Reinforcement Learning for Traffic Control using social preferences

Abstract

Traffic congestion arises from all directions, particularly during peak hours, and requires the implementation of a preference mechanism—designated lanes are set up as fast lanes for prioritizing public transportation and ride sharing. Defining a rigid criterion for using the fast lanes can be ineffective if the criterion for using these lanes is unrelated to traffic volume. In situations where fast lanes become overloaded, the rigid criteria do not ensure efficient travel. A social preference criterion, similar to those utilized in priority queues found in various service sectors such as government, travel, and cultural events, could be adapted for use in managing traffic flow and lane prioritization. The social preference criteria will be based on the driver’s characteristics (e.g., a handicraft driver) or not its travel purpose (e.g., a doctor traveling for emergency surgery). To facilitate efficient travel for vehicles utilizing the fast lanes, the implementation of a reinforcement learning (RL) algorithm, specifically the Q-learning algorithm, is proposed. The results indicated that individuals exhibit social preference for various categories of vehicle passenger characteristics. The Q-learning algorithm regulated traffic flow in a junction simulation, distinguishing between fast lanes and regular lanes based on both social preference and traffic volume. This approach ensured efficient prioritization and allocation of resources.