Relationship between Road Network Characteristics and Traffic Safety
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Summary
This study investigates the relationship between road network characteristics and traffic safety, specifically focusing on intersection crashes in San Antonio, Texas. The research was motivated by the high frequency of intersection crashes in the region, exacerbated by rapid economic growth and increased traffic volume. Despite existing safety efforts, intersection crash rates remained elevated, necessitating a data-driven approach to identify critical risk factors and prioritize infrastructure improvements. The primary objective was to analyze crash data to determine contributing factors, identify high-risk intersections, and recommend effective safety countermeasures. The researchers utilized crash data from 2013 to 2017, analyzing 73,755 intersection crashes recorded in the City of San Antonio. The methodology involved retrieving data from state and city databases, sorting variables, and conducting safety analyses using ArcGIS for hotspot identification. Key variables examined included driver demographics (age, gender), environmental conditions (weather, lighting), road characteristics (geometry, traffic control), and crash types. The study calculated both crash frequency and crash rates per million entering vehicles to assess relative safety. Additionally, field observations and site visits were conducted at identified hotspot intersections to evaluate physical conditions and driver behaviors. The findings revealed that driver inattention and disregard for traffic controls were the leading causes of intersection crashes. Demographic analysis showed that male drivers were disproportionately involved in crashes compared to female drivers, despite a higher number of licensed females in the city. Drivers aged 15–34 experienced the highest crash involvement, highlighting a significant risk for younger populations. Angle crashes were the most common type, accounting for over 23% of incidents, while crashes involving vehicles going straight were more prevalent in fatal cases. Hotspot analysis identified 52 intersections with 100 or more crashes and 36 intersections with a crash rate exceeding one per million entering vehicles. The intersection of Bandera Road and Loop 1604 was identified as the highest-risk location, recording 399 crashes and a rate of 8.5 crashes per million entering vehicles. Spatial analysis indicated that crash concentrations were highest in downtown San Antonio and the northern and western sectors of the city. The study concludes that targeted interventions are necessary to reduce intersection fatalities and injuries. Recommended countermeasures include roadway improvements such as installing stop signs with flashing beacons, enhancing nighttime lighting, adding transverse rumble strips, and optimizing signal operations. The authors also advocate for educational campaigns to address driver inattention and increased law enforcement emphasis. Furthermore, the implementation of road safety audits is suggested as a proactive method for identifying and mitigating risks at high-crash intersections. The research underscores the importance of using crash frequency and rate data to prioritize safety investments effectively.
Key finding
Driver inattention ranked first among contributing factors to intersection crashes, and the intersection of Bandera Road and Loop 1604 was identified as the highest risk location with 399 crashes and a rate of 8.5 crashes per million entering vehicles.
Methodology
dataset
Sample size: 73755
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).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- roadway lighting effects
- incidence prevalence
- intersection design
- comparative international
- fatality injury trends
- crash typology
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