Rural Mobility and Access: Leveraging Big Data Analytics and Context-Aware Computing

Abstract

This seed project investigates rural mobility and accessibility in Minnesota using mobile phone data and context-aware analytics. Traditional travel surveys, though detailed, often underrepresent rural populations and lack long-term, large-scale coverage. To address these gaps, this study leverages anonymized mobile phone data, in combination with land use and census datasets, to analyze the spatial behavior of rural, suburban, and urban residents. The project develops data-driven methods to classify users into behavior-based subgroups and identify key, group-specific routine activity locations such as home and workplace. Using the inferred home and work locations, the study evaluates the representativeness of the mobile phone sample and finds relatively high coverage in rural areas. Mobility indicators are then mapped and summarized, revealing distinct mobility patterns of rural residents - characterized by more dispersed activity spaces, less structured work schedules, and longer distances to frequent destinations. These findings underscore the value of mobile phone data in complementing travel surveys and offering a representative view of rural mobility and accessibility. In addition to methodological contributions, the project introduces an interactive mapping tool that allow users to visualize mobility flows across geographic scales, from statewide patterns to individual census tracts. These tools provide actionable insights for planners, researchers, and policymakers seeking to develop informed transportation strategies and investments to promote mobility and accessibility in rural communities.