Network-Level Control of Intermittent Bus Lanes Using Multi-Agent Deep Reinforcement Learning With Multi-Feature State Prediction

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The current method of setting up bus lanes is based on historical or current road traffic conditions for control, but these methods lack a mechanism for predicting traffic conditions. This paper constructs an LSTM multi-feature state prediction model to acquire observed states in the environment, utilizes neural networks to predict future traffic conditions, and considers variables affecting actual traffic conditions. Both are used as joint states to train each agent, helping them better understand the environment and take optimal actions. Additionally, to effectively address the differences in characteristics between social vehicles and buses, a multi-type intelligent agent for signal lights and bus lanes is constructed for training. The DPQ (Deep Prediction Q-Network) deep reinforcement learning algorithm is proposed to optimize signal lights and bus lane settings. Furthermore, using mean field theory, the MFPQ (Mean Field Prediction Q-Network) algorithm models the problem as interactions between a single intelligent agent and neighboring intelligent agents’ average effects, reducing the complexity of interaction between agents. Experimental results show that both the DPQ algorithm and the MFPQ algorithm improve evaluation metrics such as travel time. The LSTM multi-feature state prediction model promotes optimal decision-making by predicting future states for intelligent agents and significantly accelerates the convergence speed of the algorithm, enhancing learning performance.