This paper presents the results of an analytical study of alternative rear end collision-avoidance algorithms using the Ford REAMACS simulation. REAMACS (Rear End Accident Model And Countermeasure Simulation) models rear end collision situations in freeway traffic and estimates the benefits of collision avoidance systems. Previously reported applications of the model indicate that rear end crash warning system has the potential for a 60% reduction of the number of serious rear end crashes in freeway traffic. The present study confirmed and extended those findings to a broader range of conditions. Two different collision warning algorithms were analyzed:​ (1) 21 Closing Rate algorithm (CRA) which provides a warning only if the following vehicle has a positive closing rate with the lead vehicle, and (2) a Stopping Distance algorithm (SDA) which provides advanced warning of a potential hazard. The Stopping Distance Algorithm was more effective than the Closing Rate algorithm, with effectiveness rates approaching 100%, but gave 440 to 1100 times more warnings than the Closing Rate Algorithm. Whether or not drivers would comply with such warnings is an issue.