Estimation of Crash Modification Factors (CMFs) in Mountain Freeways: Considering Temporal Instability in Crash Data

Abstract

The combined contributions to mountain freeway safety of pavement performance, weather conditions, and traffic condition indicators have not been thoroughly investigated due to the complexity of their interactions and temporal instability. A cross-sectional analysis using a Generalized Linear Model (GLM) approach with negative binomial distribution considering time-correlation effects (TC-NB) was adopted to estimate the Crash Modification Factors (CMFs) of these indicators for different segment types, alignment types, and cross-sectional forms based on eight quarters of data from mountain freeways in China. According to the results, improving the pavement performance indexes positively impacts the safety of different freeway segments, especially for the curved segments. Quarterly Average Daily Traffic (QADT) has significantly negative safety effects on two-lane segments with relatively narrow spaces, while the proportion of large vehicles plays a decisive role in the safety impacts of tunnel segments. Small/moderate rain days in a quarter (SMR) were significantly positively correlated with crash frequency, while the percentage of torrential rain days in a quarter (TR) showed an opposite trend. The results of this study contribute to the effective coordination of traffic monitoring systems, pavement management systems, and traffic safety management systems to develop targeted improvement countermeasures for different freeway section types.