Intermodality or combining more than one transport mode during a single trip has been put forward to facilitate a modal shift from private car to more environmentally friendly modes such as public transport, cycling or walking. Bike-and-ride – that is, integrating cycling and public transport in one trip – is an attractive combination, as cycling as an active and clean mode is faster than walking and more affordable and flexible than other alternative modes of transport. Using cycling as a feeder mode to public transport could potentially allow people to reach more opportunities and improve their mobility, and ultimately, their well-being. Therefore, it is relevant to investigate the inequalities in access to bike-and-ride options across population groups. In this context, we suggest assessing the inequalities in bicycle access to the main transport hubs of a city by developing a composite indicator based on accessibility measures and the Theil index of inequality. This indicator captures the role of both private and public bikes – part of a Bike Sharing System (BSS) – in accessing the existing public transport system. The novelty of our approach lies in bringing the distributional justice perspective in the accessibility evaluation of transport and analysing the inequalities within and between any arbitrarily defined population groups. Moreover, in addition to travel time by bike, this accessibility measure incorporates a series of bike-related features, such as the typology of bike lanes (separated from or shared with roads), the presence of a BSS in the network, and bike facilities (e.g., parking racks) in transport hubs. The proposed methodology is applied to a real case study of the city of Malmö, Sweden, to prove its efficacy and usefulness. In particular, we examine how the level of bicycle access to the major public transport destination (including train stations and regional bus hubs) varies across the population. While considering the contextual properties of the city of Malmö, the inequalities are analysed in relation to spatial dimension and social background of the population, it is possible to extend the proposed analysis by including further features of the population, such as income or gender, and apply the same approach to different contexts.