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Optimizing the Efficiency, Vulnerability and Robustness of Road-Based Para-Transit Networks Using Genetic Algorithm Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Hybrid Genetic Algorithm for an On-Demand First Mile Transit System Using Electric Vehicles

Authors : Thilina Perera, Alok Prakash, Chathura Nagoda Gamage, Thambipillai Srikanthan

Published in: Computational Science – ICCS 2018

Publisher: Springer International Publishing

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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.

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Metadata
Title
Hybrid Genetic Algorithm for an On-Demand First Mile Transit System Using Electric Vehicles
Authors
Thilina Perera
Alok Prakash
Chathura Nagoda Gamage
Thambipillai Srikanthan
Copyright Year
2018
Book
Computational Science – ICCS 2018

Print ISBN: 978-3-319-93697-0

Electronic ISBN: 978-3-319-93698-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93698-7_8

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