Compared with traditional sensor networks, the mobile crowdsensing network has a great advantage in deployment and maintenance costs, thus becoming more and more popular in intelligent transportation systems. Prediction of traffic state is significant for traffic management systems. We explore the travel speed data in the mobile crowdsensing network to predict the future speed of vehicles. Combining with the seasonal algorithm and the cuckoo search algorithm (CSA), we use the support vector regression model (SVR) to determine the main parameters of the SSVR, and propose a prediction model called CSASSVR to estimate the road traffic status in the future. Experimental results show that this model has good accuracy in traffic state prediction in the mobile crowdsensing network.