Resumen

MAASKRI, Moustafa  y  HAMOU REDA, Mohamed. Ride Sharing Using Dynamic Rebalancing with PSO Clustring: A Case Study of NYC. Comp. y Sist. [online]. 2022, vol.26, n.2, pp.963-975.  Epub 10-Mar-2023. ISSN 2007-9737.  https://doi.org/10.13053/cys-26-2-3942.

The shared vehicle can improve the efficiency of urban mobility by reducing car ownership and parking demand. Existing rebalancing research divides the system coverage area into defined geographical zones, but this is achieved statically at system design time, limiting the system’s adaptability to evolve. In the current study, a method has been proposed for rebalancing unoccupied vehicles in real-time while considering travel requests, using a bio-inspired method known as Particle Swarm Optimization clustering (PSO-Clustering). The solution was examined using data on taxi usage in New York City, first looking at the traditional system (no ride sharing, no rebalancing), then carpooling, and finally of both ride sharing and rebalancing.

Palabras llave : Ride sharing; PSO; rebalancer; clustering; simulation.

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