Recursive logit models for dynamic versus sequential trip chaining

This paper applies recursive logit (RL) to model activity-trip chaining behaviour. We present a comparison between two approaches to applying the RL model in this context. In the first ‘sequential’ approach, agents form a trip chain by making a sequence of joint choices of activity location (i.e. trip destination) and travel mode, ending the chain by choosing to return home. The second ‘dynamic’ approach adds a time variable. Its agents form a full-day activity/travel schedule by making a sequence of choices either to continue the current activity for a fixed timestep or make a joint choice of new activity location and travel mode. We estimate parameters for both models using data from a Stockholm travel survey and validate model simulations against observed data. The models reproduce patterns of observed behaviour beyond their estimated parameters, including different types of trip chains and the spatial distribution of activities. While the dynamic model is advantageous in its ability to predict agent schedules, reflect time-varying travel conditions and endogenously represent space–time constraints, it does not surpass the simpler sequential model on mutual areas of trip chaining behaviour. We conclude that the RL model is well-suited to model trip chaining behaviour, and that the simpler sequential approach may be appropriate for many modelling purposes.