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

  • Thilina Perera
  • Alok Prakash
  • Chathura Nagoda Gamage
  • Thambipillai Srikanthan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10860)

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.

Keywords

Demand responsive transit Genetic algorithm First/last mile problem Electric vehicles 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Thilina Perera
    • 1
  • Alok Prakash
    • 1
  • Chathura Nagoda Gamage
    • 1
  • Thambipillai Srikanthan
    • 1
  1. 1.Nanyang Technological UniversitySingaporeSingapore

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