New model reveals how cities can predict commute patterns by lifestyle, not just demographics
Researchers have developed a way to predict how people will travel and schedule their days by identifying hidden lifestyle groups—car-dependent, transit-reliant, bike-focused, or multimodal commuters. The finding could help cities and transport planners design infrastructure and services that actually match how different populations move, rather than relying on broad demographic assumptions.
Originaltitel: A latent class dynamic discrete choice model for travel behaviour and scheduling
<p>In travel behaviour modelling, latent class models are used to represent underlying discrete groupings of behavioural preferences. The paper presents a latent class extension of a dynamic discrete choice model (DDCM) and applies the model to the problem of activity demand generation and scheduling. The DDCM is a recursive multinomial logit model where agents make sequential decisions in time, maximizing the expected future utility of their decisions in a random utility maximization framework. It generates activities and their associated travel within a full day schedule, endogenously respecting agents' inherent time-space constraints. The latent class DDCM builds on the base model by representing heterogeneous lifestyle preferences. A specification of the model is estimated on a Stockholm travel survey and uses age, income level, gender, car ownership and presence of children in the household as classifying variables. The models result in classes which primarily represent modality styles, finding car-, transit- and bike-primary behavioural groups as well as a multimodal group, each linked with different socio-demographic characteristics. The models improve over non-latent class reference models and provide insight into the structure of heterogeneity in travel behaviour preferences in Stockholm.</p>