Quantile-Physics Hybrid Framework for Safe-Speed Recommendation under Diverse Weather Conditions Leveraging Connected Vehicle and Road Weather Information Systems Data

2026-02-04

AI Summary

This study addresses the critical need for dynamically adjusting safe vehicle speeds under adverse weather conditions, where reduced visibility and diminished road friction significantly compromise driving safety. To this end, the authors integrate connected vehicle (CV) data with Road Weather Information System (RWIS) measurements to construct a spatiotemporally aligned dataset. They propose a novel framework that combines quantile regression forest (QRF) with a physics-based braking model grounded in visibility and road adhesion coefficients. This approach simultaneously predicts the full distribution of safe speeds in a data-driven manner while embedding physically informed upper-bound constraints, thereby ensuring both flexibility and physical plausibility. Experimental results demonstrate strong performance: 96.43% of median speed predictions exhibit errors within 5 mph, with a mean absolute error of 1.55 mph and a prediction interval coverage probability PICP(50%) of 48.55%, indicating robust generalization across diverse weather conditions and road segments.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Ad search and search for Web retail

Abstract

Inclement weather conditions can significantly impact driver visibility and tire-road surface friction, requiring adjusted safe driving speeds to reduce crash risk. This study proposes a hybrid predictive framework that recommends real-time safe speed intervals for freeway travel under diverse weather conditions. Leveraging high-resolution Connected Vehicle (CV) data and Road Weather Information System (RWIS) data collected in Buffalo, NY, from 2022 to 2023, we construct a spatiotemporally aligned dataset containing over 6.6 million records across 73 days. The core model employs Quantile Regression Forests (QRF) to estimate vehicle speed distributions in 10-minute windows, using 26 input features that capture meteorological, pavement, and temporal conditions. To enforce safety constraints, a physics-based upper speed limit is computed for each interval based on real-time road grip and visibility, ensuring that vehicles can safely stop within their sight distance. The final recommended interval fuses QRF-predicted quantiles with both posted speed limits and the physics-derived upper bound. Experimental results demonstrate strong predictive performance: the QRF model achieves a mean absolute error of 1.55 mph, with 96.43% of median speed predictions within 5 mph, a PICP (50%) of 48.55%, and robust generalization across weather types. The model's ability to respond to changing weather conditions and generalize across road segments shows promise for real-world deployment, thereby improving traffic safety and reducing weather-related crashes.

Problem

• safe-speed recommendation
• adverse weather conditions
• connected vehicle data
• road weather information systems
• traffic safety

Innovation

• Quantile Regression Forests
• Physics-based speed limit
• Connected Vehicle data
• Road Weather Information System
• Safe-speed recommendation