AI 中文总结
该研究提出完整行程数据集，通过四阶段工作流程从LBS数据重建多模式旅行行为，涵盖犹他州六个县。能保留行程关系、提供路线表示并支持人口级分析，推动交通、公共卫生等多领域可重复研究。
AI 中文摘要
人类移动数据对交通、公共卫生、城市科学和灾害恢复力等研究至关重要。但现有移动数据集通常只捕捉旅行行为的孤立方面，很少提供链接的多模式行程以及网络级路线表示和人口级推断。本文提出完整行程数据集，它从被动收集的基于智能手机位置服务（LBS）数据中重建链接的多模式旅行行为。首个发布版本涵盖2020年犹他州六个县，通过行程识别、模式插补、路线重建和行程链接四阶段工作流程呈现汽车、公交、铁路和主动交通的行程。它通过链接同属一个旅行事件的连续旅行段保留行程级关系，在数字交通网络上提供基于网络的路线表示，并通过统计校准的扩展权重支持人口级分析，能促进多领域可重复研究。
英文摘要
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.
Comments16 pages, 9 figures, 5 tables
