Bicycle and Pedestrian Safety Research Project

Jackson, Steve; Raymond, Paula; Taylor, Stephen · 2023 · ROSA P / Idaho Transportation Department

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Summary

This research report addresses the need for systematic crash typing to identify safety hotspots and deploy effective countermeasures for vulnerable road users in Idaho. The study was motivated by the complexity of bicycle and pedestrian crashes, which vary by infrastructure, user behavior, and environmental factors. By categorizing crashes into homogeneous groups, transportation agencies can better target rising problems and select appropriate safety interventions. The project aimed to apply established crash typing methodologies to a decade of Idaho crash data to analyze trends, identify risk factors, and recommend specific countermeasures. The methodology involved analyzing 10 years (2012–2021) of crash records from the Idaho Transportation Department’s WebCars database, comprising 2,739 bicycle crashes and 2,209 pedestrian crashes. The researchers utilized the Pedestrian and Bicycle Crash Analysis Tool, Version 2 (PBCAT2), as the primary crash typing framework, supplemented by hierarchical clustering algorithms. To facilitate the extraction of data from officer-generated crash narratives, the team employed large language models (LLMs) to automate the identification of key crash details. Data cleaning and analysis were conducted using the R statistical computing language. The study also reviewed literature on alternative methods, such as the Location-Movement Classification Method (LMCM) and various machine learning applications, to contextualize their approach. Key findings indicate divergent trends for bicyclists and pedestrians. While the total number of bicycle crashes declined over the ten-year period, pedestrian crashes remained steady, with a notable increase in fatalities. Specific crash types showed increasing trends, particularly those occurring near parking lots, alleys, and driveways, as well as incidents involving motorists failing to signal, making improper left turns, or speeding near turns and hills. Critical risk factors for fatal or serious injuries included motorist speeding and impairment, midblock crossings for bicyclists, and pedestrians walking along the roadway. The analysis identified high-occurrence corridors in major Idaho cities, such as Boise, Nampa, and Idaho Falls, mapping these hotspots to specific crash clusters. The significance of this research lies in its data-driven recommendations for improving safety for active transportation users in Idaho. Based on the identified crash types and risk factors, the authors suggest countermeasures including the expansion of bicycle facilities, enhanced lighting, reduced curb radii, restriction of curbside parking, and minimized visual clutter. The report also emphasizes the need for enhanced law enforcement to address distracted driving and speeding. Additionally, it recommends providing advanced medical training to emergency responders to improve survival rates in the event of a crash. These findings provide the Idaho Transportation Department with a structured framework for prioritizing safety improvements and allocating resources effectively.

Key finding

Bicycle crashes declined over the study period while pedestrian crashes remained steady with rising fatalities, driven primarily by motorist speeding and specific high-risk scenarios like parking lot interactions.

Methodology

dataset

Sample size: 4948

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

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.

• vru crash typology
• cyclist safety
• motorcycle crash typology
• incidence prevalence
• crash typology
• 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
• Methodological Resource: dataset resource