Short-Term Traffic Flow Combination Forecast by Co-integration Theory

Abstract Short-term traffic flow prediction is a priority issue of intelligent transportation system. The accuracy of the prediction results directly affects the traffic control and management. Therefore, it is the key technology for the advanced traffic management information system. This paper briefly describes the concept of co-integration and error correction model, and then verifies the validity of the combination of traffic flow forecasting model using the co-integration of series. It also improves the stability of the combination forecasting model through the error correction model. The historical and real-time traffic flow data, collected form the Second Ring Road of Beijing, are used to verify the model. The results indicate that the combination model based on the co-integration and error correction model meets the actual traffic flow characteristics well and obtains a better prediction result.