Road Attributes and Traffic Characteristics Effects on Motorcycle Safety
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Summary
This study addresses the critical road safety challenges facing motorcyclists in Indonesia, where motorcycles account for a significant portion of the over 31,000 annual road traffic deaths. While infrastructure-based interventions are known to mitigate risks, their implementation is often uneven, and research has historically prioritized arterial roads over local roads. This paper specifically investigates the safety risks for motorcyclists in campus environments, focusing on the Universitas Gadjah Mada (UGM) campus area. The research aims to systematically assess how local road attributes and traffic characteristics influence crash likelihood and injury severity, thereby identifying suitable infrastructure countermeasures. The methodology employed the International Road Assessment Programme (iRAP) tool to calculate Star Rating Scores (SRS) for motorcycle safety. The study utilized primary observational data on road conditions and secondary accident data recorded by the UGM Office of Security from July to December 2024. The analysis covered 12 road sections and 12 intersections (including roundabouts, signalized, and unsignalized intersections) where accidents occurred. Researchers assessed 64 variables related to road design and traffic dynamics, reducing them to 23 variables for road sections and 37 for intersections to avoid duplication. Correlation and non-linear regression analyses were conducted to determine the relationship between specific road attributes and the SRS, with higher SRS values indicating lower safety levels. The results identified distinct factors influencing safety at road sections versus intersections. For road sections, curvature, median traversability, and operating speed were the most significant determinants of the SRS, primarily affecting accident likelihood. An exponential regression model showed a strong positive correlation between these variables and SRS, indicating that sharper curves, traversable medians, and higher speeds increase risk. For intersections, the significant attributes were categorized by their influence on likelihood or severity. Likelihood was driven by curvature, intersection quality, channelization, and property access points. Severity was influenced by roadside severity distance, paved shoulder width, and property access points. Regression analysis revealed complex relationships; for instance, while wider paved shoulders generally improved safety, the impact of roadside distance and curvature showed non-linear patterns, suggesting that motorcyclists may adjust their caution based on perceived danger, though extreme conditions ultimately increase risk. The study concludes that infrastructure improvements targeting specific attributes—such as managing curvature, improving median barriers, controlling operating speeds, and enhancing intersection design—can significantly reduce motorcycle crash risk and severity in campus areas. The findings highlight that local roads, often perceived as safer, present comparable risks to arterial roads due to complex traffic mixes and inadequate infrastructure. The authors recommend prioritizing these identified attributes in safety interventions and suggest future research expand to cover all campus roads over longer periods to ensure stable data trends. This work provides a data-driven framework for evaluating and improving road safety for vulnerable users in low-speed, high-density environments.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | canonical_url | — | — | 1 | 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