Ride-sourcing has recently been at the centre of attention as the most disruptive mode of transport associated with the so-called shared mobility era. Drivers, riders, the platform, policymakers, and the general public are considered as the main stakeholders of the system. While ride-sourcing platforms have been growing, so did the heightened tension between them and their drivers. That is why understanding drivers' behaviour and preferences is of key importance to ride-sourcing companies in managing their relationship with drivers (also known as driver-partners) and in retaining them in the presence of competence. Ride-sourcing drivers are not only chauffeurs but fleet owners. They can make various operational and tactical decisions that directly influence other stakeholders and the transport system performance as a whole. Conducting a series of focus groups with ride-sourcing drivers in the Netherlands, we have studied their opinions about the system functionalities as well as their possible interactions with the platform and wishes for changes. The focus group results suggest that the main decisions of drivers, which are ride acceptance, relocation strategies, working shift and area in which to work, could be affected by many elements depending on platform strategies, drivers' characteristics, riders' attributes, and exogenous factors. We find that part-time and full-time drivers, as well as experienced and beginning drivers, are characterized by distinctive behaviour. Flexibility and freedom were mentioned as the key reasons for joining the platform while an unfair reputation system, unreliable navigation algorithm, high competition between drivers, passenger-oriented platform, high-commission fee, and misleading guidance were acknowledged as being the main system drawbacks. Based on our findings, we propose a conceptual model that frames the relationship between the tactical and operational decisions of drivers and related factors.

A wide range of new on-demand mobility options are appearing in urban areas, one of them being pooled on-demand services (shared taxi-like services such as UberPOOL, LyftLine, OlaShare or ViaVan). Simulation studies have shown that, due to their collective nature, pooled on-demand services can bring large mobility benefits to urban areas, helping reduce congestion, pollution and space problems (ITF, 2017, 2016). These services provide flexibility to their users and trip matching only entails little travel time increases (Tachet et al., 2017). Despite these promising research findings, actual user adoption in operating pooled on-demand services is still very limited. Therefore, it is paramount to understand the demand aspects regarding pooled on-demand services in order to unleash all the potential benefits that their usage can bring to urban areas. Individual studies provide new insights into certain aspects of these services, yet they often lack a comprehensive perspective. A comprehensive perspective is, however, necessary in order to draw conclusions on how future mobility will develop. This contribution discusses the results of a series of recent scientific studies regarding the demand for pooled on-demand services in Dutch urban settings. It links the main individual research findings, and, additionally, it discusses them from a practitioners’ perspective. Other than aiming to provide a holistic picture regarding the demand for pooled on-demand services, this contribution aims at steering further joint discussion among the different transport stakeholder.

Electrifying mobility-on-demand (MoD) fleets is an important step towards a more sustainable transportation system. With increasing fleet size, MoD operators will be able to participate in the energy exchange market and will have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of i.e. a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a computationally very efficient model to study the trade-offs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet sizes and different numbers of charging units, we compare the performance of several reactive and scheduling policies. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.