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|Title:||Genetic algorithm based EV scheduling for on-demand public transit system||Authors:||Perera, Thilina
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2019||Source:||Perera, T., Prakash, A. & Srikanthan, T. (2019). Genetic algorithm based EV scheduling for on-demand public transit system. International Conference on Computational Science 2019 (ICCS), 11540 LNCS, 595-603. https://dx.doi.org/10.1007/978-3-030-22750-0_56||Project:||NRF TUMCREATE||Abstract:||The popularity of real-time on-demand transit as a fast evolving mobility service has paved the way to explore novel solutions for point-to-point transit requests. In addition, strict government regulations on greenhouse gas emission calls for energy efficient transit solutions. To this end, we propose an on-demand public transit system using a fleet of heterogeneous electric vehicles, which provides real-time service to passengers by linking a zone to a predetermined rapid transit node. Subsequently, we model the problem using a Genetic Algorithm, which generates routes and schedules in real-time while minimizing passenger travel time. Experiments performed using a real map show that the proposed algorithm not only generates near-optimal results but also advances the state-of-the-art at a marginal cost of computation time.||URI:||https://hdl.handle.net/10356/147722||ISBN:||9783030227494||DOI:||10.1007/978-3-030-22750-0_56||Rights:||© 2019 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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