This research evaluates the accessibility in industrial areas to the labour force by applying a spatio-temporal accessibility model in the IJmond region, located in The Netherlands. Industrial areas are crucial for regional economic activity, impacting employment opportunities for the labour force, especially those with lower incomes. This research aims to understand how the spatiotemporal configuration of industrial areas affects accessibility for different demographic groups and its implications for transport equity. The study shifts focus from job accessibility for workers to workforce accessibility from the employers' perspective, highlighting challenges faced by industrial areas regarding public transport. A gravity model is built to analyse spatiotemporal accessibility using socio-demographic data from CBS and geographic data from IBIS. Findings indicate significant disparities in non-spatiotemporal accessibility levels between car and public transit, with car accessibility generally higher. Focusing on spatio-temporal accessibility for transit has shown that levels decrease further during the night. Many industrial areas become 'unreachable' by public transit during nighttime hours with those that remain reachable, facing significantly longer travel times. In addition, it has been found that low-income workers tend to live closer to industrial areas and in higher concentrations compared to high-income workers. Recommendations include developing alternative transport solutions that will primarily support shift workers during nighttime, enhancing pedestrian and cycling routes, and utilizing more detailed neighbourhood-level data for future studies that aim to improve accessibility and gain more insights into equity in industrial areas.

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.

Travelling to work is one of the essential activities in individuals’ daily lives. However, commuters who depend on public transport often face significant challenges in accessing job opportunities, resulting in transport-related social exclusion risks. In recent years, shared mobility services have gained popularity as a solution, offering greater flexibility in first/last-mile segments of multimodal trips. These services have promising potential to improve accessibility for those who rely on public transport, thereby addressing issues of accessibility inequity. Such an intervention aligns well with the unique Dutch cycling culture, where cycling already accounts for a substantial portion of transit journeys. However, limited bike availability at the egress side diminishes the utilization of bikes for the last mile of a trip. To address this issue, integrating shared bikes with transit as an egress mode could be an effective intervention.

This thesis conducted a ”what-if analysis” in the Amsterdam Transport Region to investigate the impacts on job accessibility for different commuters and the equity of the whole transportation system if shared bikes are provided at transit stations as the egress mode. The results highlight the considerable potential of this integration in improving job accessibility and promoting social equity.

Implications drawn from the analysis provide valuable insights. Statistically, groups without car access can benefit more than groups with car access from integrating transit and shared bikes, but benefits might favour high-income groups. Geographically, accessibility improvements are mainly distributed to commuters living next to transit stations, while equity improvements are concentrated in areas with dense and developed public transport systems. Additionally, commuters living close to transit stations are more likely to contribute significantly to the overall accessibility deficiency, even though they have convenient first-mile segments. These implications guide policymakers in prioritizing interventions for targeted groups or regions, and facilitating the creation of a more equitable transportation system.

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.