Latent classes of daily mobility patterns: The relationship with attitudes towards modes
In the past decade, public interest into active modes (i.e. walking and cycling) has increased significantly. Consequently, governments worldwide are aiming for a modal shift from motorised to active modes and thus increased use of active modes in individuals’ daily mobility patterns. Attitudes are considered important predictors of human behaviour. As a result, understanding the relationship between the attitude towards modes and mode choice behaviour has received a lot of attention in scientific research. Generally, findings suggest that if an individual is positive towards a mode, the probability of choosing that mode increases.
Research on mode choice is often trip-based, thus focusing on the modal choice for individual trips. However, over the course of a day most individuals make multiple trips and use multiple modes. Therefore, traditional mode choice research covers only a fraction of the complete mobility pattern. This study aims to bridge this gap, by investigating the relationship between the daily mobility pattern of an individual and their attitude towards modes, which requires taking into account the entire set of modes.
For this research the data of the Mobility Panel Netherlands (MPN) of the year 2016 is used, that contains mobility patterns of individuals. A companion survey on perceptions, attitudes and wayfinding styles towards active modes (coined PAW-AM) was distributed among the respondents of the MPN panel, to collect more data on the attitude towards modes.
The relationship between attitudes towards modes and mobility patterns is investigated by applying a latent class analysis on daily mobility patterns to identify different classes. Next, the relationship between these latent classes and the attitudes towards modes is investigated. We find that generally the combination of modes that is used relates to a more positive attitude towards these modes compared to the modes that are not in the individuals’ mobility pattern.
Danique Ton, Delft University of Technology