Infrastructure-enabled risk assessment of hazardous road conditions on rural roads during inclement weather

TL;DR

This paper introduces a risk assessment framework for rural roads during bad weather, combining multiple hazards to predict crash risks and suggest safety measures, validated through a synthetic dataset.

Contribution

It presents a novel hazard risk assessment framework that evaluates combined roadway hazards and provides actionable safety recommendations, addressing a critical gap in rural road safety.

Findings

Combined ProbabilitySeverity scores produce coherent risk profiles.

The framework effectively assesses multiple hazards simultaneously.

Results support deploying graduated safety measures.

Abstract

Rural roadways often expose Commercial Motor Vehicle (CMV) drivers to hazardous conditions, such as heavy fog, rain, snow, black ice, and flash floods, many of which remain unreported in real time. This lack of timely information, coupled with limited infrastructure in rural areas, significantly increases the risk of crashes. Although various sensing technologies exist to monitor individual hazards like low visibility or surface friction, they rarely assess the combined driving risk posed by multiple simultaneous hazards, nor do they provide actionable recommendations such as safe advisory speeds. To address this critical gap, in this study, we present a roadway hazard risk assessment framework that provides an approach to quantify the probability and severity of crash occurrences due to specific roadway hazards. To evaluate this framework, we presented a case study by constructing a synthetic "year-long" dataset that encompasses every possible pairing of road surface and visibility conditions. Our analysis confirms that the combined ProbabilitySeverity scoring yields a coherent, stepwise risk profile across all hazard scenarios. These results validate the practicality of our risk assessment approach and provide a foundation for deploying graduated safety measures in real-world roadway operations.