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Optimization of On-Demand Shared Autonomous Vehicle Deployments Utilizing Reinforcement Learning.

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  • 1Automated Driving Lab, Ohio State University, Columbus, OH 43210, USA.

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Summary

Shared autonomous vehicles (SAVs) can improve mobility for underserved communities. Reinforcement learning (RL) optimization significantly enhances SAV service by reducing passenger wait times and increasing served trips.

Keywords:
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Area of Science:

  • Transportation science
  • Artificial intelligence
  • Urban mobility

Background:

  • Shared autonomous vehicles (SAVs) offer a cost-effective approach to autonomous driving technology adoption and serve underserved communities.
  • Existing SAV operational optimization research often uses simplified networks, lacking realistic traffic representation, which limits practical deployment planning.

Purpose of the Study:

  • To optimize SAV fleet deployment and operations using a realistic urban environment.
  • To develop and evaluate a reinforcement learning (RL) based dispatcher for SAVs.

Main Methods:

  • Utilized a real-world autonomous shuttle deployment site in Columbus, Ohio, for simulation.
  • Developed a reinforcement learning (RL) agent to act as an SAV dispatcher.
  • Simulated the RL dispatcher in a realistic traffic scenario to assess performance.

Main Results:

  • The RL-aided dispatcher significantly improved SAV operational efficiency.
  • Demonstrated a substantial increase in completed trips and passengers served.
  • Achieved a notable decrease in passenger wait times.

Conclusions:

  • Reinforcement learning is a powerful tool for optimizing SAV fleet management.
  • RL-based dispatching enhances the viability and effectiveness of SAV services in urban settings.
  • This approach provides a scalable solution for planning and managing SAV deployments.