Real-time crash potential prediction on freeways using connected vehicle data

The real-time crash potential prediction model is one of the important components of proactive traffic management systems. Over the years numerous models have been proposed to predict crash potential and achieved promising results using input data from roadside detectors. However, the detectors are normally installed at certain locations with limited coverage, while the connected vehicle data can provide city-wide mobility information. Previous studies have found that driver event variables such as hard braking, hard accelerations, etc. are correlated with crash potential on the road segments. Nevertheless, the existing studies are mostly conducted at the aggregated level, and the data are mostly collected from commercial vehicles such as taxis or buses traveling in the urban areas. This paper proposes a bidirectional long short-term memory (LSTM) model with two convolutional layers to predict real-time crash potential on freeways. The input data including traffic flow variables from detectors, and driver event variables from connected vehicle (CV) data, are aggregated at the one-minute level. The model achieves a recall value of 0.772 and an AUC value of 0.857. Moreover, to investigate the transferability of the proposed model, the original data are aggregated at the hourly level. The transferred model is developed with fine tuning two convolutional layers of the established model. And the transferred model achieves a recall value of 0.715 and an AUC value of 0.763. This proves that the proposed model can be successfully applied to another similar data set, or when the connected vehicles have lower penetration rate. In this study, we proved the usefulness of the connected vehicle data in the prediction of real-time crash potential, and the possibility of using it without detector data once the penetration rate increases to a reasonable level.