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.

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).

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.

Household air pollution is ranked the 9^th largest Global Burden of Disease risk (Forouzanfar et al., The Lancet 2015). People, particularly urban dwellers, typically spend over 90% of their daily time indoors, where levels of air pollution often surpass those of outdoor environments. Indoor air quality (IAQ) standards and approaches for assessment and control of indoor air require measurements of pollutant concentrations and thermal comfort using conventional instruments. However, the outcomes of such measurements are usually averages over long integrated time periods, which become available after the exposure has already occurred. Moreover, conventional monitoring is generally incapable of addressing temporal and spatial heterogeneity of indoor air pollution, or providing information on peak exposures that occur when specific indoor sources are in operation. This article provides a review of new air pollution sensing methods to determine IAQ and discusses how real-time sensing could bring a paradigm shift in controlling the concentration of key air pollutants in billions of urban houses worldwide. We also show that besides the opportunities, challenges still remain in terms of maturing technologies, or data mining and their interpretation. Moreover, we discuss further research and essential development needed to close gaps between what is available today and needed tomorrow. In particular, we demonstrate that awareness of IAQ risks and availability of appropriate regulation are lagging behind the technologies.