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A fuzzy logic-genetic algorithm approach to modelling public transport users’ risk-taking behaviour

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  • Subeh Chowdhury
  • Michael O’Sullivan

Abstract

This paper seeks to determine the effects of uncertainty in out-of-vehicle times on route choice. Data were collected at two key interchanges in Auckland, New Zealand. Previous work modelled the data using a manual approach to fuzzy logic. This study extends that work by automating the process through defining a black-box function to match the survey data, then employing a genetic algorithm to fine-tune the fuzzy logic model. Results show that automation and the genetic algorithm improve the model’s capability to more accurately predict ridership. The tuning of the membership functions is conducted twice, first using initial fuzzy rules and again after the fuzzy rules have been adjusted to reduce disparity between the output and survey data. The calibrated membership functions provided for operational (transfer waiting and walking time and delay) and physical attributes (safety and seat availability) can be used by practitioners to determine an estimated ridership.

Suggested Citation

  • Subeh Chowdhury & Michael O’Sullivan, 2018. "A fuzzy logic-genetic algorithm approach to modelling public transport users’ risk-taking behaviour," Transportation Planning and Technology, Taylor & Francis Journals, vol. 41(2), pages 170-185, February.
    • Handle: RePEc:taf:transp:v:41:y:2018:i:2:p:170-185
    DOI: 10.1080/03081060.2018.1407520
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    Cited by:

    1. Chomphunut Sutheerakul & Nopadon Kronprasert & Wichuda Satiennam & Moe Sandi Zaw, 2024. "Classification of Roadway Context and Target Speed for Multilane Highways in Thailand Using Fuzzy Expert System," Sustainability, MDPI, vol. 16(9), pages 1-23, May.

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