Exploring Crowdsourced Hard—Acceleration and Braking Event Data for Evaluating Safety Performance of Low-Volume Rural Highways in Iowa
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Summary
This study addresses the challenge of monitoring safety performance on low-volume rural roads (LVRs) in Iowa, where traditional crash data is often insufficient due to the rarity of incidents and the extensive geographic spread of the network. With over four million miles of two-lane roadways in the United States, many of which are rural LVRs with annual average daily traffic under 400 vehicles, transportation agencies struggle to identify high-risk locations proactively. The research investigates whether commercially available crowdsourced hard-acceleration and hard-braking (HA/HB) event data can serve as a reliable surrogate for historical crash data, enabling more frequent and precise safety evaluations. The methodology involved analyzing approximately 12 million HA/HB events recorded over a three-month period (October–December 2021) and 26,743 crashes, including 9,373 fatal injuries, from a five-year period (2016–2021). The study focused on 50,662 road segments comprising roughly 63,000 miles of Iowa’s LVR network. Researchers used ArcGIS to spatially join HA/HB events and crash data to the nearest road segments within a 100-foot buffer. Correlation analysis and multiple linear regression models were employed to assess the relationship between HA/HB frequencies and crash rates, incorporating variables such as traffic volume, lane count, speed limits, and terrain. Additionally, Getis-Ord ($G_i^*$) spatial statistics were used to perform hotspot analysis, identifying spatial clusters of high-risk locations based on both datasets. The results demonstrated a moderate correlation (coefficient of 0.59) between HA/HB events per mile and crash events per mile per year. Regression analysis indicated that for every 342 HA/HB events per mile per year, one crash per mile per year occurred, with the HA/HB rate remaining a significant predictor even when controlling for roadway geometry and traffic volume. The hotspot analysis identified 848 common high-risk sites across both datasets, validating the spatial consistency between surrogate driving behavior data and historical crash records. The study also proposed a combined ranking scheme that integrates historical crash counts with HA/HB event frequencies to prioritize safety improvements. The significance of this research lies in its potential to shift transportation safety management from a reactive to a proactive approach for rural networks. By leveraging readily available, low-latency connected vehicle data, agencies can monitor the safety performance of vast LVR networks more frequently than is possible with sparse crash reports. The findings suggest that HA/HB event data is a viable tool for identifying high-risk locations and allocating resources for crash mitigation countermeasures, offering a practical solution for the ongoing challenge of managing safety on low-volume rural highways.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Empirical Findings: crash risk outcomes, observational prevalence
- Methodological Resource: dataset resource