The measurement of social and psychological phenomena has been advanced by recent progress in the fields of behavioural economics and hedonic psychology. In addition, the increased interest in understanding how individuals perceive their own quality of life, has led to investigating the relations between various macro and individual level variables, generically subsumed as happiness. For many “happiness is considered to be an ultimate goal in life” and it plays an important role in the way people perceive the overall society they live in. Therefore, social scientists and behavioural economists are now stressing the importance of well-being measures, related to people’s evaluations of their quality of life in addition to economic indicators. In the transport sector, project evaluation is mainly based on cost–benefit analyses using economic indicators. However, any provided transportation project/service impacts the quality of the travel experience, the well-being of travellers and their travel behaviour. Competitiveness of modes may be also affected by the promotion of derived or experienced travellers’ well-being. Thus, existing behavioural travel choice models should be enhanced with regards to their behavioural validity incorporating the impacts of travelling happiness/ satisfaction. This study aims to understand and model the impact of stated (anticipated) happiness in the decision choice between a private transport mode—car, and a public transport mode—metro.

The aim of this paper is to develop a methodological framework for the incorporation of social interaction effects into choice models. The developed method provides insights for modeling the effect of social interaction on the formation of psychological factors (latent variables) and on the decision-making process. The assumption is based on the fact that the way the decision maker anticipates and processes the information regarding the behavior and the choices exhibited in her/his social environment, affects her/his attitudes and perceptions, which in turn affect her/his choices. The proposed method integrates choice models with decision makers’ psychological factors and latent social interaction. The model structure is simultaneously estimated providing an improvement over sequential methods as it provides consistent and efficient estimates of the parameters. The methodology is tested within the context of a household aiming to identify the social interaction effects between teenagers and their parents regarding walking-loving behavior and then the effect of this on mode to school choice behavior. The sample consists of 9,714 participants aged from 12 to 18 years old, representing 21 % of the adolescent population of Cyprus. The findings from the case study indicate that if the teenagers anticipate that their parents are walking lovers, then this increases the probability of teenagers to be walking-lovers too and in turn to choose walking to school. Generally, the findings from the application result in: (a) improvements in the explanatory power of choice models, (b) latent variables that are statistically significant, and (c) a real-world behavioral representation that includes the social interaction effect.

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.

Urban Air Mobility (UAM) constitutes a future aerial mobility alternative, which concerns the use of electric and autonomous aerial vehicles for transporting people throughout a planned network of vertiports. To materialize UAM, several actors of the air and urban transport ecosystem play a vital role. This paper describes the insights gathered from 32 key stakeholders around the world to present and frame the key aspects for the future implementation of UAM. The participants include representatives from the UAM industry such as airports, airlines, aviation consulting companies, academia, and authorities. The data collection encompasses various key research areas, covering topics such as UAM strengths, weaknesses, opportunities and risks, requirements for implementation, concept integration in the existing transport system, specific use cases, business models, and end-user segments. The research aims at setting up the stakeholder scene and expanding the current literature for UAM by engaging key decision makers and experts towards shaping the new mobility era. The results demonstrate that ensuring certification standards for UAM fleets and updating the current legal and regulatory framework are the main prerequisites for UAM’s realization. In addition, the usage of UAM for transporting cargo or for air ambulance services are the most mature business models for the coming decade.

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.