Safety in Numbers: A Literature Review

Kehoe, N. P.; Goughnour, E; Jackson, S; Sykes, K; Miller, S.; Blackburn, L · 2022 · ROSA P / United States. Department of Transportation. National Highway Traffic Safety Administration

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Summary

This literature review, conducted by the National Highway Traffic Safety Administration (NHTSA), examines the "Safety in Numbers" (SIN) theory, which posits an inverse relationship between the volume of pedestrians and bicyclists and their individual risk of collision with motor vehicles. The research was motivated by rising fatalities among vulnerable road users and the need to understand whether increasing non-motorized travel volumes inherently improves safety, thereby informing policy and infrastructure decisions. The review synthesizes multidisciplinary research from engineering, planning, sociology, psychology, and public health to provide a comprehensive overview of the SIN concept’s development, validity, and implications for practitioners. The methodology involved a systematic search of domestic and international sources, utilizing databases such as TRID, Google Scholar, and the American Journal of Public Health. The authors critically reviewed 250 documents, focusing on studies from the past 15 years as well as foundational works. The review categorized sources by field of study, location, and topic area, including infrastructure, behavioral factors, and statistical modeling. A subset of highly rated sources underwent further statistical assessment to evaluate the robustness of the methods and data used in SIN-related research. The findings trace the evolution of SIN from early crash prediction models in the 1990s to Jacobsen’s (2003) foundational study, which coined the term and calculated that crash numbers increase at roughly the 0.4 power of pedestrian/bicyclist volume increases. While many subsequent studies support the SIN effect, others question its validity or fail to observe it, such as a 2017 study in Melbourne. The review identifies significant methodological weaknesses in the literature, particularly regarding data limitations. Exposure data (volume counts) are often scarce and resource-intensive to collect, while safety data suffer from underreporting in police crash records. Additionally, many studies lack explanatory variables regarding the built environment or human behavior, such as driver distraction. Statistical analyses frequently employ generalized linear models, specifically negative binomial models, to handle overdispersion in crash count data. The significance of this review lies in its clarification of the SIN theory’s practical application. The authors conclude that while SIN suggests a reduced individual risk as volumes rise, total crashes and injuries will still increase as more users enter the system. Therefore, relying solely on SIN to justify policies promoting walking and biking is insufficient. The review advocates for a multi-pronged approach that includes education, enforcement, and infrastructure improvements alongside volume increases. It highlights a gap between academic consensus and practical application, noting that practitioners often lack the detailed explanatory factors needed to integrate SIN into planning. The report underscores the need for better data collection and further research into behavioral and environmental mechanisms to fully understand and leverage the SIN effect for improving pedestrian and bicyclist safety.

Key finding

The literature review confirms that the Safety in Numbers theory generally holds that increased pedestrian and bicyclist volumes correlate with a decreased per-capita risk of crashes, although the exact causal mechanisms remain unclear and methodological issues persist in the research.

Methodology

review

Provenance

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Topics

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

• cyclist safety
• incidence prevalence
• vru crash typology
• comparative international
• induced exposure
• demographic disparities

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