Denoising traffic collision data using ensemble empirical mode decomposition (EEMD) and its application for constructing continuous risk profile (CRP).

Filtering out the noise in traffic collision data is essential in reducing false positive rates (i.e., requiring safety investigation of sites where it is not needed) and can assist government agencies in better allocating limited resources.

Surrogate safety measure for evaluating rear-end collision risk related to kinematic waves near freeway recurrent bottlenecks.

This study presents a surrogate safety measure for evaluating the rear-end collision risk related to kinematic waves near freeway recurrent bottlenecks using aggregated traffic data from ordinary loop detectors. The attributes of kinematic waves that accompany rear-end collisions and the traffic conditions at detector stations spanning the collision locations were examined to develop the rear-end collision risk index (RCRI). Together with RCRI, standard deviations in occupancy were used to develop a logistic regression model for estimating rear-end collision likelihood near freeway recurrent bottlenecks in real-time.