Advancing Large-Scale Traffic Safety Analysis with Sociodemographic and High-Resolution Probe Data

Traffic safety is a fundamental research theme in transportation engineering, involving influences and interactions among infrastructure, users, as well as transportation policy and management, which can be particularly complex from a perspective of large-scale transportation systems. In recent years, there has been an increased emphasis on adopting comprehensive and integrated approaches to traffic safety improvements, driven by growing evidence that crash risks disproportionately affect specific sociodemographic groups. Despite these advances, current methodologies for assessing disparities in traffic safety remain limited, due primarily to the reliance on historical crash data, which typically provide aggregated incident counts and classification without sufficient granularity and comprehensive temporal-spatial data availability. Leveraging emerging data sources provides a promising opportunity to facilitate current traffic safety improvement practices. Open-source sociodemographic data, such as income level, race, ethnicity, age distribution, and socioeconomic status, have become a critical component in traffic safety studies for understanding community-specific factors influencing traffic safety risks. The advent of high-resolution probe data (HRPD) offers extensive spatial coverage and detailed vehicle trajectory information, allowing for the identification of risky driving behaviors beyond what historical crash data alone can provide. This dissertation aims to explore the application of HRPD and detailed sociodemographic data in the development of quantitative metrics to assess and address the disproportionate distribution of crash risks across diverse communities. To achieve this objective, the dissertation comprises three interconnected research efforts: The first study introduces an innovative approach to crash disparity metrics development, which integrates historical crash data, sociodemographic characteristics, and various measures of traffic exposure. A Crash Gini index (CGI) and Theil Index of Crash (TIC) are developed to indicate the level of traffic safety disparity. This approach facilitates standardized comparisons of crash risk disparities across extensive geographical regions, thereby supporting data-driven policy decisions and enabling the evaluation of interventions for transportation systems. The subsequent two studies emphasize the integration of HRPD into large-scale traffic safety analyses. Specifically, the second study establishes connections between HRPD-derived driving behaviors and sociodemographic factors, highlighting variations in risky driving behaviors across communities. The third study implements an advanced geospatial analysis to demonstrate how HRPD can effectively function as a surrogate for current crash data in identifying and predicting traffic safety risks. Collectively, insights from these three studies contribute to a deeper understanding of the relationship between sociodemographic factors and traffic safety. Moreover, the dissertation showcases how HRPD can complement current traffic safety evaluation practices and methodologies, guiding policymakers and transportation professionals toward more comprehensive, effective, and user-driven safety strategies.

Level thesis:degree_level

Doctorate Degree

Year dc:date.issued

2025

Author dc:creator

• Feng, Suoyao

Advisor dc:contributor.advisor

• Park, Seri

Committee members dc:contributor.committeemember

• Xu, Hao
• Tian, Zong
• Feng, Jia
• Kelley, Scott

Language dc:language.iso

en_US, English

Repository record dc:identifier.uri

https://scholarwolf.unr.edu/handle/11714/11467

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/11467