Complete Trip: A Linked Multimodal Human Mobility Dataset
For researchers in transportation and urban science, this dataset addresses the lack of linked multimodal mobility data, but it is an incremental contribution as it applies existing methods to a new dataset.
This paper introduces Complete Trip, a dataset that reconstructs linked multimodal travel behavior from smartphone location data, covering six counties in Utah for 2020. It provides journey-level linkages, network-based routes, and population-level weights, enabling research across transportation, public health, and urban science.
Human mobility data have become fundamental to research across transportation, public health, urban science, and disaster resilience. However, existing mobility datasets typically capture only isolated aspects of travel behavior and rarely provide linked multimodal journeys together with network-level route representations and population-level inference. Here we present Complete Trip, a mobility dataset that reconstructs linked multimodal travel behavior from passively collected smartphone location-based services (LBS) data. The first released implementation covers six counties in Utah throughout 2020 and represents journeys across car, bus, rail, and active transportation through a four-stage workflow consisting of trip identification, mode imputation, route reconstruction, and trip linking. Complete Trip preserves journey-level relationships by linking sequential travel segments where multiple segments belong to the same travel episode, provides network-based route representations on digital transportation networks, and supports population-level analyses through statistically calibrated expansion weights. By providing a representation of linked multimodal human mobility, Complete Trip enables reproducible research across transportation, public health, urban science, disaster resilience, and related fields.