A Proactive Approach to Evaluating Intersection Safety Using Hard-Braking Data
DOI: 10.1007/s42421-021-00039-y
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Summary
This study addresses the limitations of traditional intersection safety evaluations, which rely on 3–5 years of crash data to identify and prioritize improvements. This reactive approach delays mitigation efforts due to the statistical infrequency of crashes. The authors propose a proactive method using crowdsourced hard-braking data as a surrogate for safety conflicts, aiming to identify emerging safety issues more rapidly. The research investigates whether hard-braking events, specifically those occurring upstream of signalized intersections, correlate with historical rear-end crash frequencies. The methodology involved analyzing weekday hard-braking data collected in July 2019 from eight signalized intersections along the SR-37 corridor in Indiana. The study utilized enhanced probe data from connected vehicles, defined as decelerations exceeding 8.76 ft/s², with an estimated penetration rate of 2%. Hard-braking events were geofenced to through lanes and categorized by distance from the stop bar: within 400 feet (associated with dilemma zones or stopping for red lights) and greater than 400 feet (associated with queuing under oversaturated conditions). These event counts were compared against rear-end crash data spanning 4.5 years at the same locations. Statistical analysis employed Spearman’s rank-order correlation to assess the relationship between hard-braking frequencies and crash rates. The results demonstrated a strong correlation between hard-braking events occurring more than 400 feet upstream of the stop bar and historical rear-end crashes. While the majority of hard-braking events occurred within 400 feet of the stop bar, specific intersections exhibited significant upstream braking activity, particularly during peak hours, indicative of long queues. For instance, intersections such as Smith Valley Road and Southport Road showed distinct patterns of upstream braking during PM peak periods. The analysis confirmed that hard-braking data effectively highlights locations with elevated rear-end crash risks, validating its use as a screening tool. The significance of this work lies in its potential to transform safety management from a reactive to a proactive model. By utilizing commercially available hard-braking data, transportation agencies can screen for potential safety hazards using only one or two months of data, rather than waiting years for sufficient crash statistics. This approach allows for quicker identification of emerging problems and faster implementation of mitigation measures, such as signal retiming or geometric changes, ultimately improving intersection safety more efficiently than traditional practices.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-20 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| 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