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User preferences in ride-sharing mathematical models for enhanced matching.

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Integrating user preferences into ride-sharing with passenger transfer significantly boosts system efficiency and user satisfaction. This approach enhances matching, reduces response times, and increases overall demand and revenue.

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Area of Science:

  • Operations Research
  • Transportation Science
  • Computer Science

Background:

  • Ride-sharing services face challenges like overcrowding, resource limitations, and environmental impact.
  • Passenger transfer in ride-sharing can overcome matching limitations, especially in areas with lower population density.
  • Incorporating user preferences is vital for improving ride-sharing system efficiency and user satisfaction.

Purpose of the Study:

  • To develop a mathematical programming model that integrates user preferences into ride-sharing with passenger transfer.
  • To introduce algorithms for preference-driven matching and efficient solution generation.
  • To evaluate the performance of the proposed model in real-scale scenarios.

Main Methods:

  • Development of a mathematical programming model incorporating user preferences.
  • Proposal of a Preference-Driven Matching Algorithm to capture user preferences and identify potential matches.
  • Introduction of an Iterative Enhance-and-Optimize Algorithm for rapid, high-quality solution generation.

Main Results:

  • The proposed model with integrated user preferences demonstrated superior performance compared to other methods.
  • The algorithms effectively captured user preferences and generated optimal matches.
  • Evaluations on real-scale scenarios confirmed the efficiency and effectiveness of the proposed approaches.

Conclusions:

  • User preferences are crucial for optimizing ride-sharing systems, balancing efficiency and satisfaction.
  • The developed model enhances user satisfaction, system responsiveness, and operational efficiency.
  • The approach leads to increased demand and revenue by servicing a larger user base.