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- 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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| 2. |
- Solorzano, Leslie, 1989-, et al.
(författare)
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Whole Slide Image Registration for the Study of Tumor Heterogeneity
- 2018
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Ingår i: MICCAI 2018 - International Workshop on Ophthalmic Medical Image Analysis. - Cham : Springer. - 9783030009496 - 9783030009489 ; , s. 95-102
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Konferensbidrag (refereegranskat)abstract
- Consecutive thin sections of tissue samples make it possible to study local variation in e.g. protein expression and tumor heterogeneity by staining for a new protein in each section. In order to compare and correlate patterns of different proteins, the images have to be registered with high accuracy. The problem we want to solve is registration of gigapixel whole slide images (WSI). This presents 3 challenges: (i) Images are very large; (ii) Thin sections result in artifacts that make global affine registration prone to very large local errors; (iii) Local affine registration is required to preserve correct tissue morphology (local size, shape and texture). In our approach we compare WSI registration based on automatic and manual feature selection on either the full image or natural sub-regions (as opposed to square tiles). Working with natural sub-regions, in an interactive tool makes it possible to exclude regions containing scientifically irrelevant information. We also present a new way to visualize local registration quality by a Registration Confidence Map (RCM). With this method, intra-tumor heterogeneity and characteristics of the tumor microenvironment can be observed and quantified.
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