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  • Presentation | GC22B: Advancing Representation of Urban Processes and Dynamics in Models Across Scales II Oral
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  • GC22B-05: Complete Trip: A High-Resolution Multimodal Mobility Dataset for Urban Process Modeling and Environmental Simulation
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Author(s):
Ruohan Li, Villanova University (First Author)
Weiyu Luo, Villanova University (Presenting Author)
Xin Wu, Villanova University
Chenfeng Xiong, Villanova University


Understanding how people move through cities is essential for building safer, more sustainable, and more resilient communities. However, many cities lack detailed, real-world data on daily travel. This project introduces the Complete Trip dataset, which tracks how people traveled across six counties in Utah during the entire year of 2020, including changes in behavior caused by the COVID-19 pandemic. Using anonymous information from mobile devices, the dataset records more than 28 million trips by car, walking, biking, bus, rail, and air. It includes not only when and how people traveled, but also their routes, travel speeds, and estimated purposes of travel, such as commuting. It also provides a general picture of the types of people traveling, including age and income groups. Because the data is connected to actual roads and transit systems, it can be used to support research on air pollution, extreme heat exposure, and how well different communities can reach jobs or services. The dataset also helps scientists understand how cities respond to disasters or disruptions. Overall, it offers a powerful tool for improving transportation systems, public health, and climate resilience in urban areas.



Scientific Discipline
Neighborhood
Type
Main Session
Discussion