Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction. Moreover, people may adapt their behaviour differently depending on their current travel mode. This study is based on stated preference data collected in Amsterdam. We analysed the data using mixed logit models. We found that users of different modes not only have varied general preferences for different shared modes but also have different sensitivity for attributes such as travel time and cost. Public transport users are more likely to switch to eHUBS modes than car users. People who bike and walk have strong inertia, but the percentage choosing eHUBS modes doubles when the trip distance is longer (5 or 10 km).

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eHUBS are physical locations that integrate two or more electric shared mobility modes. As they provide transport users easier access to a wide range of transport modes, multimodal behaviour is expected to be more common. However, this issue has not been addressed in previous stated preference studies on mode choices involving innovative transport modes. In this study, multimodal behaviour is explicitly addressed both in measurement and in modelling by adopting the multiple discrete–continuous (MDC) modelling framework in contrast to discrete choice models. Instead of asking transport users to indicate the most preferred alternative, they were allowed to choose more than one alternative by allocating trips between several modes. This study aims to answer two questions: 1) whether there is complementarity between the multiple shared modes offered in eHUBS and 2) how would transport users adapt when one of the shared modes that they plan to use becomes unavailable. Using stated mode choice data of non-commuting trips from transport users whose current mode is driving a private car in Manchester, UK, several models under the MDC framework were estimated including Multiple Discrete-Continuous Extreme Value (MDCEV) model, mixed MDCEV model, and the extended Multiple Discrete Continuous (eMDC) model. The results show that there is complementarity between shared electric vehicle (EV) and electric bike (e-bike) offered in the eHUBS. In addition, the research show that the flexibility between those two shared modes is stronger than assumed in the MDCEV model, and common preference heterogeneity cannot fully account for this phenomenon.@en

Electric mobility hubs (eHUBS) represent an innovative approach to providing diverse shared electric transportation options, aimed at curbing private car use, and mitigating associated environmental impacts. Assessing the impact of eHUBS on travel choices across different cities requires significant resource and time investment due to the need for localized data collection and model development. This paper proposes a potential solution to this challenge by investigating the transferability of mode choice models originally developed for eHUBS in Amsterdam to predict behaviour towards eHUBS in Manchester. Multinomial Logit (MNL) and mixed logit models were transferred using four different procedures, and their effectiveness was evaluated using three assessment measures. The findings indicate that a scaled mixed logit model with an updated Alternative Specific Constant (ASC) outperforms other models in terms of its transfer effectiveness, both for disaggregate and aggregate assessment measures. The interplay between transfer procedures and assessment measures also was examined, with results indicating enhancements in disaggregate transferability measures with the 'scaling' transfer procedure, while 'updating the Alternative Specific Constants (ASCs)' improved predictions of aggregate mode shares. Following the analysis, the paper presents an in-depth discussion to provide a nuanced understanding of transferability and thus offers valuable insights for researchers planning future studies and practical considerations for policymakers.

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Shared electric mobility hubs, or eHUBs, offer users access to a range of shared electric vehicles on demand. However, little is currently known about what the characteristics of potential users of this novel type of shared mobility are. This makes it difficult to plan the location of hubs and to provide facilities, which ultimately will determine their success. This paper therefore seeks to identify potential users based on an in-depth case study of a representative sample of the Municipality of Amsterdam population. The analysis employed an attitudinal market segmentation approach supported by the Theory of Diffusion of Innovations (DOI). The analysis identified four specific target groups, each with a different propensity to use eHUBs in the future. In our sample, two groups expressed an interest in using eHUBs. The first group consists of highly educated and non-car owning young adults (19% of the sample), whereas the second group shows a higher level of car ownership and a greater number of households with children (69% of the sample). The two remaining groups comprise the majority of laggards (52%), despite only representing 12% of the sample. They tend to be older, less educated, and live in a household without children. The four groups are further distinguished based on their current shared mobility use, traveler identity, and perceived barriers to using shared electric vehicles. Finally, general recommendations to practitioners and policymakers to increase the uptake of shared mobility, including paying attention to the availability, cost, and convenience of shared mobility options, are provided.

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