Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147728
Title: Hybrid genetic algorithm for an on-demand first mile transit system using electric vehicles
Authors: Perera, Thilina
Prakash, Alok
Gamage, Chathura Nagoda
Srikanthan, Thambipillai
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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