Abstract
The paper researches the problem of how to forecast traffic information in one minute interval. A kalman filter algorithm for short-term traffic forecasting based on the construction of dynamic traffic routing system of Nanning city of China was proposed in this paper, and selected National Road as field calibration, which compared with the real traffic conditions, and the error of predicted results is less than 10%. The results demonstrate the effectiveness of the proposed forecasting model in paper, and the research has a significant contribution for data process of Dynamic Traffic Routing System.
Highlights
Reliable and accurate traffic information enables traffic participants to make better choices of route which may result in a more efficient use of road networks
This paper introduces approach for the forecasting of overall traffic information from individual traffic information measurements in one minute interval using kalman filter algorithm for dynamic traffic routing system
Through the comparison of experimental results and error analysis, the forecast results are approaching to the actual speed, the predictive value of some points are slightly larger or slightly less than the actual speed, but the relative error is 3.125% less than 10%, it theoretically meets the actual demand for dynamic traffic routing system
Summary
Reliable and accurate traffic information enables traffic participants to make better choices of route which may result in a more efficient use of road networks. This paper introduces approach for the forecasting of overall traffic information from individual traffic information measurements in one minute interval using kalman filter algorithm for dynamic traffic routing system. From the standpoint of how information is used in different model, we can summary the main short-term prediction method of traffic states as: statistical regression, state estimation, time series, neural network, dynamic traffic assignment and traffic simulation [1,2,3,4]. This paper builds the traffic state forecasting model–based Kalman filter for speed and travel time of sections based on the construction of Nanning dynamic route guidance system, it is verified through actual traffic data of Nanning, and the forecasting results are released using VMS display after data fusion and data processing
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