Road crashes and field rainfall data: mathematical modeling for the Brazilian mountainous highway BR-376/PR

Guideli, Leandro Canezin; Cuenca, André Lucas dos Reis; Silva, Milena Arruda; Passini, Larissa de Brum · 2021 · DOAJ

DOI: 10.14295/transportes.v29i4.2498

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Summary

This study investigates the relationship between daily rainfall intensity and traffic crash frequency on a rural, mountainous segment of the Brazilian highway BR-376/PR. Motivated by the need for spatially precise weather data to improve crash risk modeling, the research addresses the limitations of using regional weather stations, which may lack local correlation with specific road conditions. The authors aim to quantify how precipitation influences accident occurrence, while also examining variations by season and vehicle type. The analysis covers data from March 2014 to September 2018. Precipitation data was collected from six in-field rain gauges distributed along the highway, grouped into two areas (North and South) based on statistical similarity in rainfall patterns. Crash data, comprising 2,314 incidents, was obtained from the Federal Highway Police database. To account for traffic exposure, daily crash counts were normalized using traffic volume data from toll stations. The researchers employed Negative Binomial regression models, selected over Poisson models due to data over-dispersion. Separate models were estimated for the two highway segments, four seasons, and two vehicle categories (light and heavy). Rainfall was categorized into six classes ranging from drizzle to extreme precipitation. The results demonstrate a consistent positive relationship between daily rainfall intensity and crash frequency across all models. In the North Area, extreme precipitation (>50 mm) increased crash frequency by approximately 6% compared to dry days, while the South Area showed a smaller but significant increase. Seasonal analysis revealed distinct critical periods: summer (DJF) was the most hazardous season for rainfall-related crashes in the North Area, whereas winter (JJA) was most critical in the South Area. This difference is attributed to road geometry; the South Area features sharper curves and higher grades, which may induce driver caution that mitigates rain-related risks, unlike the straighter North Area. Regarding vehicle types, rainfall significantly increased crash frequency for light vehicles in both areas. However, for heavy vehicles in the South Area, rainfall was not a significant factor. This is explained by the prevalence of overturning accidents in the South, driven by sharp curves, whereas the North Area saw more runway exits, which are directly influenced by road slipperiness caused by rain. The study concludes that local rainfall data provides a more accurate assessment of crash risk than regional data. The findings highlight the necessity of distinct road safety policies that account for seasonality and specific road geometries. Furthermore, the results suggest that traffic control measures should prioritize light vehicles during rainy conditions, while heavy vehicle safety in curved, mountainous segments may require interventions focused on road design rather than weather-dependent warnings.

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Topics

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• incidence prevalence
• weather rain fog snow

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• Empirical Findings: crash risk outcomes