Investigating key explanatory factors for safer long-distance bus services
DOI: 10.1186/s12544-024-00665-x
archive: archived pipeline: cataloged verified
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Summary
This study investigates the relationship between weather conditions and accident causes in long-distance bus services to enhance road safety. Focusing on the Madrid-Bilbao corridor in Spain, the research addresses the critical need for data-driven safety interventions, given that buses are a primary mode of transport in the region. The authors aim to identify key explanatory factors for accidents by analyzing real-world data, filling a gap in previous research that often relied on aggregate data or theoretical models. The methodology employs an integrated statistical approach using data from 115 accidents recorded between January 2019 and November 2021 by the operator ALSA, combined with meteorological data from the State Meteorological Agency. The analysis utilizes Latent Class Clustering (LCC) to identify patterns in heterogeneous data, Hierarchical Ordered Logit (HOL) models to assess the relationship between weather and accident causes, and Kaplan-Meier survival analysis to evaluate temporal accident risks. Accident causes were categorized into nine types, while weather conditions were classified as visible, cloudy, rainy, or foggy. The results indicate a downward trend in accidents since 2019, with "manoeuvres" being the most frequent cause (46%). LCC identified three distinct accident clusters; the most probable cluster (63%) involves manoeuvres and cars invading opposite lanes during clear or cloudy weather. The HOL model revealed that rainy weather significantly affects all accident causes, whereas cloudy weather significantly impacts manoeuvres. Foggy and visible conditions showed fewer statistically significant correlations with specific causes. Kaplan-Meier analysis demonstrated a sharp decline in survival probability within the first ten days of the observation period, highlighting a high initial risk of accidents. The significance of this work lies in its comprehensive, multi-method approach to understanding bus accident hazards. By integrating clustering, regression, and survival analysis, the study provides nuanced insights into how weather influences accident causality. The findings underscore the necessity for rapid and sustained safety interventions, particularly regarding manoeuvres and rainy conditions. These actionable insights can inform targeted strategies for improving bus safety, not only in Spain but potentially in other regions worldwide, thereby enhancing robustness in public transportation safety management.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-24 |
| 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 | 2026-06-24 |
| 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