SmartFlow Reinforcement Learning and Agentic AI for Bike-Sharing Optimisation

TL;DR

SmartFlow is a multi-layered AI framework combining reinforcement learning and agentic AI to optimize bike-sharing rebalancing, significantly reducing imbalance and operational costs in urban mobility networks.

Contribution

It introduces a scalable, interpretable AI architecture integrating RL and LLMs for dynamic bike rebalancing and operational communication.

Findings

Reduced network imbalance by over 95%

Minimized fleet travel distance and improved truck utilization

Enhanced interpretability and operational efficiency

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.