Learning Mobility Insecurity from Location Intelligent Data
Funded by CCTA, 2024 – 2025. Joint with Alex Murphy from University of Michigan and Ying Chen from Northwestern University.
Abstract
The project aims to develop a novel, scalable individual-level measure to capture a critical dimension of transportation insecurity (TI): the irregularity of travel. Utilizing location intelligence data, the research will focus on extracting mobility irregularity (MI) information, broadly defined by variations in spatial and temporal trip-making patterns. The core hypothesis of the project is the strong correlation between MI and TI, positing that a detailed understanding of MI could serve as a key predictor for broader transportation insecurity. To systematically quantify MI, the project proposes the creation of the Mobility Regularity Index (MRI), a composite metric encapsulating diverse trip attributes such as frequency, purpose, duration, and mode. The project promises to offer a deeper insight into the nuances of transportation challenges faced by various communities, thereby contributing to more equitable and effective transportation planning and policymaking.
Related research outputs.
A presentation given at the 2025 Annual Meeting of Transportation Research Board, Washington DC.
A journal article currently under revision at Nature Communication
A journal article currently under revision at Transportation Research Part A