A Graph Neural Network and Reinforcement Learning-Based Framework for Transparent Vehicle Rental Pricing

The rapid expansion of vehicle rental platforms and mobile applications has significantly increased demand for on-demand vehicle rental services. However, traditional vehicle rental systems face several operational challenges such as inefficient fleet distribution, uncertain vehicle availability, and non-transparent dynamic pricing mechanisms. This paper proposes an intelligent vehicle rental management framework that integrates Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL) to improve fleet allocation and pricing transparency. The GNN captures spatial relationships between rental locations and predicts vehicle availability across different zones. A Deep Q-Network (DQN) based reinforcement learning agent dynamically adjusts vehicle rental prices based on demand, supply, and temporal factors. Explainable AI techniques are incorporated to provide transparent reasoning behind pricing decisions. Experimental evaluation demonstrates that the proposed system improves fleet utilization and provides more stable and interpretable pricing predictions compared with traditional machine learning models. However, these models typically fail to capture spatial relationships between rental locations, which are critical for transportation networks.