Exploring the viability of surrogate safety measures based on smartphone data for the safety assessment and screening of urban road street segments

The focus of traffic safety research has recently shifted from reactive to proactive safety management approaches. In reactive approaches, historical crash data is used to identify accident-prone locations or black spots. However, this approach is inherently reactive, focusing on accidents that have already occurred, requiring several years of accident data. Recently, surrogate safety measures have been adopted as a proactive alternative for identifying black spots. Nowadays, the widespread use of smartphones equipped with GPS systems makes it possible to utilize mobile GPS data to identify accident-prone locations. In this study, GPS-derived surrogate safety measures extracted from smartphone data are used to predict the frequency of potential accident incidences on road segments. On this basis, several Poisson-based statistical models and Artificial Neural Network (ANN) models were developed based on the data collected by smartphones on two carriageways of a long arterial street in Mashhad, Iran. GPS trajectory data, including vehicle speed, acceleration, and location, were collected using a GPS data recorder application installed on the smartphones of drivers and passengers traveling along the carriageway. The results indicated that both the Poisson-based and ANN models can predict crash frequency with reasonable accuracy. However, the ANN model, comprising surrogate speed and acceleration-related safety measures, demonstrated slightly better performance than Poisson-based regression models. The models were then successfully tested for their ability to identify accident-prone segments. Our findings indicated that data obtained from smartphones can be used as surrogate measures for assessing road safety and ranking accident-prone segments along urban roads.