A traffic forecasting system configured to forecast a travel time of a route is disclosed. The traffic forecasting system includes a model-training module, a model-selecting module and a forecasting module. The model-training module builds multiple candidate models. Each of the candidate models corresponds to one of a plurality of road section and one of a plurality of mathematical models. The model-selecting module selects an estimated model corresponding to the road section from the candidate models matching the road sections of the route. The forecasting module calculates estimated vehicle speeds of each road section according to the estimated models of each road section of the route. The model-selecting module selects one of the candidate models corresponding to the road section with the smallest error as the estimated model of the road section.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A traffic forecasting system configured to forecast a travel time of a route, comprising: a model-training module configured to build a plurality of candidate models, wherein each of the candidate models corresponds to one of a plurality of road sections and one of a plurality of mathematical models; a model-selecting module configured to select a corresponding estimated model from the candidate models matching the road sections of the route for each road section; and a forecasting module configured to calculate an estimated vehicle speed for each road section according to the estimated model of each road section of the route, so as to calculate the travel time of the route; wherein the model-selecting module selects one of the candidate models corresponding to the road section with the smallest error as the estimated model of the road section. 2. The traffic forecasting system of claim 1, further comprising: a traffic historical database configured to store at least one historical data, wherein the historical data comprises speed records corresponding to one of the road section in a corresponding time period; a model database configured to store the candidate models corresponding to different time periods, different situation, and different mathematical models; and a data-receiving module configured to receive at least one real-time data, wherein the real-time data comprises a real-time speed information corresponding to one of the road section; wherein the model-selecting module is configured to calculate a estimation error value for each candidate model according to the historical data and the real-time data so as to select the candidate model with the smallest error as the estimated model of the road section. 3. The traffic forecasting system of claim 2, further comprising: a data-processing module electrically coupled to the data-receiving module and configured to perform data processing to the real-time data; and a road section mapping module electrically coupled to the data processing module and configured to map the processed real-time data to corresponding road section in a map data so as to store the real-time data as the historical data in the traffic historical database. 4. The traffic forecasting system of claim 3, wherein the real-time data further comprises at least one situation information, and the data-processing module comprises: a data normalization unit configured to normalize the situation information and the real-time speed information in the real-time data. 5. The traffic forecasting system of claim 3, wherein the real-time data further comprises at least one situation information, and the data-processing module comprises: a situation information analyzing unit configured to receive the situation information and calculate a weight coefficient indicating the impact of the situation information on the traffic speed of corresponding road section at corresponding time period. 6. The traffic forecasting system of claim 5, wherein the situation information analyzing unit is configured to build a situation model according to the weight coefficient, and when the forecasting module determines the corresponding time period of the corresponding road section is in an effective time of the situation model, the forecasting module calculates the estimated vehicle speed for the road section according to the weight coefficient of the situation model. 7. The traffic forecasting system of claim 2, further comprising: a data-recovery module electrically coupled to the traffic historical database and configured to calculate the speed of the time period missing the speed record in the road section according to the historical data so as to recover the historical data. 8. The traffic forecasting system of claim 7, wherein the data-recovery module is configured to perform a space-series data recovery and calculate the speed information of the corresponding road section according to the speed information of multiple adjacent road sections. 9. The traffic forecasting system of claim 7, wherein the data-recovery module is configured to perform a time-series data recovery and calculate the speed information of the corresponding road section according to the speed information of multiple adjacent time periods. 10. A traffic forecasting method performed by a processor, wherein the traffic forecasting method comprises following steps: (a) receiving, by the processor, at least one real-time data; (b) selecting, by the processor, one of a plurality of candidate models corresponding to a route for each road sections of the route as the estimated model of the road section; (c) calculating, by the processor, a travel time according to the corresponding estimated model of the road sections of the route; wherein the estimated model of each road section are selected according to the real-time data and a historical data in a database such that the candidate model corresponding to the road section with the smallest error is selected as the estimated model of the road section. 11. The traffic forecasting method of claim 10, wherein the at least one real-time data further comprises a situation information, and the traffic forecasting method further comprises following steps: (d) determining, by the processor, whether corresponding time period of the corresponding road section is within an effective time of the situation information; (e) calculating, by the processor, a weight coefficient of the situation information at corresponding road section at corresponding time period when the corresponding time period of the corresponding road section is within the effective time; and (f) selecting, by the processor, corresponding estimated model to calculate the estimated vehicle speed for the road section according to the weight coefficient. 12. The traffic forecasting method of claim 11, wherein the step of selecting corresponding estimated model according to the weight coefficient further comprises: when the weight coefficient is larger than a threshold value, calculating the estimated vehicle speed for the road section according to the weight coefficient with a situation model corresponding to the situation information. 13. The traffic forecasting method of claim 11, wherein the effective time of the situation information is between a start point and an end point, wherein the start point and the end point are calculated according to the time period in which the impact of the situation information to the estimated vehicle speed is larger than a threshold value. 14. The traffic forecasting method of claim 11, wherein the situation information comprises at least one of weather information, activities information, and traffic event information. 15. A traffic model establishing method performed by a processor, wherein the traffic model establishing method comprises following steps: (a) receiving, by the processor, at least one real-time data wherein the at least one real-time data comprises a speed information; (b) mapping, by the processor, the speed information in the at least one real-time data to corresponding one of a plurality of road sections in a map data; and (c) calculating, by the processor, a plurality of candidate models corresponding to a plurality of different mathematical models for each of the road sections, such that a model-selecting module selects one of the candidate models of the road sections as an estimated model of the corresponding road section according to the at least one real-time data and a historical data in a traffic historical database. 16. The traffic model establishing method of claim 15, wherein the at least one real-time data further comprises a situation information, and the traffic model establishing method further comprises following steps: (d) normalizing, by the processor, the speed information and the situation information in the real-time data; (e) calculating, by the processor, a weight coefficient of the situation information at corresponding road section and corresponding time period according to the situation information; and (f) mapping, by the processor, the weight coefficient to the corresponding one of the road sections in the map data. 17. The traffic model establishing method of claim 15, the traffic model establishing method further comprises following steps: (g) performing a space-series recovery, by the processor, to the historical data in the traffic historical database and calculate the speed information of the corresponding road section according to the speed information of multiple adjacent road sections. 18. The traffic model establishing method of claim 15, the traffic model establishing method further comprises following steps: (h) performing a time-series recovery, by the processor, to the historical data in the traffic historical database and calculate the speed information of the corresponding road section according to the speed information of multiple adjacent time periods.