Villanova Human Mobility Labs (NovaMobility Labs)
Our group builds datasets, models, and tools with transportation agencies and health researchers. Explore four featured projects below, or visit NovaMobility Labs for our team, research platforms, and opportunities.
FHWA · Mobility data
A linked travel dataset and interactive explorer covering approximately 28.8 million trip records across six Utah counties. Connect car, transit, and active travel to see the complete journey.
Dashboard by Ruohan Li et al.
FTA · Transit equity
Can tradable equity credits improve access to public transit? We study dynamic pricing and different traveler needs to explore equitable transit incentives and financing.
Led by Xin Wu and Kailun Liu.
Published in Transportation Research Part E, 2026.
NIH · Villanova × IBM Research Africa
Satellite imagery, urban form, and socioeconomic data help us understand surface heat inequity in Johannesburg–Ekurhuleni and evaluate equitable greening scenarios.
Led by Lele Zhang.
Research under review at Sustainable Cities and Society.
NIH U54 · INFORM Africa · DS-I Africa
Research in Nigeria and South Africa examines mobility patterns, interregional travel, and pandemic dynamics. Shared datasets and reproducible analyses support collaboration across transportation and public health.
Explore research at NovaMobility Labs → Collaborate or join the team →
The group led and partcipated in a number of high-profile research projects funded by federal and local agencies, including USDOT Federal Highway Administration, Federal Transit Administration, USDOE, National Science Foundation, National Institutes of Health, Maryland Department of Transportation, and Maryland DOT State Highway Administration.
The team receives funding to develop a software application for transit agencies to generate tradable credits from emission reductions and social equity improvements. These emission and equity credits will be integrated into a mobile app that uses real-time data to determine their credit and value. The project will incentivize individuals to use mass transit and ultimately provide transit agencies additional revenue through partnerships and innovative financing practices.
Under the unprecedented SARS-CoV-2 pandemic, a major mitigation effort has focused on non-pharmaceutical interventions, including masking requirements, shelter-in-place orders, border closures, school restrictions, and business interruptions. The effectiveness of these interventions for infection control depends on adherence as well as their specific impact: for instance, travel restrictions may reduce the geographic dispersion of infections but do not interrupt local transmission. Therefore, mobility-based analytics that link human movement patterns to spatiotemporal trends in SARS-CoV-2 are required to understand whether the interventions exhibit their intended effect. We aim to build a publicly accessible mobility data platform and to investigate the relationship between human movement patterns and SARS-CoV-2 transmission through three specific aims: (1) Fuse, integrate, and analyze passively collected mobile device locations and public health records for the two application areas: Nigeria and South Africa; (2) Design and reliably train an agent-based mobility model for multimodal and multi-dimensional travel behavior (i.e., across all modes, destinations, departure times, and routes) before and during the SARS-CoV-2 pandemic; and (3) Develop an epidemiological layer for the agent-based mobility model and empirically calibrate the model to spatiotemporal data of mobility and public health records.
This novel NIH Pillar-1 “Health Effects Research” investigates the influence of climate change and extreme weather events on SARS-CoV-2 related health outcomes. The study has the spatial and temporal focus on extreme weather events in Nigeria and South Africa, explicitly quantifying the direct health impacts from climate change on the weather-impacted areas, indirect impacts caused by induced human behavior changes and on different population groups, and the spillover influences on connected neighboring regions. The proposed study innovatively applies techniques from statistics, data sciences, computer science, and public health to deliver actionable results as well as data and new measurements to understand the health effects of extreme climate events on SARS-CoV-2 outcomes.