Investigating key explanatory factors for safer long-distance bus services

Abstract

Abstract Buses are among the most accessible and frequently used means of transport. Due to its importance, road safety analysis is frequently conducted to reduce accidents. This paper studied the relationship between weather conditions and the causes of accidents to improve road safety, focusing on long-distance services between Madrid and Bilbao (Spain). We employed Latent Class Clustering (LCC) and Hierarchical Ordered Logit models to identify these factors’ relationships. Additionally, Kaplan-Meier survival analysis was adopted to provide temporal insights into accident occurrences. The main results show a downward trend in accidents since 2019, with manoeuvres being the most frequent cause. LCC reveals that “manoeuvres and car invading lanes in the opposite direction” in “clear and cloudy weather” has the highest probability of occurrence (63%). The hierarchical-ordered logit model indicates that rainy weather significantly affects all accident causes. Kaplan-Meier survival analysis reveals a vertical initial decline in survival probability within the first ten days, emphasizing a high initial accident risk. The integrated approach used in this work provides a thorough understanding of accident hazards, which is its main contribution. By integrating LCC, Hierarchical Ordered Logit models and Kaplan-Meier survival analysis; we could offer a comprehensive and nuanced interpretation of the connection between weather and bus accidents. The findings highlight the need for rapid and sustained safety interventions, enhancing robustness and providing actionable insights for improving bus safety.

Keywords

• Bus transport
• Road Safety
• Weather conditions
• Latent class clustering (LCC)
• Hierarchical ordered logit method
• Kaplan-Meier survival analysis