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Untangling the Growing Pedestrian Safety Problem on Urban Arterials

Project Description

The safety literature identifies urban arterials as a leading contributor to the rise in pedestrian fatalities in the United States, among other factors. These arterials are linked to pedestrian crashes on wide, straight roads with high traffic speeds and volume. Previous studies have also shown that most pedestrian deaths occur at midblock locations on high-speed roads during nighttime, which are common features of pedestrian crashes on arterials. The definition of arterial provided by the United States Department of Transportation (USDOT) based on functional classification raises concerns as it may underestimate the severity of pedestrian crashes on roads with similar design features to urban arterials but not classified as such.


This research will investigate the relationship between crashes on urban arterials and their relationship to areas with high pedestrian activity, such as low-income neighborhoods and public transit stops. It will also delve into factors like driver distractions and fatigue caused by monotonous driving on urban arterials, vehicle speed and volume, and the effects of suburbanization of poverty and gentrification. By utilizing pedestrian crash data, including records of injury severity from California, Tennessee, and Wisconsin, the study will differentiate between crashes with a high likelihood of being fatal—such as those on arterials or comparable roads—and relatively less severe incidents like parking lot collisions or low-speed neighborhood crashes. This distinction will involve the use of machine learning methods and intuitive categorizations.


Following this, the study plans to integrate the crash data with census attributes, police narratives, and other geographical data sources to gain insights into changes in population, land-use patterns, and vehicle-related details. In doing so, the research will effectively address the core research questions. Overall, this study intends to identify the most hazardous forms of pedestrian crashes and understand their characteristics.

Outputs

This study will contribute notably by proposing clustering the most hazardous pedestrian crashes, particularly those on urban arterials or comparable roads. The study aims to create a novel classification method using relevant machine-learning techniques to identify these accidents accurately. Additionally, it seeks to devise an intuitive classification system based on specific road characteristics.

 

Among the significant outcomes anticipated from this research is an improved understanding of the fundamental mechanisms behind these crashes. It will include an in-depth exploration of the contributing factors, the dynamics of occurrence, the demographics of the victims, and an examination of how the built environment and road design influence these crashes' potentially fatal outcomes. Finally, guided by a thorough analysis of associated risk factors and grounded in Safe Systems principles, the study intends to offer a set of effective countermeasures. These measures are envisioned to mitigate such crashes within US suburbs, thereby reducing the severity of their consequences.

Outputs

This study will contribute notably by proposing clustering the most hazardous pedestrian crashes, particularly those on urban arterials or comparable roads. The study aims to create a novel classification method using relevant machine-learning techniques to identify these accidents accurately. Additionally, it seeks to devise an intuitive classification system based on specific road characteristics.

 

Among the significant outcomes anticipated from this research is an improved understanding of the fundamental mechanisms behind these crashes. It will include an in-depth exploration of the contributing factors, the dynamics of occurrence, the demographics of the victims, and an examination of how the built environment and road design influence these crashes' potentially fatal outcomes. Finally, guided by a thorough analysis of associated risk factors and grounded in Safe Systems principles, the study intends to offer a set of effective countermeasures. These measures are envisioned to mitigate such crashes within US suburbs, thereby reducing the severity of their consequences.

Outcomes/Impacts

By providing a precise classification system tailored to crashes on urban arterials and similar roadways, this research will equip safety researchers and planners with a targeted approach to addressing pedestrian safety. This strategic tool will help prioritize high-risk roads, enabling authorities to allocate resources and interventions more effectively. The potential impact is twofold: not only does it empower authorities to address worst-case scenarios promptly, but it also fosters public awareness regarding these hazardous environments. The benefits extend to adopting countermeasures, where options range from immediate tactical adjustments to more comprehensive measures such as road diets or even complete roadway redesigns.

 

Gaining insights into the risks from the built environment and land use patterns can guide policymakers toward bolstering pedestrian safety. It will, in turn, encourage urban developments that ensure pedestrian-friendly surroundings and walkability. Moreover, the study's comprehension of the interaction between vulnerable pedestrian groups—including low-income individuals, non-car owners, and marginalized populations—with urban arterials will help decision-makers and urban planners to address inclusive road designs, fostering safer and equitable urban environments.

Dates

06/01/2023 to 05/31/2024

Universities

University of Tennessee-Knoxville

Principal Investigator

Christopher Cherry

University of Tennessee-Knoxville

cherry@utk.edu

ORCID: 0000-0002-8835-4617

Research Project Funding

Federal: $69,359

Non-Federal: $25,731

Contract Number

69A3552348336

Project Number

23UTK02

Research Priority

Promoting Safety

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