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. The parameters in the logistic regression models were calibrated using collision data gathered from the 6-mile study site between 2006 and 2007. Findings indicated that an additional unit increase in RCRI results in increasing the odds of rear-end collision by 21.1%, a unit increase in standard deviation of upstream occupancy increases the odds by 19.5%, and a unit increase in standard deviation of downstream occupancy increases the odds by 18.7%. The likelihood of rear-end collisions is highest when the traffic approaching from upstream is near capacity state while downstream traffic is highly congested. The paper also reports on the findings from comparing the predicted number of rear-end collisions at the study site using the proposed model with the observed traffic collision data from 2008. The proposed model’s true positive rates were higher than those of existing real-time crash prediction models.