This thesis explores the impact of transport disadvantage on the unemployed's participation in out-of-home activities beyond the labor market. It delineates eight distinct groups primarily based on transport accessibility and car availability among the unemployed, assessing how these factors are shaped by socio-demographic characteristics and influence engagement in various activities, such as shopping, social visits, and recreational activities. Latent class cluster analysis is employed to identify distinct patterns of transport accessibility and car availability among the unemployed, avoiding arbitrary definitions and incorporating socio-demographic determinants. This approach reveals a broad spectrum of transport accessibility and car availability patterns, ranging from low to medium to high, organized into groups or clusters. The research contrasts out-of-home activity participation between unemployed and employed individuals with similar socio-demographic profiles to identify relative deficits, using propensity score matching. This method isolates the combined effect of employment status and non-socio-demographically determined transport disadvantage on activity participation and travel behavior. The study then conducts a comparative analysis of deficits in activity engagement across the eight groups, allowing for the separation of the direct impact of transport disadvantage from direct effects related to employment status. A key discovery of this thesis is a compensatory mechanism among unemployed individuals who tend to increase their participation in non-work-related activities. However, this compensatory behavior is not uniformly realized across all groups, particularly not among those with low car availability, who exhibit significant deficits in out-of-home activity participation. Surprisingly, accessibility does not seem to influence the differential in activity participation between the unemployed and their employed counterparts, suggesting that car availability is the primary dimension through which compensatory behavior is enabled. This finding challenges the hypothesized compensatory relationship between transport accessibility and car availability and suggests that individuals with higher car availability, regardless of their level of accessibility, are at a lower risk of transport-related social exclusion, whereas those with low car availability exhibit a higher risk, irrespective of their accessibility levels. Furthermore, the results suggest that socio-demographic factors compound with employment status and transport disadvantage to inhibit the full expression of this compensatory mechanism among low car availability groups. These groups, representing 61% of the unemployed sample, typically have higher accessibility and comprise younger people, individuals living in single-person households, those with lower household incomes, and individuals with a non-native (parental) birthplace, predominantly residing in highly urbanized areas. This study, informed by existing literature on affordability limitations, recommends enhancing affordable transport accessibility for unemployed individuals with low car availability, advocating for subsidized public transport fares and travel allowances for low-household-income groups. It supports promoting mixed-use developments and enhancing infrastructure for active transportation modes to increase activity options within reachable distances for those with limited car availability. Future research should explore physical and digital in-home activities, subjective experiences of social inclusion, and the role of transport and housing expenses in shaping transport affordability.

Limiting transport poverty is consequential in improving well-being and employment levels, which play meaningful roles in deciding public policy. We analyze how different socioeconomic and built environment factors are related to the transport poverty environment for car and public transport in terms of strength, significance, and direction for neighborhood zones within the Amsterdam Metropolitan Area—our study area. Additionally, we provide policy recommendations for the study area. To this end, we use spatial distributions of environmental transport poverty indicators and perform weighted least-squares regression analyses, where we regress each transport poverty indicator on all built environment and socioeconomic variables. Our regression analyses are preceded by a combination of logarithmic variable transformation, insignificant variable elimination, and data normalization. The environmental transport poverty indicators consist of average travel times, average single-trip travel costs, and the number of accessible jobs within thirty minutes. Regarding the built environment; we include inhabitant density and job density, whereas the socioeconomic characteristics in our analyses consist of household size, five age cohorts (0-18, 18-34, 35-54, 55-64, and $65^{+}$), gender, car ownership, and income. Our results indicate that levels of environmental car transport poverty are fairly low over the whole study area when compared to the public transport poverty environment—the highest car transport poverty levels among all zones correspond with the lowest levels of public transport poverty. Regression results demonstrate that differences in the transport poverty environment are substantially correlated with differences in the zonal built environment and socioeconomic characteristics only for public transport travel times and public transport job accessibility, which are also the only transport poverty indicators to exhibit considerable variation among the study area zones in general. Furthermore, the strength and significance of job density almost invariably greatly exceed those of inhabitant density and the socioeconomic variables—in relation to the transport poverty environment indicators. Inhabitant density is, despite being overshadowed by job density, also deemed to exhibit a marked correlation to the public transport measures of travel time and job accessibility. Both built environment variables are related to favorable transportation conditions. Remarkably, all socioeconomic variables display either insignificant or rather weak correlations to the transport poverty environment. Consequently, we suggest focusing mainly on public transport measurements of travel times and job accessibility when analyzing differences in the transport poverty environment at a neighborhood level and the roles of job density and (to a lesser extent) inhabitant density herein. We visualize the combined spatial distributions of both job density and average public transport travel times, and inhabitant density and average public transport travel times for our study area. Areas potentially interesting for public transport improvement are identified based on the simultaneous occurrence of both relatively high levels of job or inhabitant density and relatively high travel times.