Traffic congestion is a vital factor hindering travel. As such, developing a reliable traffic congestion early warning system is essential for providing traffic condition supervision and programming. However, previous research has rarely focused on traffic flow characteristics or on providing comprehensive assessments, resulting in poor warning performances. In this study, an innovative traffic congestion early warning system is proposed, comprising point forecasting, characteristic estimate, interval prediction, and comprehensive assessment. In the characteristic assessment phase, eight common statistical distributions are used to fit the characteristics of an original traffic flow parameter series in a training set, and the best fitting results are considered as the basis for building a prediction interval. An extreme learning machine combined with a modified multi-objective dragonfly optimization algorithm and variational mode decomposition is constructed in the point forecasting phase to provide accurate and stable traffic flow parameter forecasting results; two different strategies are used to establish the prediction interval, so as to conduct interval forecasting based on different types of uncertainty information (probability distribution information or known interval information). Extenics evaluation theory is then used in the comprehensive assessment phase to evaluate the traffic congestion level. Simulations of traffic flow parameter series, including simulations of the road density, road occupancy, and average velocity, reveal that the proposed early warning system demonstrates powerful abilities based on its precision and stability. The mean absolute percentage error (MAPE) values of the traffic flow parameters for the three datasets are 3.6265%, 3.7203%, and 4.5100%, respectively. The forecasting accuracy for the traffic congestion level is more than 97% for both point and interval prediction. Thus, this approach can be widely used for personal traffic route planning and the unified management of governmental traffic conditions.