Furthermore, the results show that active mode use is most sensitive to changes in the trip characteristics and the built environment. Cycling and walking should thus be regarded as two distinguished alternatives. In addition, no active mode nest was found in the model estimation. However, the choice for cycling or walking is affected by different determinants and to a different extent. The results show that all categories of determinants influence both walking and cycling. The determinants can be categorized as individual characteristics, household characteristics, season and weather characteristics, trip characteristics, built environment, and work conditions. Based on data from the Netherlands Mobility Panel (MPN) in combination with an additional survey focused on active modes (coined PAW-AM), this study estimates which determinants influence mode choice. walking, cycling, public transport and car). This paper estimates a mode choice model focusing on active modes, while including a more comprehensive set of modes (i.e. Furthermore, it can provide quantitative input for policies aiming at an active mode shift. Mode choice research from the Netherlands enables a comparison on relevant determinants with countries that have a low active mode share. The Netherlands is country with a large and demographically diverse active mode user population, mature and complete active mode infrastructure, and safe environment. To devise policies that promote this goal, understanding the determinants that influence the choice for an active mode is essential.
walking and cycling) has increased significantly over the past decades, with governments worldwide ultimately aiming for a modal shift towards active modes.
It finds that Census tracts with larger proportions of Black and Hispanic population tend to have significantly larger cost-distance ratios (i.e., slower speeds/lower potential mobility) for non-auto modes, while Census tracts with higher proportions of “creative class” employment and features of walkable built environments have significantly lower cost-distance ratios (i.e., faster speeds/higher potential mobility). Using this dynamic mode choice framework, this paper explores the features underlying observed structural heterogeneity in the ratio of cost to distance (i.e., speed or potential mobility) for observed flows across the city for each mode. This approach can be used to calculate dynamic modal cost-distance trade-offs for specific times, routes, and geographic areas of interest, providing a framework for creating aggregate mode choice profiles for individual cities and neighbourhoods that can be used to assess structural differences in transportation investment and mobility, as well as to test various assumptions about travel behaviour, observe temporal changes in modal trade-offs, and model the system-wide implications of changes to the transportation system to modal trade-offs. This paper develops a method to dynamically model urban passenger mode trade-offs at fine-grained spatial and temporal scales using data from OpenTripPlanner (OTP) and the City of Chicago’s Transportation Network Providers (TNP) dataset.