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    Carpooling reduces traffic and pollution by matching drivers and passengers. A new multiobjective optimization algorithm improves carpool matching, outperforming previous methods for efficient ride-sharing.

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    Area of Science:

    • Operations Research
    • Transportation Science
    • Computer Science

    Background:

    • Vehicle sharing offers environmental and congestion benefits.
    • Carpooling requires balancing increased ridership, detours, and simultaneous matches.
    • Existing methods combine objectives, risking suboptimal performance for individual goals.

    Purpose of the Study:

    • Define the multiobjective optimization carpool service problem (MOCSP).
    • Develop an advanced algorithm for effective carpool matching.
    • Improve upon existing evolutionary algorithms for carpool optimization.

    Main Methods:

    • Proposed the multiobjective optimization carpool service problem (MOCSP).
    • Developed a novel set-based simulated binary and multiobjective carpool matching algorithm.
    • Enhanced individual representation and genetic operations for improved optimization.

    Main Results:

    • The proposed algorithm effectively solves the MOCSP.
    • Achieved superior driver-passenger matching results compared to prior algorithms.
    • Demonstrated improved performance in balancing multiple carpooling objectives.

    Conclusions:

    • The new algorithm offers a more effective approach to carpool matching.
    • Addresses limitations of single-objective optimization in ride-sharing.
    • Provides a foundation for more efficient and sustainable urban transportation solutions.