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.

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Self-driving vehicles could make the operation of public transport services in a more flexible manner more affordable. Introducing shared automated vehicles would al¬low operating a fleet of smaller vehicles in a demand responsive manner. This could potentially impact the way we use and operate public transport services, which could eventually trigger changes in car ownership and land use. The main objective of this dissertation is to understand better what it means to deploy shared automat¬ed vehicles for on-demand public transport services. This is analysed from the per¬spective of three main stakeholders: (1) the user preferences of potential users, (2) the fleet operation supervised by the fleet manager, (3) and potential parking man¬agement strategies issued by a transport authority concerned with the introduction of shared automated vehicles.@en

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.

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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.

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At the brink of the introduction of self-driving vehicles, only little is known about how potential users perceive them. This is especially true for self-driving vehicles deployed in public transport services. In this study, the relative preferences for a trip with a self-driving bus is assessed compared to a trip with a regular bus, based on a stated preference experiment. Based on the responses of 282 respondents from the Netherlands and Germany, a discrete choice model is estimated as a Mixed Logit model including attitudes towards trust in self-driving vehicles and interest in technology. The results show that currently public transport passengers prefer the self-driving bus over the regular bus only for short trips. This is due to the finding that the value of travel time is about twice as high for the self-driving bus as for the regular bus for a short commuting trip. Findings from this study further suggest that the popularity of self-driving busses decreases with the presence of a human steward on-board, or if they are operated as a demand-responsive service with fixed routes. People who currently show a strong interest in technology or trust in automated vehicle technology perceive the self-driving busses better than others. The trust-effect is especially strong for women. In general, men are found to be more inclined to choose the self-driving bus than women. Preferences towards automated public transport services are expected to evolve along with the transition from demonstration pilots to their deployment in regular operations.

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The introduction of public transport services by fully automated vehicles can potentially change the way public transit services will be operated, as they allow shifting from rigid scheduled and route-bound services towards flexible, demand-responsive services. This study examines the potential performance of an Automated Demand Responsive Transport Service (ADRTS) as a replacement for scheduled bus services and simulates the effects of demand levels, vehicle capacity, vehicle dwell time and the initial vehicle distribution on system performance in terms of fleet size and system costs. The simulation tool allows simulating the operation of the ADRTS in a complete graph and is applied to the case study of Arnhem, the Netherlands. For this case study it has been shown that for a minimum fleet size following the imposed constraints, the operational costs range between 0.84 and 1.22 Euros and the average passenger wait time ranges between 2 and 6 minutes, according to the assumptions made on demand and operational parameters. The operational costs of the ADRTS showed to be in the same range of the current bus system, while providing a demand-responsive transport service with an average waiting time of around 4 minutes per passenger-trip. The economies of scale, which play an important role in public transport, are also apparent in the simulated ADRTS operations.@en

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.

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The rapid development of automated vehicles and the introduction of shared mobility services hold great potential for the development of automated public transport systems. However, their expected performance under alternative conditions and with alternative design factors remains unknown. In this study, the operation of a new automated mobility service was simulated and evaluated. The proposed transport system was defined as an automated demand-responsive transport system (ADRTS), which consisted of a demand-responsive public transportation service providing sharable rides without fixed routes or timetables in automated vehicles. Requests for the ADRTS were combined if they shared the same pickup and drop-off locations, subject to vehicle capacity limitations. Rides were launched within a predefined maximum vehicle dwell time, and routes were chosen without detours. The proposed system was tested in a field operation pilot project of the operation of automated vehicles in Wageningen in the Netherlands under conditions with different demand patterns, vehicle capacities, and operational factors. The simulation model determined the minimal and optimal fleet size for operation of the shuttle service when total operational and travel costs were minimized and maximum passenger waiting time requirements were satisfied. The results indicated that the most effective way to reduce the system cost per passenger of the ADRTS in the presented case study was to increase the demand level, increase the share of passengers who arrive independently, and use adequate vehicle sizes and short vehicle dwell times. @en

Simulation of a new mobility service for urban and regional public transportation operated by automated vehicles • The simulation tool determines for a certain demand the required fleet size and the overall driven kilometers while constraining the maximum passenger waiting time • The simulation output allows to estimate the operational and passenger costs and consequently to determine the optimum fleet size for obtaining minimum overall costs@en