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

2025-08-26

AI Summary

Rural roads face significantly elevated accident risks under compound adverse weather conditions—including dense fog, rain/snow, black ice, and flash floods—due to the absence of real-time, multi-factor coupled risk assessment. Existing sensing technologies are limited to single-point monitoring and fail to quantify composite hazards or generate actionable safety recommendations. To address this, we propose a road risk assessment framework integrating multi-source perceptual parameters (e.g., visibility, pavement friction coefficient) and introduce the first probabilistic–severity (P–S) scoring model for hierarchical quantification of coupled disaster risks. Validated on a synthetically generated, year-round multi-scenario dataset, the model demonstrates consistent stepwise risk stratification across diverse weather combinations. It enables dynamic safe-speed advisories and tiered traffic management strategies, establishing a novel paradigm for intelligent, resilient operation of rural road networks.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Assess combined driving risks from multiple simultaneous weather hazards
• Quantify crash probability and severity on rural roads during inclement weather
• Provide actionable safety recommendations like advisory speeds for CMV drivers

Innovation

• Infrastructure-enabled risk assessment framework
• Synthetic dataset for hazard pairing evaluation
• ProbabilitySeverity scoring for stepwise risk profiling