交通路面温度预测系统的设计

pplied Kalman filter to modifying the collected data,and based on the combination of the data collected by the transportation meteorology device and the original meteorological input valueto build a flexible forecast model,describing how to use temperature, longitude and latitude data to drive the program process.The application was written in C++. Set latitude、longitude and temperature data obtained from the meteorological station on highway as source data to predict the temperature value for a certain point which own the longitude and latitude we hope to check.Any point can be forecasted within the range of the collection template. Using Beijing's Sixth Ring Road's measured data and forecasted data betwwen 28 February, 2014 and 1 March, 2014 for datat valid verification.The results show that predictive value and the real value of the road network trends coincide, a correlation coefficient is 0.88, the maximum absolute error is 0.55 C, a minimum is 0.0023 C,which prove that this forecast model is valuable for the prediction of continuous temperature on the space dimension along the highway network.