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This paper aimed at evaluating the application of the genetic programming (GP) model for real-time crash prediction on freeways. Traffic, weather, and crash data used in this paper were obtained from the I-880N freeway in California, United States. The random forest (RF) technique was conducted to select the variables that affect crash risk under uncongested and congested traffic conditions. The GP model was developed for each traffic state based on the candidate variables that were selected by the RF technique. The traffic flow characteristics that contribute to crash risk were found to be quite different between congested and uncongested traffic conditions. This paper applied the receiver operating characteristic (ROC) curve to evaluate the prediction performance of the developed GP model for each traffic state. The validation results showed that the prediction performance of the GP models were satisfactory. The binary logit model was also developed for each traffic state using the same training data set. The authors compared the ROC curve of the GP model and the binary logit model for each traffic state. The GP model produced better prediction performance than did the binary logit model for each traffic state. The GP model was found to increase the crash prediction accuracy under uncongested traffic conditions by an average of 8.2% and to increase the crash prediction accuracy under congested traffic conditions by an average of 4.9%.