Intelligent transportation systems use parameters such as traffic flow, density and speed to manage city traffic. This paper presents a novel prediction model for traffic speed prediction consisting of nine stages. In the presented model, real vehicle data were passed through data filtering and map matching processes, density-based clusters were created, cluster features were generated, instant traffic state was displayed, and traffic speed prediction was performed using with the artificial neural network RNN model. In previous studies, while traffic speed prediction can be performed on a specific road with stationary data sources or on different days with distributed GPS records, in the developed model, characteristics of the interested road are created by obtaining density-based vehicle cluster features, short-term and data-driven speed prediction is made within the changeable structure of traffic. Speed prediction was tested on Eskisehir and Istanbul roads belonging to Ankara province, the error rates were determined for speed prediction using the RNN variant LSTM and GRU methods, Eskisehir road LSTM-GRU error rates were measured as 8,595-8,656 and Istanbul road error rates as 7,331-7,955, respectively. The developed model for the changeable nature of traffic has yielded successful results in near real time. It is considered that the proposed model will offer different and new solutions in the prediction of traffic parameters, accelerate the processes and assist to the users more accurate and faster services.