Enhanced Crash Frequency Models Using Surrogate Safety Measures from Connected Vehicle Fleet

Abstract

As connected vehicle data became available, efforts to employ surrogate safety measures (SSMs) for crash frequency modeling were undertaken. For road safety evaluation, traffic conflicts are quantitatively measured by various SSMs in two dimensions: spatial/temporal proximity (e.g., time-to-collision, TTC) and evasive action (e.g., deceleration rate, DR). However, a single SSM or a single dimension only represents partial images of the true severity of traffic conflicts. Therefore, this study investigates possible enhancements in crash frequency modeling by concurrently using proximity and evasion SSMs. For rear-end crash frequency estimation, five negative binomial regression models and two tree-based models (a regression tree and random forest) were developed. All models were estimated using crashes, traffic volume, and segment length, along with three SSMs (DR, TTC, and modified TTC) extracted from connected vehicle data in Ann Arbor, Michigan. Results show that the multi-SSM model produced a 19.3% reduction in mean absolute error (MAE) compared to the baseline model with no SSM variable, which was significantly higher than those of single-SSM models (4.5−6.9% reductions in MAE). Between all models, the random forest, which is the ensemble machine learning model, produced the highest error reductions (a 44.3% reduction in MAE). These findings show that the concurrent use of proximity and evasion SSMs can yield further enhancements in crash frequency models compared to the singular use of either type of SSM. The proposed modeling method can be used for proactive safety management and assessment using connected vehicle data collected over a short period.