Abstract
Roadway safety in low-income and ethnically diverse U.S. communities has long been a major concern. This research was designed to address this issue by developing a data-driven approach and computational tools to quantify equity issues in roadway safety. This report employed data from Houston, Texas, to explore (1) the relationship between road infrastructure and communities’ socioeconomic and demographic characteristics and its association with traffic safety in low-income, ethnically diverse communities and (2) the type of driver behaviors and characteristics that affect crash risks in underserved communities. The team first built an inclusive road infrastructure inventory database by employing remote sensing and image processing techniques. Then, the relationship between communities’ socioeconomic and demographic characteristics and traffic safety was investigated through the lens of road infrastructure characteristics using data mining, deep learning tools, and statistical and econometric models. Clustering analysis was used to uncover the role in underserved communities of socioeconomic and demographic characteristics of drivers and victims involved in crashes. Structural equation models were then used to explore the association between neighborhood disadvantage, transportation infrastructure, and roadway crashes. Findings shed light on road safety inequity and sources of these disparities among communities using data-driven methods.
Project Highlights
- Investigating the causes of disparities in crashes, particularly involving pedestrian and bicyclists using data driven methods.
- Identifying potential data sources and collection methods to address multiple limitations present in equity research.
- Developing innovative methods and tools beyond traditional safety analysis to address the equity in safety.
- Proposing guidance and strategies to address the inequities present in safety outcome particularly involving vulnerable road users.
Final Report
EWD & T2 Products
Course of URSC 689 (Programming for Urban and Regional Analytics, Landscape and Urban Planning Department, TAMU) taught by team member Prof. Xinyue Ye leveraged the materials generated from the project to provide students with practical insights and skills in understanding, analyzing, and addressing the challenges of roadway safety in low-income and ethnically diverse communities. Course module is available here.
Student Impact Statement(pdf): Two students received funding under this project (Arwah Al-Kahtani, a summer intern from Texas A&M and Laura Barowski, a Masters’s student from VT). This file contains a statement by Laura Barowski as to the impact this project had on education and workforce development.
PhD thesis: Examining Environmental Justice in Roadway Safety Through AI-based Environmental Audit Approach.
Web story for dissemination (Under Development): https://experience.arcgis.com/experience/34212f929ed64676a54ad68cadfbf1a1/
Presentations/Publications
Zhu, C., Dadashova, B., Brown, C.T., Ye, X., Sohrabi, S. and Potts, I. (2023) Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm. 102nd Transportation Research Board Annual Meeting, Washington, DC, Jan 10th, 2023.
Zhu, C., Dadashova, B., Brown, C.T., Ye, X., Sohrabi, S. and Potts, I. (2022) Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm. 1st IACP Planning Research and Career Development Symposium, Gainesville, FL, Dec 21st, 2022.
Zhu, C., Dadashova, B., Brown, C.T. and Ye, X. (2024). Disparities in Roadway Safety: Exploring Direct and Indirect Pathways Contributing to Disparities in Non-Motorist Crashes in Houston, Texas. 103rd Transportation Research Board Annual Meeting, Washington, DC.
Zhu, C., Dadashova, B., Brown, C.T. and Ye, X. (2023). Disparities in Roadway Safety: Exploring Direct and Indirect Pathways Contributing to Disparities in Non-Motorist Crashes in Houston, Texas. Association of Collegiate Schools of Planning Annual Meeting 2023, Chicago, Illinois.
Zhu, C., Brown, C. T., Dadashova, B., Ye, X., Sohrabi, S., & Potts, I. (2023). Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm. Accident Analysis & Prevention, 182, 106964.
Thinking Transportation Podcast: Episode 38. Safety in Numbers? As bicycle use grows more popular, crash numbers carry mixed messages.https://tti.tamu.edu/thinking-transportation/episode-38-safety-in-numbers-as-bicycle-use-grows-more-popular-crash-numbers-carry-mixed-messages/ (relevant to the project)
Final Dataset
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/1X8SEF.
Research Investigators (PI*)
Bahar Dadashova (TTI/TAMU)*
Soheil Sohrabi (TTI/TAMU)
Xinyue Ye (TTI/TAMU)
Ingrid Potts (TTI/TAMU)
Charles Brown (Equitable Cities)
Chunwu Zhu (TTI/TAMU-Student)
Project Information
Start Date: 2021-09-01
End Date: 2023-05-31
Status: Completed
Grant Number: 69A3551747115
Total Funding: $320,000
Source Organization: Safe-D National UTC
Project Number: 06-001
Safe-D Theme Areas
Safe-D Application Areas
Planning for Safety
Risk Assessment
Vulnerable Users
Infrastructure Technology
More Information
Sponsor Organization
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Performing Organization
Texas A&M University
Texas A&M Transportation Institute
3135 TAMU
College Station, Texas 77843-3135
USA