Integrating road infrastructure condition into intelligent transportation systems: a machine learning-based infrastructure-aware risk prediction approach

Abstract

Although the majority of Intelligent Transportation Systems (ITS) and risk-prediction frameworks keep considering pavement quality and dynamic traffic behavior as separate phenomena, road infrastructure degradation and traffic flow instability both contribute to risky driving situations. This study combines segment-level Pavement Condition Index (PCI) data with actual traffic observations from New York City to present an integrated, data-driven methodology for simulating infrastructure-induced unsafe driving circumstances. A supervised machine learning model is developed by combining measures of traffic congestion, speed variation, and pavement deterioration to estimate hourly instability risk, which serves as a proxy for risky driving behavior. Evaluating on a temporally separated test set, the infrastructure-aware model achieves an ROC-AUC of 0.9804 and a PR-AUC of 0.9074, outperforming a traffic-only baseline (ROC-AUC 0.8785, PR-AUC 0.5833). These instability indicators correspond to high-level behavioral patterns commonly observed in ITS monitoring contexts, without relying on raw visual data. Model explainability using SHAP indicates that pavement condition and congestion are the most influential features, with comparable contributions to instability prediction. Predicted risk probabilities are geospatially mapped to identify infrastructure-driven hotspots, and the ORQCIAM framework demonstrates how such risk outputs can inform infrastructure-aware routing and maintenance prioritization. The findings reveal that machine learning enhanced with pavement condition data offers a data-driven approach for predicting hazardous driving situations and supporting infrastructure-aware decision-making, demonstrating how infrastructure-aware risk estimates might help with routing analysis and repair priority in future ITS applications.