Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control

• URL: http://arxiv.org/abs/2302.03963v2
• Date: Wed, 21 Feb 2024 09:33:36 GMT
• Title: Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
• Authors: Kai Jungel, Axel Parmentier, Maximilian Schiffer, Thibaut Vidal
• Abstract summary: We study online control algorithms for autonomous mobility-on-demand systems. We develop a novel hybrid enriched machine learning pipeline which learns online dispatching and rebalancing policies. We show that our approach outperforms state-of-the-art greedy, and model-predictive control approaches.
• Score: 8.020856741504794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution. However, the success of these systems heavily depends on efficient and effective fleet control strategies. In this context, we study online control algorithms for autonomous mobility-on-demand systems and develop a novel hybrid combinatorial optimization enriched machine learning pipeline which learns online dispatching and rebalancing policies from optimal full-information solutions. We test our hybrid pipeline on large-scale real-world scenarios with different vehicle fleet sizes and various request densities. We show that our approach outperforms state-of-the-art greedy, and model-predictive control approaches with respect to various KPIs, e.g., by up to 17.1% and on average by 6.3% in terms of realized profit.

Related papers

• Routing and Scheduling Optimization for Urban Air Mobility Fleet Management using Quantum Annealing [1.2145532233226684]
  Efficiently managing the anticipated high-density air traffic in cities is critical to ensure safe and effective operations. We propose a routing and scheduling framework to address the needs of a large fleet of UAM vehicles operating in urban areas. Our method is validated using a traffic management simulator tailored for the airspace in Singapore.
  arXiv  Detail & Related papers  (2024-10-15T03:27:52Z)
• Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots [50.02055068660255]
  Navigating urban environments poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain.
  arXiv  Detail & Related papers  (2024-05-03T00:29:20Z)
• Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning [60.17407932691429]
  Open Radio Access Network systems, with their base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability. We propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments. We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments.
  arXiv  Detail & Related papers  (2023-09-04T17:30:21Z)
• Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning [3.9121134770873733]
  We propose to formalize the control of Autonomous Mobility-on-Demand systems through the lens of offline reinforcement learning. We show that offline RL is a promising paradigm for the application of RL-based solutions within economically-critical systems.
  arXiv  Detail & Related papers  (2023-02-28T18:31:07Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing [0.26249027950824505]
  We propose a DRL-based signal control system that adjusts traffic signals according to the current congestion situation on intersections. To deal with the congestion on roads behind the intersection, we used re-routing technique to load balance the vehicles on road networks.
  arXiv  Detail & Related papers  (2022-06-28T02:46:20Z)
• AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles [61.21359293642559]
  The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies. We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
  arXiv  Detail & Related papers  (2022-03-05T10:54:05Z)
• Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL [63.52264764099532]
  We study the ability of autonomous vehicles to improve the throughput of a bottleneck using a fully decentralized control scheme in a mixed autonomy setting. We apply multi-agent reinforcement algorithms to this problem and demonstrate that significant improvements in bottleneck throughput, from 20% at a 5% penetration rate to 33% at a 40% penetration rate, can be achieved.
  arXiv  Detail & Related papers  (2020-10-30T22:06:05Z)
• Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon [14.059728921828938]
  This paper utilizes the deep reinforcement learning (DRL) method to address the continuous-horizon decision-making problem on the highway. The running objective of the ego automated vehicle is to execute an efficient and smooth policy without collision. The PPO-DRL-based decision-making strategy is estimated from multiple perspectives, including the optimality, learning efficiency, and adaptability.
  arXiv  Detail & Related papers  (2020-08-26T22:49:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.