With the anticipated introduction of self-driving vehicles, new challenges arise for urban transport- and planning authorities. This study contributes to the efforts of formulating the potential opportunities and threats stemming from the introduction of larger fleets of self-driving vehicles to our cities, and what action could be taken by transport authorities to shape this introduction beneficially. In particular, the focus is put on the impact different parking management strategies can have on the performance of a fleet of shared automated vehicles providing on-demand transport services. This analysis focuses on aspects of service efficiency, externalities and service provision equity. The selected parking management strategies are tested in a large-scale activity-based simulation of a case study based on the city of Amsterdam, in which the parking facilities for SAV are digitally mapped throughout the city for different parking scenarios. The vehicles of the fleet aim at relocating to zones with high future demand, which can lead to bunching of vehicles at demand-hotspots. Parking management in the form of restricting parking facilities forces idle vehicles to spread out more evenly in the network. We show that this can reduce average passenger waiting times, increase service provision equity, cause less congestion and even can reduce the necessary fleet size. However, this comes at the cost of an increase in vehicle-kilometres-travelled, which reduces fleet efficiency and causes more undesired service externalities. Parking management is thus a simple, yet effective way for transport authorities to (a) determine where idle self-driving vehicles operating an on-demand transport service will be parked and (b) influence the performance of said transport service.

Equity considerations in transportation planning literature have received increasingly more attention in the previous decades. While there have been theoretical suggestions to base transportation planning methods on the philosophical principle of “sufficientarianism” (whereby everyone is entitled to a minimum level of a good or service), the proposed approaches have not yet been developed enough to be usable for policy decision-making. In this paper we aim to bridge this gap by operationalizing in a case study an indicator of equity based on the theoretical work of Martens (2017) which argues for sufficientarianism. The presented formalised methodology can identify and quantify equity issues in transportation, is flexible to different contexts, and is a transparent way to assess equity in transportation. The case study shows that data availability is an important constraint and that careful attention must be paid to various assumptions and choices made.

New forms of shared mobility such as free-floating car-sharing services and shared automated vehicles have the potential to change urban travel behaviour. In this paper, we identify potential user classes for these new modes. For this, a stated choice experiment on mode choice among a sample of the Dutch urban population has been conducted, which features free-floating car-sharing and shared automated vehicles next to private vehicles, bus, and taxi. The experimental design allows disentangling the effects of vehicle ownership, vehicle sharing and vehicle automation on the perceived utility of these modes. Further contributions lie in the identification of user classes for shared and automated mobility services and their potential migration from their current modes to the these services. Latent class choice models were estimated to capture the heterogeneity in these preferences among the respondents. The most explanatory mode choice model is obtained by estimating a 3-class nested logit model capturing the impact of vehicle ownership. The results show that higher educated and more time-sensitive respondents are more inclined than others to favour the (automated) car-sharing options. By simulating a scenario that directly compares car with free-floating car-sharing and taxi with shared automated vehicles, a migration analysis has been performed. This analysis shows that the preferences towards shared automated vehicles and free-floating car-sharing is highest for those currently combining car and public transport for their commute. Commuters using the car showed a high preference towards free-floating car-sharing, in particular as for the latter no parking fees are issued. Respondents currently commuting by public transport showed the lowest preference for the new modes.

With shared mobility services becoming increasingly popular and vehicle automation technology advancing fast, there is an increasing interest in analysing the impacts of large-scale deployment of shared automated vehicles. In this study, a large fleet of shared automated vehicles providing private rides to passengers is introduced to an agent-based simulation model based on the city of Amsterdam, the Netherlands. The fleet is dimensioned for a sufficient service efficiency during peak-hours, meaning that in off-peak hours a substantial share of vehicles is idle, requiring vehicle relocation strategies. This study assesses the performance of zonal pro-active relocation strategies for on-demand passenger transport under constrained curbside parking capacity: (1) demand-anticipation, (2) even supply dispersion and (3) balancing between demand and supply of vehicles. The strategies are analysed in regard to service efficiency (passenger waiting times, operational efficiency), service externalities (driven mileage, parking usage) and service equity (spatial distribution of externalities and service provision). All pro-active relocation strategies are outperformed by a naïve remain-at-drop off-location strategy in a scenario where curbside parking capacity is in abundance. The demand-anticipation heuristic leads to the highest average waiting times due to vehicle bunching at demand-hotspots which results in an uneven usage of parking facilities. The most favourable results in regard to service efficiency and equity are achieved with the heuristics balancing demand and supply, at the costs of higher driven mileage due to the relocation of idle vehicles. These results open up opportunities for municipalities to accompany the introduction of large fleets of shared automated vehicles with suitable curbside management strategies that mitigate undesired effects.

The introduction of taxi-like transit services operated by shared automated vehicles comes into sight with the development of vehicle automation. In this paper, the operation of such a service is simulated for a generic grid network in order to determine the impact of different relocation strategies for idle vehicles on passenger waiting time, empty mileage and parking needs. The tested strategies consist of remaining idle at the latest drop-off location, returning to the initial position, relocating to a random location, relocating according to anticipated demand or relocating to a zone with a low vehicle supply. For the simulated case study, remaining idle outperformed the other relocation strategies in terms of service efficiency and service effectiveness, while the strategy of evenly or randomly dispersing vehicles over the network lead to largest reduction of the number of parked vehicles per link, and the strategy of anticipating demand to largest reduction of deadheading mileages.