Considering the importance of traffic flow parameters prediction in intelligent transportation systems, the traffic flow parameter prediction method combining spatio-temporal features was summarized to find a more accurate real-time prediction method. Taking the traffic spatio-temporal data as the research object, the prediction methods of traffic flow parameters were divided into statistical learning method, deep learning method, and graph neural network method. Based on these three categories methods, the research status and characteristics of various methods were summarized. The difficulties of traffic flow parameters prediction were analyzed from the perspective of traditional and spatio-temporal features. The results show that the traffic flow prediction method combined with spatio-temporal features has a great improvement in prediction performance compared with the traditional similar methods because it considers the complex and dynamic spatio-temporal dependence in the road network. Finally, the future research direction of traffic flow parameters prediction was discussed from the perspective of model input and model design.