The introduction of shared autonomous vehicles into the transport system is suggested to bring significant impacts on traffic conditions, road safety and emissions, as well as overall reshaping travel behaviour. Compared with a private autonomous vehicle, a shared automated vehicle (SAV) is associated with different willingness-to-adopt and willingness-to-pay characteristics. An important aspect of future SAV adoption is the presence of other passengers in the SAV—often people unknown to the cotravellers. This study presents a cross-country exploration of user preferences and WTP calculations regarding mode choice between a private non-autonomous vehicle, and private and shared autonomous vehicles. To explore user preferences, the study launched a survey in seven European countries, including a stated-preference experiment of user choices. To model and quantify the effect of travel mode attributes and socio-demographic characteristics, the study employs a mixed logit model. The model results were the basis for calculating willingness-to-pay values for all countries and travel modes, and provide insight into the significant heterogeneous, gender-wise effect of cotravellers in the choice to use an SAV. The study results highlight the importance of analysis of the effect of SAV attributes and shared-ride conditions on the future acceptance and adoption rates of such services.

The technology that allows fully automated driving already exists and it may gradually enter the market over the forthcoming decades. Technology assimilation and automated vehicle acceptance in different countries is of high interest to many scholars, manufacturers, and policymakers worldwide. We model the mode choice between automated vehicles and conventional cars using a mixed multinomial logit heteroskedastic error component type model. Specifically, we capture preference heterogeneity assuming a continuous distribution across individuals. Different choice scenarios, based on respondents’ reported trip, were presented to respondents from six European countries: Cyprus, Hungary, Iceland, Montenegro, Slovenia, and the UK. We found that large reservations towards automated vehicles exist in all countries with 70% conventional private car choices, and 30% automated vehicles choices. We found that men, under the age of 60, with a high income who currently use private car, are more likely to be early adopters of automated vehicles. We found significant differences in automated vehicles acceptance in different countries. Individuals from Slovenia and Cyprus show higher automated vehicles acceptance while individuals from wealthier countries, UK, and Iceland, show more reservations towards them. Nontrading mode choice behaviors, value of travel time, and differences in model parameters among the different countries are discussed.

As the private sector is under heavy pressure to serve the ever-growing e-commerce market, the potential of implementing new disruptive mobility/logistics services for increasing the level of the current last-mile delivery (LMD) services, is emerging. Vehicle automation technology, characterized by high-capacity utilization and asset intensity, appears to be a prominent response to easing this pressure, while contributing to mitigation of the adverse effects associated with the deployment of LMD activities. This research studied the perceptions of Greek end-users/consumers, regarding the introduction of autonomous/automated/driverless vehicles (AVs) in innovative delivery services. To achieve this, a mixed logit model was developed, based on a Stated Preferences (SP) experiment, designed to capture the demand of alternative last-mile delivery modes/services, such as drones, pods, and autonomous vans, compared to traditional delivery services. The results show that the traditional delivery, i.e., having a dedicated delivery person who picks up the parcels at a consolidation point and delivers them directly to the recipients while driving a non-autonomous vehicle—conventional van, bike, e-bike, e-scooter—remains the most acceptable delivery method. Moreover, the analysis indicated that there is no interest yet in deploying home deliveries with drones or AVs, and that participants are unwilling to pay extra charges for having access to more advanced last-mile delivery modes/services. Thus, it is important to promote the benefits of innovative modes and services for LMD, in order to increase public awareness and receptivity in Greece.

Exploring route choice in the context of tolled alternatives can support road operators to achieve better utilization of the infrastructure, as well as maximizing revenue collection. The research presented in this paper is conducted in the context of OPTIMUM, a European Union-funded project. The research objectives include a two-component system of models that proactively calculates commercial vehicles’ toll prices. The component presented in this paper rests on the development of a route choice model that estimates the probabilities of using two alternative routes (toll road vs. national road), based on route attributes and user characteristics. To explore the usefulness of the proposed methodology a case study involving 50 truck drivers and 25 freight operators was conducted in Portugal between January 2016 and November 2017. Results from the route choice model reveal interesting insights about the role of incentives in the choice of toll roads, the perspectives of the different decision-makers and produce Values of Time for the study area.