Political and societal attention on transport inequality, transport vulnerabilities and transport poverty risk has been increasing the last five years. The Dutch government has committed multiple large sums of resources to abate the issue, 120 million euros to cancel the increase in price of train tickets, 300 million to cancel the increase in price and preserve the provision of the regional public transit and 6 million to start a pilot of ‘innovative public transport’ in the Zeeland region. A review of the literature on transport poverty risk reveals that past studies analyze the concept using a limited number of pre-selected variables. For example, car ownership and residence area. This carries the risk for misinterpretation as an accurate representation can only be achieved by considering all relevant variables. Such a comprehensive analysis could allow policymakers to formulate tailor-made policies according to the specific problems and needs of certain groups in society. This study aims to include the entire complexity of the concept of transport poverty risk in the analysis. Relevant factors include affordability, accessibility and mobility. Then there are individual differences, and for every individual changing over time, between needs of participating in society and social life. This study aims to incorporate this comprehensive definition in a Latent Class Analysis approach to construct groups of individuals based on the MPN and ODiN national datasets. The different groups of individuals can be then used to indicate the effect of policies on transport inequality. Results show three groups that are most transport ‘fortunate’, with high income, high car ownership, high accessibility and the potential to use different travel modes. Results also show one group with substantial transport vulnerabilities due to higher car dependency, lower car ownership and lower household income. A policy thought experiment is established through the application of mobility policy packages of three political parties. Car policies that reduce costs per driven kilometer such as reduction in excise tax will increase transport inequality due to the three groups that are the most transport ‘fortunate’, which also have the highest car use. This study provides the scientific community with a comprehensive definition and conceptual model of transport poverty risk and for policymakers contributes a comprehensive understanding of transport inequality in the Netherlands. Future research needs to focus on identifying specific minority groups that are not or less captured in the national datasets. Transport problems are experiences by an individual, while transport inequality is represented by the differences in the extent of transport problems between certain individuals or groups. In this case, certain groups in the Netherlands. This calls for the need to differentiate the government levels at which transport inequality and transport problems are addressed. While this study presents the transport inequality at the national level, municipalities will be able to identify specific individuals and neighborhoods who are more prone to transport problems.