Combined Behavioral and Engineering Approach to Preventing Highway Fatalities

Blandford, Benjamin; Souleyrette, Reginald; Pope, Catlin; Fields, Tony; Lammers, Erin · 2022 · ROSA P / University of Kentucky Transportation Center

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Summary

This study addresses the persistent challenge of reducing highway fatalities in Kentucky, where traditional single-discipline approaches have failed to meet the goals set by the 2020 Strategic Highway Safety Plan. Motivated by the need for a more holistic safety strategy, the research investigates how engineering countermeasures can be co-integrated with behavioral interventions—specifically education, enforcement, and public outreach. The authors argue that crashes are not merely random errors but are influenced by latent societal conditions and proximate roadway factors. Consequently, the study aims to develop analytical methods that unpack the influence of behavioral-related factors on crash occurrences, focusing on four high-risk behaviors identified in the state’s safety plan: aggressive driving, distracted driving, impaired driving, and driving without proper restraint. The researchers employed a multidisciplinary methodology combining statistical modeling, geospatial analysis, and safety performance function (SPF) modeling. They analyzed a dataset of over 500,000 crashes in Kentucky between 2014 and 2018. The analysis incorporated crash data alongside roadway, vehicle, driver, locational, and socioeconomic characteristics derived from U.S. Census block groups. To identify at-risk populations, the study utilized Esri Tapestry® Segmentation data, a geodemographic tool typically used in marketing, to link crash outcomes with specific demographic and socioeconomic clusters. Additionally, the team developed network screening techniques using SPF modeling with Empirical Bayes adjustment to identify highway corridors with high excess expected crashes (EECs) for each behavioral type. The findings reveal that statistical models can effectively identify geodemographic groups most likely to engage in risk-taking driving behaviors, providing practitioners with targeted insights for educational and enforcement campaigns. The network screening results successfully identified specific roadway segments statewide and within Jefferson County where behavioral-related crashes were overrepresented. By isolating these high-risk corridors, the study demonstrated that engineering data can pinpoint locations where behavioral interventions are most needed. The analysis confirmed that latent conditions, such as socioeconomic status and demographic factors, significantly correlate with the likelihood of severe crashes involving the four targeted behaviors. The significance of this work lies in its provision of a replicable framework for integrating behavioral and engineering approaches to highway safety. By leveraging systems-based conceptual frameworks and interdisciplinary data sources, the study offers transportation practitioners tools to locate areas where behavioral crashes are problematic. This enables the design of focused countermeasures that target at-risk populations and specific roadway segments. The research supports the shift from isolated engineering fixes to a comprehensive "4 Es" approach (Education, EMS, Enforcement, Engineering), ultimately aiming to reduce fatalities by addressing the complex, compounding factors that influence driver behavior and crash outcomes.

Key finding

The study successfully demonstrated that combining statistical modeling of behavioral crash factors with geodemographic segmentation and network screening can effectively identify specific highway corridors and population segments in Kentucky where behavioral-related crashes are most prevalent.

Methodology

dataset

Sample size: 500000

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• demographic disparities
• incidence prevalence
• urban rural setting
• comparative international
• sex gender
• induced exposure

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: crash risk outcomes, observational prevalence
• Methodological Resource: dataset resource