This study investigates the real-time traffic factors associated with sideswipe crashes, including a surrogate measure of lane change and compares conditions for sideswipe and rear-end crashes on the basis of these factors. This study extends a previous experimental study of lane change by suggesting that a geometric mean of ratios of flows between adjacent lanes called the overall average flow ratio (OAFR) can be used to indicate the likelihood of sideswipe crashes given that a crash is likely to occur. OAFR was calculated for 5 to 10 min before crash occurrence at a location upstream of crash sites. Using 4-year crash and loop detector data on a 36.3-mi section of I-4 freeway in Orlando, Florida, the study found that OAFR was generally higher for sideswipe than rear-end crashes at a 95% confidence level. Analysis using logistic regression models showed that other traffic-related factors, such as the variation in flow and peak and off-peak periods, were also important factors correlated with sideswipe crashes. The findings in this study suggest that the model is potentially capable of predicting the risk of specific types of crashes, not only generic crashes, through use of real-time traffic flow data on freeways.