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Table 2 Summary of methods, authors and their focus for MaaS demand modelling
From: MaaS modelling: a review of factors, customers’ profiles, choices and business models
Method | Authors, year | Focus |
|---|---|---|
Surveys | ||
Regression analysis | Fioreze et al. 2019 [83] | Attitude among residents towards the introduction of MaaS |
Liljamo et al. 2020 [51] | Estimating the current mobility costs of the respondents and relating their willingness to pay (WTP) for MaaS to their mobility costs | |
Heteroscedastic non-linear random parameter Multinomial logit | Ho et al. 2018 [61] | Understanding what types of MaaS subscription plans might appeal to potential users |
Ho et al. 2020 [78] | Different business bundle models and their appeals | |
Error logit component | Feneri et al. 2020 [82] | Understanding the model shift as a result of the availability of MaaS |
Krauss et al. 2023 [79] | Transport supply and mobility behaviour on preferences for MaaS bundles in multiple cities | |
Multinomial logit | Tsouros et al. 2021 [14] | Exploring demand and WTP for MaaS |
Narayanan et al. 2023 [90] | The development of a joint mode choice model for bike-sharing, car-sharing and ride-hailing services | |
Mulley et al. 2020 [89] | The WTP for bundles of mobility services | |
Mixed logit | Caiati et al. 2020 [77] | Formulating and estimating a discrete choice model for MaaS adoption decision |
Kim et al. 2023 [91] | Understanding relationships of the tourist preference for tourism travel alternatives represented as MaaS | |
Matyas and Kamargianni, 2018 [6] | Understanding potential modes and features to be included in the MaaS plan and the WTP for these features | |
Guidon et al. 2020 [80] | Analysing the difference between bundle and sum-of-parts WTP to determine bundling valuation | |
Matyas and Kamargianni, 2019 [48] | Identifying individuals’ preferences for the modes in the plans | |
Caiati et al. 2020 [31] | Explore potential MaaS adoption considering age groups and life stages of potential users | |
Latent class | Alonso et al. 2020 [81] | Identifying factors relevant for MaaS adoption |
van’t Veer et al. 2023 [92] | Providing insights into which factors influence the intention to use MaaS among private vehicle owners | |
Kim et al. 2022 [68] | Understanding how people’s lifestyle associated to WTP | |
Hybrid choice model parts | Polydoropoulou et al. 2020 [85] | Individualising preferences for MaaS |
Matyas and Kamargianni, 2021 [52] | Examining individual preferences for MaaS packages | |
Kim et al. 2021 [84] | Identifying users’ preference for intermodal options under MaaS adoption | |
Schikofsky et al. 2020 [86] | Understanding motivational mechanisms behind the intention to adopt MaaS | |
Lopez-Carreiro et al. 2021 [88] | Identifying a set of attitudinal and personality factors relevant for MaaS adoption | |
Vij et al. 2020 [49] | Understanding consumer demand and willingness to pay for MaaS | |
Pilots | ||
Statistic analysis | Storme et al. 2019 [96] | Exploring car usage reduction in return for a monthly mobility budget, which they could spend on MaaS services |
Musolino et al. 2023 [98] | Capturing the main behaviour variables of MaaS transport users | |
“before”, “during”, “after” questionnaires | Sochor et al. 2016 [74] | Insights from a six-month field operational test |
Strömberg et al. 2018 [97] | Analysing who is the potential MaaS customer | |
Karllson et al. 2016 [94] | Insights from the trial and evaluation of an example of MaaS | |
The binary choice model | Hensher et al. 2021 [57] | Investigating the potential for changes in monthly car use in the presence of a MaaS program |
Mixed logit with correlated random parameters | Ho et al. 2021 [58] | Assessing the interest in MaaS subscription bundles compared to PAYG |