Active commuting provides routine-based regular physical activity which can reduce the risk of chronic diseases. Using public transportation involves some walking or cycling to a transit stop, transfers and a walk to the end location and users of public transportation have been found to accumulate more moderate physical activity than non-users. Understanding how public transportation characteristics are associated with active transportation is thus important from a public health perspective. This study examines the associations between objective measures of access to public transportation and self-reported active commuting. Self-reported time spent either walking or cycling commuting each day and the distance to workplace were obtained for adults aged 16 to 65 in the Danish National Health Survey 2010 (n = 28,928). Access to public transportation measures were computed by combining GIS-based road network distances from home address to public transit stops an integrating their service level. Multilevel logistic regression was used to examine the association between access to public transportation measures and active commuting. Distance to bus stop, density of bus stops, and number of transport modes were all positively associated with being an active commuter and with meeting recommendations of physical activity. No significant association was found between bus services at the nearest stop and active commuting. The results highlight the importance of including detailed measurements of access to public transit in order to identify the characteristics that facilitate the use of public transportation and active commuting.

The increased availability of transit schedules from web sites or travel planners as well as more disaggregate data has led to a growing interest in creating individual public transportation accessibility measures. However, used extensively, standard GIS software does not have direct capabilities to integrate transit schedules into multimodal networks and measure space–time-based accessibility. This has caused authors to either simplify travel time elements or develop tools to overcome these challenges. In this paper we aim to describe and implement a method that enables integrating time-table data from a travel planner into a multimodal network model using simple SQL (structured query language) programming and standard GIS. The method presented here integrates all parts of travelling by public transportation from individual home addresses to all reachable transit stops within different travel time thresholds. The method is used successfully to create a multimodal travel-time network model of the Capital Region of Denmark comprising bus, train, light rail, metro, and ferry as well as integrating walking or cycling to stops. Here, the individual accessibility is defined as accessibility areas. The accessibility areas are created at morning rush hour for a study population of 29 447 individuals and a few examples of accessibility areas are presented. The results show a big difference in individual public transportation accessibility in the region. In addition, how the transit network is accessed, whether it is at the nearest stop or at all stops within 1 km walking distance or 3 km cycling distance, leads to very different accessibility areas.