An adaptive hybrid model for short-term urban traffic flow prediction

Abstract With the rapid increase in car ownership, urban transport systems are challenged by the overwhelming traffic demand and congestion. Dynamic prediction of traffic flows is of considerable significance for congestion mitigation and demand management. Real-time and precise prediction models are capable of analyzing traffic flow characteristics, predicting traffic flow trends, and motivating reasonable inductive actions. Considering the periodicity and variability of traffic flow and limitations of single prediction models, an adaptive hybrid model for predicting short-term traffic flow was proposed in this study. Firstly, the linear Autoregressive Integrated Moving Average (ARIMA) method and non-linear Wavelet Neural Network (WNN) method were used to predict traffic flow. Then, outputs of the two individual models were analyzed and combined by fuzzy logic and the weighted result was regarded as the final predicted traffic volume of the hybrid model. The results indicate that the hybrid model can offer better performance in predicting short-term traffic flow than the two single models either in stable or in fluctuating conditions. The relative error is within ± 10%, showing that the proposed hybrid model is both accurate and reliable.