| 1. |
- Ihanus, Harri
(kompositör, creator_code:cre_t)
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Present past
- 2006
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Konstnärligt arbeteabstract
- Harri Ihanus, guitar; Jerry Bergonzi, saxophone; Renato Chicco, piano; Andrea Michelutti, drums; Dave Santoro, bass Fysisk medie: CD
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| 2. |
- Jakobsson Bergstad, Cecilia, 1967, et al.
(författare)
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The influence of socioeconomic factors in the diffusion of car sharing
- 2018
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In order to put into relationship, the diffusion of car sharing practices and relevant individual sociodemographic and economic factors, the research started analysing the information in some of the national travel surveys administered throughout Europe. From this research, it emerged that in most of the cases information on the use of car sharing for daily mobility at a national level is of poor quality. In particular, the characteristics of car sharing members such as gender, age, car ownership and travel behaviour have been compared with the characteristics of the population living in the same country or city, coming from the national (or city) travel survey. Since there is an urgent demand to reduce the damaging impact of transportation on the environment (air pollution, noise pollution, reduced green areas, traffic accidents, etc.) in urban cities, we aim to answer the following question: What are the main behavioural, psychological and social factors influencing people’s choice to use car sharing? This main question can be operationalized in more specific sub questions: do users and nonusers of car sharing differ, regarding transport choices? Are there differences among ages and genders for specific services and demands? What are the main motives for using car sharing for users and non-users? Are the social, behavioural and psychological aspects influencing people’s intention to use car sharing? If so, to which extent does it occur? In order to answer those questions, this report was structured in 5 main sections: Section 1: In this section multiple sources were exploited in order to give insights about the impact of car sharing on travel behaviours, among different kind of users and different countries/ cities. Section 2: Based on the model of Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB), the latent variables Attitudes, Perceived Behaviour Control (PBC), Perceived Usefulness (PU), Ease of Use (EU), Subjective Norms (SN), Trust, Personal Norms (PN) Environmental Awareness (EA) and Habit were tested in a linear regression model along with sociodemographic variables to predict behaviour intention to use car sharing. The data were collected by STARS partners along EU countries with users and non-users of car sharing. Section 3: In this case study, it is examined how car sharers in Flanders assess the services of different car sharing organisations. Focus was given on membership, car ownership, customer satisfaction, overall characteristics of the service, costs, flexibility and offer of cars with alternative fuels. The influence of socioeconomic factors in the diffusion of car sharing GA n°769513 Page 17 of 243 Section 4: This second case study analysed and compared behavioural data from URBI during two months in Berlin, Milan, Turin and Madrid. Focus was given to patterns and hourly distribution of trips. Section 5: This case study analysed and compared behavioural data for users and non-users of car sharing in Germany. Focus was given to social demographic variables, the use of smartphones, attitudes towards different transport modes, incentives to use car sharing, support to implement car sharing, characterization of users of car sharing and relation to the characteristics of services.
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| 3. |
- Martins Silva Ramos, Érika, 1991, et al.
(författare)
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Mobility styles and car sharing use in Europe: attitudes, behaviours, motives and sustainability
- 2020
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Ingår i: European Transport Research Review. - : Springer Science and Business Media LLC. - 1867-0717 .- 1866-8887. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- What are the profiles of both users and non-users of car sharing in European cities regarding their travel patterns and psychological aspects? Two subsamples (1519 users and 3695 non-users of car sharing) participated in a survey, translated into seven languages, with 36 questions regarding attitudes towards car sharing, the environment, political orientation, personal norms, frequency of use of different transport modes and transport mode choice for different travel purposes. Through a hierarchical cluster analysis, five distinct mobility styles were identified, with no a priori restriction of the number of clusters. The mobility styles were further characterised by sociodemographic variables and by the motives for making use of car sharing. This paper discusses the implications of research based decision-making and urban planning in a way that guarantees long-term human and environmental security.
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| 4. |
- Martins Silva Ramos, Érika, 1991, et al.
(författare)
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Overall assessment of the drivers for behavioural change
- 2019
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The final task of work package 4 is to draw on the empirical evidence collected in the previous tasks of this current WP as well as the results of WPs 2 and 3. The aim is to give an overall picture of the underlying mechanisms behind observed behavioural changes towards an increased use of shared mobility services. The objective is furthermore to assess the relative importance of sociodemographic, individual and contextual factors as well as advance the analyses of how the characteristics of the different services and the business models (classified into car sharing operator profiles) interact with users profiles (of sociodemographic and attitudinal characteristics) and mobility styles (including user attitudes, travel modes and frequencies). Finally, the WP includes a workshop with the aim to discuss and validate the results with experts on car sharing both from the inside as well as the outside of academia. The workshop took place on the 24th of January in Bremen, Germany. The report consists of four main sections; Introduction, Method, Results and Conclusions. Furthermore, the Method and Result sections have three subsections; one on the work carried out by UGOT i.e. the SEM analyses, another section describing the work by POLITO on car sharing user trends according to different user profiles, and a final subsection describing the work-shop hosted by the city of Bremen. The main findings of this deliverable are: Users of free floating car sharing (Italian sample) and free floating with pool stations (Sweden) were the users with the lowest percentage of car-free household. In Italy, users of free floating services are more likely to subscribe to more than one service (1.5 on average) of the same typology of service. The frequent users of private cars are, at the same time those that envisage greater use of car sharing in the future than today, while among those who own and use private cars less frequently (MultiOC users, who are registered to more than one car sharing variant in parallel, and FFPS), there is a lower propensity for an increase compared to the current level of car sharing use. Even though all MultiOC users are registered to a free floating service in combination with another car sharing typology (FFPS in Italy, RTSB in Sweden and Germany), they are more frequently users of PT and active modes (walk and bike) and they have a higher degree of car-free households than the free floating users. Therefore, services integration and a higher degree of MultiOC users may be one important key to reduce the use of private cars and consequently its impacts. The FFOA service is more likely to grow in terms of number of subscribers in Italy; while in Sweden, round trip station based service have the highest number of potential users. Clearly these predictions may be affected by the actual provision of such services in the cities if there is a lack today. Overall assessment of the drivers for behavioural change GA n°769513 Page 8 of 93 The strongest direct predictors of behavioural intention (BI) to use (or increase using) car sharing services in a near future (6 months) were perceived behaviour control (PBC), currently being registered on a car sharing service, a lower degree of past car based travels and trust in the quality of the service delivered. The number of current car sharing operators in the city was not a predictor of behaviour intention, which indicates that by only increasing the number of operators within cities or fleet sizes, is not enough to induce behaviour change. It is instead more important to increase the perceived usefulness of car sharing services for people’s travels necessities. Women could be a target niche in the market, since being a woman has a positive direct effect on BI to use car sharing in the future. In addition, increasing trust in the service availability and quality is also a possible strategy to foster use of car sharing. Past travels by car based modes leads to driving habit formation and when this habit becomes stronger, one is less likely to express a strong intention to use car sharing The expert work shop in Bremen contained presentations of the STARS project in general and results from WP 4 summarized above in particular. Experts from car sharing organisations, cities and other research projects attended. Overall the work shop proved that the knowledge is welcome and can be useful when developing the services, as well as implement those services in a city.
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| 5. |
- Sanvincente, Esti, et al.
(författare)
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Key technology and social innovation drivers for car sharing
- 2018
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Car sharing has huge potential to improve quality of life and traffic conditions in cities. It offers a car at your disposal without the need of ownership and has the potential to reduce the number of cars in cities without reducing individual mobility. The wide spread of information and communication devices (smartphones in particular) and of social media and web platforms, together with the sharing economy that is growing into a cultural consumption approach, are at the basis of this development. Moreover, smart technology has helped to improve the experience of using car sharing, making booking, accessing and using shared transport easier. While car sharing in recent years has witnessed double-digit growth, particularly in bigger cities where the costs of owning a car can be more easily offset, only a small percentage of people actually use it when compared to other urban modes. This leaves a gap, meaning that cities are unable to reap the full benefits of car sharing. With this in mind, the STARS partners set out to better capture the underlying forces that affect car sharing. In fact, D2.2 of the STARS project focuses on a number of aspects to understand how mobility sharing practices are influenced by the arrival of digital technologies, automotive advances, the emergence of social innovation patterns and mobility behaviour and choices. The first chapter of the present report explores the three types of underlying forces that are essential to understanding the new era of mobility and particularly the future of car sharing. These include technology enablers, such as ICT based innovations and automotive advances; societal changes such as the emergence of new forms of sharing economy practices and Mobility as a Service; and attitudinal and motivational characteristics of citizens with regards to emerging urban transport opportunities. The second part aims to advance understanding of how car sharing adoption trends are influenced by the evolution of sociodemographic characteristics (population characteristics, education level, income), car ownership rate and mobility split, and the use of web 2.0 services (participation in social networks, internet banking and the use of internet for travelling purposes). To do so, we undertook a complementary approach in which we analysed aggregated statistics for a time series in a given area, or the same statistics in different countries and cities. Car sharing data was gathered through different sources, including car sharing operators’ websites, newspaper, annual surveys for the different car sharing systems, and statistical data at national and city level. Finally, a specific analysis of three use cases was carried out with the objective of studying the main drivers and barriers to deploy car sharing in urban areas. Autolib in Paris, Cambio in Bremen and Drivy in Barcelona, were the selected use cases. The methodology undertaken to conduct the three case studies combined data from literature analysis and expert interviews. A multi-level perspective was then used to help analyse both the internal (business model and business performance) and external (city/local related) factors shaping the car sharing deployment in these urban areas. Key technology and social innovation drivers for car sharing GA n°769513 Page 8 of 106 The analysis showed that both digital technologies and transport innovations hold a great promise for the development of car sharing services, in terms of enhancing fleet management and maintenance and improving user’s experience. Moreover, while the arrival of driverless autonomous vehicles represents a unique opportunity for fundamental change in urban mobility, it will only help to reduce the number of cars (reduce car ownership, car traffic and parking needs) and drastically improve mobility options, if they come as shared fleets integrated with public transport. As automotive advances are reshaping the driving experience - turning drivers into passengers and pulling users at the centre of the mobility ecosystem – people’s values, norms and attitudes towards shared mobility are shown to change significantly with the rapid spread of smartphones and new practices of sharing economy. Therefore, new predictors of travel mode choice, including technological and social innovations, are highlighted in the present study to explore the attitudebehaviour gap related to mobility choices. Finally, it is worth stressing that this study has shed light on the drivers and challenges that car sharing operators face, both from a business model and city level perspective. Indeed, based on the operator’s strategy, different impact levels have been highlighted.
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