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Path optimization of taxi carpooling.

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Optimizing taxi carpooling paths using genetic algorithms reduces empty driving and passenger wait times. This approach benefits both drivers with increased income and passengers with lower travel costs.

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Area of Science:

  • Operations Research
  • Transportation Science
  • Computer Science

Background:

  • Traditional taxi services face challenges with high empty driving rates and passenger difficulties in finding rides.
  • Taxi carpooling offers a potential solution to mitigate these operational inefficiencies.

Purpose of the Study:

  • To develop and evaluate optimized pathfinding models for taxi carpooling.
  • To enhance efficiency and profitability within the ride-sharing ecosystem.

Main Methods:

  • Development of single-objective and multi-objective taxi carpooling path optimization models.
  • Application of improved single-objective and multi-objective genetic algorithms for solving the models.
  • Validation through a case study on a 24-node road network.

Main Results:

  • The proposed models and algorithms efficiently determine optimal taxi carpooling routes.
  • Demonstrated ability to increase taxi driver income.
  • Proven effectiveness in reducing passenger transportation costs.

Conclusions:

  • The implemented genetic algorithms provide a viable solution for taxi carpooling path optimization.
  • The study confirms the dual benefits of increased driver earnings and decreased passenger expenses.
  • This research contributes to more efficient and economical urban mobility solutions.