SmartFlow Reinforcement Learning and Agentic AI for Bike-Sharing Optimisation

Aditya Sreevatsa K; Arun Kumar Raveendran; Jesrael K Mani; Prakash G Shigli; Rajkumar Rangadore; Narayana Darapaneni; Anwesh Reddy Paduri

arXiv · 2601.00868

SmartFlow Reinforcement Learning and Agentic AI for Bike-Sharing Optimisation

Aditya Sreevatsa K·Arun Kumar Raveendran·Jesrael K Mani·Prakash G Shigli·Rajkumar Rangadore·Narayana Darapaneni·Anwesh Reddy Paduri

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Abstract

SmartFlow is a multi-layered framework that integrates Reinforcement Learning and Agentic AI to address the dynamic rebalancing problem in urban bike-sharing services. Its architecture separates strategic, tactical, and communication functions for clarity and scalability. At the strategic level, a Deep Q-Network (DQN) agent, trained in a high-fidelity simulation of New Yorks Citi Bike network, learns robust rebalancing policies by modelling the challenge as a Markov Decision Process. These high-level strategies feed into a deterministic tactical module that optimises multi-leg journeys and schedules just-in-time dispatches to minimise fleet travel. Evaluation across multiple seeded runs demonstrates SmartFlows high efficacy, reducing network imbalance by over 95% while requiring minimal travel distance and achieving strong truck utilisation. A communication layer, powered by a grounded Agentic AI with a Large Language Model (LLM), translates logistical plans into clear, actionable instructions for operational staff, ensuring interpretability and execution readiness. This integration bridges machine intelligence with human operations, offering a scalable solution that reduces idle time, improves bike availability, and lowers operational costs. SmartFlow provides a blueprint for interpretable, AI-driven logistics in complex urban mobility networks.

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Aditya Sreevatsa K, Arun Kumar Raveendran, Jesrael K Mani, Prakash G Shigli, Rajkumar Rangadore, Narayana Darapaneni, Anwesh Reddy Paduri. 2025-12-30. SmartFlow Reinforcement Learning and Agentic AI for Bike-Sharing Optimisation. https://arxiv.org/abs/2601.00868

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