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|Title:||Hybrid genetic algorithm for an on-demand first mile transit system using electric vehicles||Authors:||Perera, Thilina
Gamage, Chathura Nagoda
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2018||Source:||Perera, T., Prakash, A., Gamage, C. N. & Srikanthan, T. (2018). Hybrid genetic algorithm for an on-demand first mile transit system using electric vehicles. ICCS 2018, 10860 LNCS, 98-113. https://dx.doi.org/10.1007/978-3-319-93698-7_8||Project:||NRF TUMCREATE||Abstract:||First/Last mile gaps are a significant hurdle in large scale adoption of public transit systems. Recently, demand responsive transit systems have emerged as a preferable solution to first/last mile problem. However, existing work requires significant computation time or advance bookings. Hence, we propose a public transit system linking the neighborhoods to a rapid transit node using a fleet of demand responsive electric vehicles, which reacts to passenger demand in real-time. Initially, the system is modeled using an optimal mathematical formulation. Owing to the complexity of the model, we then propose a hybrid genetic algorithm that computes results in real-time with an average accuracy of 98%. Further, results show that the proposed system saves travel time up to 19% compared to the existing transit services.||URI:||https://hdl.handle.net/10356/147728||ISBN:||9783319936970||DOI:||10.1007/978-3-319-93698-7_8||Rights:||© 2018 Springer International Publishing AG, part of Springer Nature. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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