# Built Environment, Pedestrian Injuries and Deep Learning (BEPIDL) Study

> **NIH NIH K01** · DREXEL UNIVERSITY · 2021 · $138,024

## Abstract

PROJECT SUMMARY
Road traffic injuries are a major contributor to the burden of disease globally with nearly 1.3 million deaths
globally and as many as 50 million injured annually with pedestrians and cyclists in low and middle-income
countries (LMICs) among the most affected. Road infrastructure of the built environment (e.g., sidewalks),
neighborhood design (e.g., street connectivity) and urban development (e.g., urban sprawl) are key
determinants of the risk of pedestrian injuries. In LMICs, poor road infrastructure and neighborhood design are
acknowledged as being important contributors to rising numbers of road traffic injuries and deaths, but there
are few studies systematically identifying and quantifying what specific features of the built environment are
contributing to motor vehicle collisions in these settings. Within LMIC cities, there are often large disparities
where infrastructure is improved that reflect socioeconomic characteristics, leading to health inequities in road
traffic injury. The paucity of georeferenced data on the built environment in LMICs has made research on road
traffic injuries more difficult, though recent advances in computer vision and image analysis combined with Big
Data of publicly available, georeferenced, images of roads worldwide (e.g., Google Street View, GSV) can help
overcome the paucity of data and the cost and time limitations of collecting and analyzing data on the built
environment in LMICs. Automated image analysis has largely been made possible via deep learning, a subfield
of artificial intelligence and machine learning and relies on training neural networks to detect and label specific
objects within images. These methods can drastically reduce the barriers to citywide built environment and
traffic safety research in LMIC cities, thus substantially increasing research capacity and generalizability. My
career goal is to become an independent investigator in global urban health with a focus on road safety and
the built environment in LMICs. I propose undertaking research and training in deep learning methods applied
to public health in the setting of Bogota, Colombia: 1) Develop neural networks to create a database of BE
features of the road infrastructure from image data and to create neighborhood typologies from those features;
2) Assess the association between neighborhood-level BE features and typologies and pedestrian collisions
and fatalities and road safety perceptions; 3) Assess the association of neighborhood social environment
characteristics with pedestrian collision and fatalities, perceptions, and BE features and typologies. I am
seeking additional training in 1) developing competency in deep learning methods applied to public health; 2)
creating neighborhood indictors and typologies of health and the built environment; 3) applying Bayesian
spatiotemporal models to understand how neighborhood characteristics and typologies influence health; 4)
develop skills in multi-country collabor...

## Key facts

- **NIH application ID:** 10264065
- **Project number:** 5K01TW011782-02
- **Recipient organization:** DREXEL UNIVERSITY
- **Principal Investigator:** Duane Alexander Quistberg
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $138,024
- **Award type:** 5
- **Project period:** 2020-09-18 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10264065

## Citation

> US National Institutes of Health, RePORTER application 10264065, Built Environment, Pedestrian Injuries and Deep Learning (BEPIDL) Study (5K01TW011782-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10264065. Licensed CC0.

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