Complete Trip: A Linked Multimodal Human Mobility Dataset

2026-07-16

AI Summary

Existing mobility datasets are often limited to isolated trip segments, hindering the modeling of multimodal continuous travel and population-level inference. This study leverages smartphone location-based service (LBS) data from six counties in Utah in 2020 and proposes a four-stage processing pipeline—trip detection, mode inference, network-level path reconstruction, and continuous trip linking—to integrate fragmented trajectories into complete multimodal travel episodes accurately represented on a digital transportation network. By incorporating statistical calibration techniques to generate expansion weights, the framework enables representative population-level analysis across travel modes, including car, bus, rail, and active transportation. The resulting dataset provides a reproducible, high-resolution foundation for multimodal human mobility research, with broad applications in transportation planning, public health, and urban science.

Abstract

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.

Problem

• human mobility
• multimodal transportation
• trip linking
• mobility dataset
• population-level inference

Innovation

• linked multimodal mobility
• trip linking
• route reconstruction
• mode imputation
• population-level inference