Short-Term Traffic Flow Prediction Considering Spatio-Temporal Correlation: A Hybrid Model Combing Type-2 Fuzzy C-Means and Artificial Neural Network

Traffic flow prediction is a key step to the efficient operation in the intelligent transportation systems. This paper proposes a hybrid method combing clustering methods and spatiotemporal correlation to predict future traffic trends based on artificial neural network. First, for the traffic flow collected from different loop detectors, a spatio-temporal correlation of data samples is evaluated by considering time correlation and spatial equivalent distance. Second, in order to improve classifying performance and reliability to anomalous data samples, a type-2 fuzzy c-means (FCM) is adopted to make fuzzification of the membership function. Then, a hybrid prediction model combined classification algorithm and neural network is designed to predict various patterns or trends in traffic flow data. Furthermore, the results from the prediction model are modified according to quantized spatio-temporal correlation. Finally, traffic volume data collected from the highway is used to optimize the parameter in the prediction model combination. Several traditional models are used as candidates in comparison, and the higher prediction accuracy demonstrates the effectiveness and feasibility of the hybrid prediction model.