Assessing Surrogate Safety Measures using a Safety Pilot Model Deployment Dataset

Abstract

Emerging data sources such as Safety Pilot Model Deployment (SPMD) provide a great opportunity to gain a better understanding of collision mechanisms and to develop novel safety metrics. The SPMD program was a comprehensive data collection effort under real-world conditions in Ann Arbor, Michigan, covering over 73 lane-miles and including approximately 3,000 pieces of onboard vehicle equipment and 30 pieces of roadside equipment. In-vehicle data (e.g., speed, location) collected by the SPMD program can potentially be an important supplement to traditional crash data-oriented safety analysis. The goal of this study was to assess roadway link-level surrogate safety measures using the vehicle trajectory data from SPMD. The study’s objectives included: 1) developing a framework to process the SPMD dataset using big-data analytics; 2) converting raw vehicle motion data from SPMD to surrogate safety measures; and 3) analyzing the statistical relationship between crash records and the calculated safety index. The statistical models showed that modified time to collision (MTTC) outperforms time to collision (TTC) and deceleration rate to avoid collision (DRAC) with respect to its goodness of fit. The findings are promising in that augmenting safety analysis with surrogate measures and vehicle performance (e.g., speed and brake duration from connected vehicles) improves the overall model performance. Such information is vital for safety analysis, especially in the absence of detailed roadway and traffic data.