About

Huyen Le is an Associate Professor in the Department of Geography at The Ohio State University. She holds a PhD in Planning from Virginia Tech, an MS in Planning from The University of Iowa, and a BEng in Urban Construction Management from Hanoi Architectural University. Her research lies at the intersection of transportation, environment, health, and well-being, focusing on managing and modeling urban transportation demand, the impacts of information and communications technology on transportation and environmental outcomes, and the physical and mental health outcomes resulting from daily travel and activities. Her work explores how transportation systems influence emotional and physical health, energy efficiency, and environmental sustainability, contributing valuable insights into urban planning and transportation policy.

Research topics

• Sociology
• Economics
• Political Science
• Geography
• Business
• Demography
• Transport engineering
• Computer Science
• Socioeconomics
• Engineering
• Medicine
• Economic growth
• Marketing
• Psychology
• Advertising
• Cartography
• Demographic economics
• Finance
• Environmental health
• Gerontology
• Civil engineering
• Microeconomics

Selected publications

• Smartphone habits are stronger in spaces chosen out of habit

  Scientific Reports · 2025-11-21 · 1 citations

  articleOpen access

  Habits are a fundamental driver of human behavior. However, prior research has largely studied habits in isolation. This study investigates spatial and smartphone habits-two cornerstone habits in contemporary society-in tandem, revealing the multiplicative ways in which habitual processes can underlie human behavior. We provide evidence that smartphone habits are stronger in habitually traveled and visited spaces. We leverage a custom-designed app that logged mobility and app use while prompting map-based questionnaires of participants' trips for two weeks (N = 419 participants; n = 27,446,977 GPS and app logs; n = 7,226 trip questionnaires). Overall, app habit strength predicted higher likelihood of app use (vs. non-use) and more app use (when used) on more habitually travelled routes and in more habitually visited destinations. The interaction between spatial and smartphone habits was most consistent for objective (vs. subjective) habit indicators. Social app habits were stronger and more context-independent than non-social app habits. Altogether, our findings reveal how distinct habitual processes can combine to shape daily behavior. Further, our method illustrates the potential of linking mobile sensing and surveying to measure habits in the real world.

  PublisherDOI

• Just transition to electric vehicles in disadvantaged communities: Integrating transportation, energy, environmental, and climate justice

  Energy Research & Social Science · 2025-11-20

  articleSenior authorCorresponding
  PublisherDOI

• Changes in the predictors of transit ridership in post-COVID-19 US metropolitan areas

  Travel Behaviour and Society · 2025-02-18 · 5 citations

  article
  PublisherDOI

• Heat and noise exposure during active travel: a systematic review

  Transport Reviews · 2025-10-25 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• Data aggregation impacts on built environment-mode share models around public transit stations

  Journal of Transport and Land Use · 2025-05-19 · 2 citations

  articleOpen access

  This study examines how data aggregation influences the relationship between the built environment (BE) and mode share around 2,794 rail and BRT stations in the United States, using both inferential and machine learning methods. The results indicate that data aggregation impacts the outcomes of BE-mode share models, regardless of the data analysis approach. Models using network buffers are less affected by data aggregation compared to those using circular buffers, Thiessen polygons, or administrative boundaries (block groups). In addition, the optimal buffer sizes for capturing BE effects and minimizing sensitivity to data aggregation for active and public transit modes are 800 meters for BRT stations and 1000 meters for rail stations, while 1200 meters is effective for private vehicle mode share at both rail and BRT stations. Furthermore, key BE features in commuting mode share models—such as employment density, jobs per household, intersection density, residential density, distance from the central business district, job accessibility (active), and regional population density—remain robust against data aggregation. We recommend that urban and transportation planners account for aggregation biases and apply multiple methods when evaluating BE's impact on mode share around public transit stations to inform more effective policy recommendations.

  PublisherOA PDFDOI

• Impacts of bicycle facilities on residential property values in 11 US cities

  Journal of Transport Geography · 2025-02-01 · 2 citations

  articleOpen accessSenior authorCorresponding

  Bicycle infrastructure has been found to increase nearby residential property values. However, most evidence for this economic impact is limited to a single city. This study investigates the pre- and post-treatment effects of different types of bicycle facilities on the values of single-family and multifamily homes in 11 cities in the United States from 2000 to 2019. We utilize a quasi-experimental approach with matching techniques and hedonic models to track down the changes in the sales price of residential properties over time within an 800-m buffer of bicycle facilities. We found a mixed impact of property value appreciation, depreciation, and no change in the sales price by different types of bicycle infrastructure including on-street and off-street facilities on single-family and multifamily residential properties across the 11 cities. Single-family and multifamily properties near off-street-only facilities experienced appreciation in Los Angeles, Minneapolis, and Cleveland. Meanwhile, single-family homes near on-street-only facilities tended to decrease their values in Columbus, Eugene, Philadelphia, and Tucson, and increase only in Minneapolis. All properties within 800 m of both on-street and off-street facilities saw their values increase in Columbus and Minneapolis. However, we did not find a statistically significant effect of bicycle infrastructure on housing values in Portland, San Francisco, and Seattle. Findings from our study will inform decision-making and planning for bicycle infrastructure while ensuring the equitable distribution of these facilities and affordable housing for disadvantaged populations. • This quasi-experimental study quantifies the impacts of bike facilities on property values. • We analyzed data from 11 US cities from 2000 to 2019. • We found a mix of increase, decrease, and null effects on property values near on-street and off-street facilities. • Negative impact was more prevalent for single-family properties than multifamily properties.

  PublisherDOI

• SarcNet: A Novel AI-Based Framework to Automatically Analyze and Score Sarcomere Organizations in Fluorescently Tagged HIPSC-CMS

  2025-04-14 · 2 citations

  article1st authorCorresponding

  Quantifying sarcomere structure organization in human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is crucial for understanding cardiac disease pathology, improving drug screening, and advancing regenerative medicine. Traditional methods, such as manual annotation and Fourier transform analysis, are labor-intensive, error-prone, and lack high-throughput capabilities. This paper proposes SarcNet, a novel deep learning-based framework that leverages cell images and integrates cell features to automatically evaluate the sarcomere structure of hiPSC-CMs from the onset of differentiation. The proposed framework is a cell-features concatenated and linear layers-added ResNet-18 module, to output a continuous score ranging from 1 to 5 that captures the level of sarcomere structural organization. SarcNet achieves a Spearman correlation of 0.831 with expert evaluations, demonstrating superior performance and an improvement of 7.5% over the previous approach, which uses linear regression. Our results also show a consistent pattern of increasing organization from day 18 to day 32 of differentiation, aligning with expert evaluations. This study contributes to the development of a new automated tool for quantifying sarcomere structural organization in hiPSC-CMs, advancing cardiac research.

  PublisherDOI

• ML/AI, Data Science, and Exposure Science: Lessons Learned from Engagement with Communities and Public Health/Policy Planning for the Future

  Annals of Epidemiology · 2024-09-01

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  PublisherDOI

• RxNorm for drug name normalization: a case study of prescription opioids in the FDA adverse events reporting system

  Frontiers in Bioinformatics · 2024-01-05 · 8 citations

  articleOpen access1st author

  Numerous studies have been conducted on the US Food and Drug Administration (FDA) Adverse Events Reporting System (FAERS) database to assess post-marketing reporting rates for drug safety review and risk assessment. However, the drug names in the adverse event (AE) reports from FAERS were heterogeneous due to a lack of uniformity of information submitted mandatorily by pharmaceutical companies and voluntarily by patients, healthcare professionals, and the public. Studies using FAERS and other spontaneous reporting AEs database without drug name normalization may encounter incomplete collection of AE reports from non-standard drug names and the accuracies of the results might be impacted. In this study, we demonstrated applicability of RxNorm, developed by the National Library of Medicine, for drug name normalization in FAERS. Using prescription opioids as a case study, we used RxNorm application program interface (API) to map all FDA-approved prescription opioids described in FAERS AE reports to their equivalent RxNorm Concept Unique Identifiers (RxCUIs) and RxNorm names. The different names of the opioids were then extracted, and their usage frequencies were calculated in collection of more than 14.9 million AE reports for 13 FDA-approved prescription opioid classes, reported over 17 years. The results showed that a significant number of different names were consistently used for opioids in FAERS reports, with 2,086 different names (out of 7,892) used at least three times and 842 different names used at least ten times for each of the 92 RxNorm names of FDA-approved opioids. Our method of using RxNorm API mapping was confirmed to be efficient and accurate and capable of reducing the heterogeneity of prescription opioid names significantly in the AE reports in FAERS; meanwhile, it is expected to have a broad application to different sets of drug names from any database where drug names are diverse and unnormalized. It is expected to be able to automatically standardize and link different representations of the same drugs to build an intact and high-quality database for diverse research, particularly postmarketing data analysis in pharmacovigilance initiatives.

  PublisherOA PDFDOI

• Built environment’s nonlinear effects on mode shares around BRT and rail stations

  Transportation Research Part D Transport and Environment · 2024-02-27 · 22 citations

  article
  PublisherDOI

Frequent coauthors

• Huixiao Hong

  22 shared

• Wen Zou

  National University of Defense Technology

  22 shared

• Weizhong Zhao

  21 shared

• Weigong Ge

  National Center for Toxicological Research

  18 shared

• Weida Tong

  16 shared

• Henry Francis

  15 shared

• Steve Hankey

  Virginia Tech

  15 shared

• Beverly Lyn‐Cook

  National Center for Toxicological Research

  13 shared

Education

• PhD, Transportation Planning

  Virginia Tech

  2019