József Pál Lieszkovszky (p)

Stefanie Peer

This paper tries to identify the connection between social exclusion and transport-related social exclusion.

The definition of social exclusion has abundant literature. The most accurate definition is given by the researchers from the LSE (London School of Economics): an individual is considered to be socially excluded if (s)he does not participate in social activities from time to time but at the same time (s)he wants to join them, however something prevents her/him from participating in them.

The transport-related researchers – carried out mainly by engineers and transport economists – neglected the social aspect until the beginning of 2010. At the same time – as part of social exclusion – traffic-based exclusion at the public policy level has been dealt with since the late 1990s (first in the United Kingdom to government order).

Transport related exclusion can be briefly identified by saying that the person does not have the suitable access to any means of transport to reach the place where important social activities are taking place in which (s)he would participate in. One of the crucial bases of transport exclusion is the possibility of access to car concerning the individual, which can be structural or comfort. A specific group of structurally dependent individuals is the "constraint car owners", who are mainly the residents of low-status households – especially in rural areas – with no adequate public transport. These households would not otherwise maintain a car, but they have to – due to public transport being unusable – and this is a tremendous financial burden for them. Those social groups are concerned in transport exclusion who have reduced mobilities because of various reasons. These groups can be classified in lots of ways (e.g. age, sex, income status).

As part of my PhD research on transport related social exclusion in rural areas, this analysis aims to apply the Zimpel index to evaluate settlement accessibility in rural Hungary and introduce a new method (points system) to classify microregions. These methods are based on a dataset including public transport supply (no. of services in a working day, average journey times) between rural settlements and microregion centres for all microregions in Hungary.

Ravid Luria (p), Amnon Frenkel, Yoram Shiftan

József Pál Lieszkovszky

Many cities have been implementing digital systems, including in transportation, in order to improve their mobility systems. The process of digitizing services has generally resulted in exclusion of digital illiterate populations. Most of the previously studied determinants of the digital divide are related to socio-demographic attributes. Some studies, however, indicate a correlation between habitus and digital divide. None of the studies have modeled habitus in relation to the digital divide in mobility and its effect on accessibility and social integration. This study aims to explain the relationships between habitus, smart mobility and digital divide and their reflection in space and society. Therefore we developed a conceptual model, which based on three hypotheses: First, we expect the digital divide in Smart mobility to be a result to a habitual mechanism. This mechanism includes, besides the socio demographic factors, the attitudes, perceptions and travel habits of individuals. Second, we hypothesize that the digital-mobility divide is an accessibility barrier for marginalized social groups. The digital-mobility divide is the combination of mobility and digital skill disparities, and accessibility refers to the capability of reaching destinations. These destinations provides opportunities such as employment and education. Third, we hypothesize that high or low level of accessibility may cause social and spatial integration or segregation in accordance. The model will serve as a basis for an empiric research. We presume that it would provide new insights of how to increase mobility capacity and accessibility of marginalized groups. The insights can be used for policy making regarding the implementation of digital modes and the empowerment of disadvantaged groups. This kind of policy might contribute to a better socio-spatial integration, and eventually it might progress a more equitable and just society in urban spaces

Stefanie Peer (p), Reinhard Hössinger, Simona Jokubauskaite

Ravid Luria

Transport-related measures that are effective in reducing CO2 emissions however often suffer from a lack of public acceptance. In this paper, we investigate, which bundles of measures fulfill the climate goals and at the same time have high levels of public acceptance. Our aim is thus to better understand the determinants of the acceptance of specific policies as well as policy bundles, in particular the trade-off between private costs and social benefits (most importantly, lower CO2 emissions).

For this purpose, we introduce a novel online tool in which citizens can select between 11 different policy measures and between 3 to 5 different intensity levels for each measure (e.g. different fuel tax levels). The measures include push measures such as fuel taxes, road pricing, or a limitation of parking spaces, as well as pull measures like increased investments in public transport, cycling, and e-mobility subsides. When selecting between measures and intensities, respondents see a real-time visualization of the expected consequences of their choice in terms of CO2 emissions, public revenues and expenditures (including penalty payments for missing the climate goal, if applicable), as well as personal monetary consequences (based on his/her indicated current mobility behavior). In a second step, also the expected effects of the chosen bundle of policies on specific population groups (the rich, the poor, people strongly dependent on car ownership, etc.), on the quality of specific transport modes, noise, public health, public space, and the economy are shown.

Besides the personalized changes in monetary transport costs, which are computed based on the information provided by each respondent on their current mobility behavior, all other effects of the different policy measures (and their different intensity levels) have been derived from relevant scientific studies, policy documents, and a Delphi survey among experts.

The tool has been filled in by more than 1500 respondents. Besides a rich descriptive analysis, we present advanced choice models that capture the trade-offs faced by the respondents when filling in the tool. In line with the descriptive analysis, we find that personal benefits and costs are weighted highly in the decisions.