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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | https://arxiv.org/abs/2601.00868 |
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| _version_ | 1866917180870754304 |
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| author | K, Aditya Sreevatsa Raveendran, Arun Kumar Mani, Jesrael K Shigli, Prakash G Rangadore, Rajkumar Darapaneni, Narayana Paduri, Anwesh Reddy |
| author_facet | K, Aditya Sreevatsa Raveendran, Arun Kumar Mani, Jesrael K Shigli, Prakash G Rangadore, Rajkumar Darapaneni, Narayana Paduri, Anwesh Reddy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00868 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SmartFlow Reinforcement Learning and Agentic AI for Bike-Sharing Optimisation K, Aditya Sreevatsa Raveendran, Arun Kumar Mani, Jesrael K Shigli, Prakash G Rangadore, Rajkumar Darapaneni, Narayana Paduri, Anwesh Reddy Machine Learning Artificial Intelligence 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. |
| title | SmartFlow Reinforcement Learning and Agentic AI for Bike-Sharing Optimisation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.00868 |