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Electronic Edition @ Ceur-ws.org (open Access).pdf

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Preview of electronic edition @ ceur-ws.org (open access)
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📊 Size: 2.29 MB
📄 Pages: 8 pages
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Summary

D2R2 uses Expectation-Maximization (EM) clustering to determine relocation zones at runtime, recalculating zones at each decision step and assigning relative probabilities based on current demand. The approach is evaluated in a Multi-Agent Reinforcement Learning (MARL) ride-sharing enabled MoD system, where 200 vehicle agents serve 10,000 ride requests in lower Manhattan. Results show that D2R2 enhances performance, improves the overall balance of request distribution across the fleet, and confirms the efficiency of the demand-responsive rebalancing strategy.

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D2R2 uses Expectation-Maximization (EM) clustering to determine relocation zones at runtime, recalculating zones at each decision step and assigning relative...

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  • File Format: PDF
  • File Size: 2.29 MB
  • Pages: 8
  • Language: EN
  • Total Downloads: 80
  • Last Updated: 1 month ago

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