Shifting from a fossil-fueled to an eco-friendly vehicle fleet in cities could pave the way towards a more sustainable future. Electric Vehicles (EVs) should thus be prioritized, so that they could replace conventional vehicles gradually. In this context, an EV-accommodating infrastructure, which ensures the functionality of the entire system, is essential. This study aims to develop a methodological framework to identify suitable locations for the deployment of EV charging points in urban environments. To meet this objective, we acquired a mixed method approach including a systematic literature review, 12 semistructured stakeholder interviews which were thematically analyzed, and an Analytical Hierarchy Process (AHP). The outcome is a spatial model function, which consists of parameters and weights for estimating the suitability of each urban road link that will allow the establishment of EV charging points. Results show that the key location selection factors are: transport hubs, marked or controlled parking spaces, and points of interest. The less significant factor is public services. Therefore, there is a preference, in stakeholder level, for transport features over the land use ones (69% over 31%). Although this research is conducted in Greece, we intend to suggest methods and generate valuable findings that may be valid and generalizable for a more global context.

Micro-mobility transport modes like e-bikes and e-scooters promise higher flexibility when covering the first/last mile trip from/to the public transport stop/station to the destination point and vice-versa. However, safety concerns about riding a micro vehicle in mixed traffic limit the flexibility of shared mobility modes and make conventional ones still more attractive, e.g., private car and walking. This study investigates the effect of perceived safety in first/last mile mode choice by conducting an image-based double stated preference experiment targeted at potential micro-mobility users and developing ordinal and mixed logistic regression models. The Value-of-Safety (VoS) is introduced. It refers to the additional distance a user is willing to exchange to avoid an unsafe path. Main findings show that shared space can be a middle-ground solution, as it reports lower heterogeneity among individuals in terms of safety perceptions. The intensive use of e-scooters in mixed-traffic decreases the perceived safety of pedestrians, while e-bikers are threatened by the existence of heavy motorized traffic. Low mean VoS is also reported for e-scooters, demonstrating the unwillingness of potential micro-mobility service users to either detour or use this micro vehicle. The mean VoS of the e-bike is estimated as almost equal to that of the private car. It could be, hence, concluded that perceived safety can systematically explain the unobserved disutility of e-bikes.

E-scooters aspire to provide flexibility to their users while covering the first/last mile of a multimodal trip. Yet, their dual travel behavior, i.e., utilizing both vehicles’ roadways and pedestrians’ sidewalks, creates new challenges to transport modelers. This study aims to model e-scooter riding behavior in comparison to traditional urban transport modes, namely car and walking. The new modeling approach is based on perceived safety that is influenced by the road environment and affects routing behavior. An ordinal logistic model of perceived safety is applied to classify road links in a 7-point Likert scale. The parametric utility function combines only three basic parameters: time, cost, and perceived safety. First/last mile routing choices are modeled in a test road network developed in Athens, Greece, utilizing the shortest-path algorithm. The proposed modeling approach proved to be useful, as the road environment of an urban area is heterogenous in terms of safety perceptions. Indeed, the model outputs show that the flexibility of e-scooters is limited in practice by their low-perceived safety. To avoid unsafe road environments where motorized traffic dominates, e-scooter riders tend to detour. This decision-making process tool can identify road network discontinuities. Nevertheless, their significance regarding routing behavior should be further discussed.

E-scooter sharing services been grown exponentially within the last five years. They are based on the flexibility of accessing dense urban areas without specialized infrastructure. In modern cities, there are diverse road environments that impact the comfort, and therefore the attractiveness, of micro-mobility services. This study aims to investigate the willingness to use e-scooter sharing services, while considering the road environment. To formulate area-specific pricing policies, a stated preferences experiment with 243 respondents, who can be considered as potential users, is conducted in Athens, Greece and a binary logistic regression model with random beta parameters is developed. The analysis of the model marginal effects indicates that the integration of bonus points into micro-mobility services, combined with the option of transferring these points to parking services, can compensate a non-friendly road environment, thus increasing the service demand. The existence of roads with good pavement conditions and wide sidewalks significantly increased the willingness of respondents to use e-scooter sharing services. Unexpectedly, pedestrianized zones in a buffer area of 2 km radius from the trip origin reinforce the attractiveness of shared e-scooters, while the contribution of bike lanes and traffic calming streets (or shared space) were proven to be insignificant.