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Predicting Mobility as a Service (MaaS) use for different trip categories: An artificial neural network analysis

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  • Duan, Sophia Xiaoxia
  • Tay, Richard
  • Molla, Alemayehu
  • Deng, Hepu

Abstract

Mobility as a Service (MaaS) has gained popularity as a means of sustainable urban transport which makes the understanding of MaaS use critical for its design and promotion. Studies have contributed to the growing understanding of MaaS, its design features, and consumers’ willingness to pay for MaaS in general, without recourse to the differences in traveling for work, social and general purposes. This paper develops and tests models to predict MaaS use by applying artificial neural network analysis, following the cross industry standard process and data mining framework. It also estimates separate models for social, general, and work trips, using 33 input variables reflecting network externality, transaction cost, behaviour, institutional, environmental concern, personal travel, and socio-economic factors. Data are collected from a survey of 331 Australians. The study reveals different sets of socio-economic factors, the impact of Covid-19, and personal travel factors as key predictors of MaaS use for general, social, and work trips with an average prediction accuracy of 68%, 68%, and 75% respectively. The findings can be used to inform strategies and policies on how to attract a user base with respect to socio-economic and personal travel factors for promoting MaaS use.

Suggested Citation

  • Duan, Sophia Xiaoxia & Tay, Richard & Molla, Alemayehu & Deng, Hepu, 2022. "Predicting Mobility as a Service (MaaS) use for different trip categories: An artificial neural network analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 135-149.
    • Handle: RePEc:eee:transa:v:166:y:2022:i:c:p:135-149
    DOI: 10.1016/j.tra.2022.10.014
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