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

Principal Investigator(s):

Ying Song, Associate Professor, Geography


  • Di Zhu, Assistant Professor, Geography

Project summary:

Access to transportation plays a key role in rural communities and addresses the needs of rural residents to access jobs, education, healthcare, recreation, and other activities of daily life. Recent projects such as Rural Community Transit Strategies and Funding Shared Mobility as an extension of existing public transit services have identified the special transportation needs and challenges of rural communities in Minnesota and explored strategies and funding sources for implementing new or strengthening current shared mobility services.

To ensure the success of the proposed strategies, it would be crucial to gain a comprehensive view of the current mobility and accessibility of rural residents. Recent studies show the feasibility of using mobile phone data to study human mobility, but most studies focus on aggregated measures such as origin-destination (OD) flows, vehicle miles traveled (VMT), and traffic volumes. These trip-based metrics may not adequately address the unique geographic contexts in rural areas and the inherent associations among activities and trips in everyday task scheduling. Also, discussions on how representative the data is often adopt a simple weighting schema but do not sufficiently capture the spatial heterogeneity.

This project aims to address the above gaps by integrating geographic contexts into the analysis of mobile phone data and the development and interpretation of mobility and accessibility. Methods and findings from this project can support future projects on urban transportation developments.

Project details: