optimizing parking management, electric bus charging schedules, and Low Emission Zones to reduce pollution and congestion. Noteworthy papers in this area include:

• A Feedback Control Framework for Incentivised Suburban Parking Utilisation and Urban Core Traffic Relief, which proposes a novel feedback control system to better distribute vehicles between city and suburban parking facilities.
• Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning, which presents a Hierarchical DRL approach to optimize electric bus charging schedules.
• Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning, which introduces a novel HDRL algorithm to address uncertainties in travel time, energy consumption, and fluctuating electricity prices.