A system for filtering data for a traffic control center includes: a plurality of data sources, comprising a plurality of traffic-related data sources and a weather-related data source; one or more network computing devices, configured to process data from the plurality of data sources to predict causes associated with predicted traffic incidents, and to select data from the plurality of data sources to be output to the traffic control center based on the predicted causes; and one or more output devices, located at the traffic control center, configured to display respective data selected by the one or more network computing devices.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for filtering data for a traffic control center, comprising: a plurality of data sources, comprising a plurality of traffic-related data sources and a weather-related data source; one or more network computing devices, configured to process data from the plurality of data sources to predict causes associated with predicted traffic incidents, and to select data from the plurality of data sources to be output to the traffic control center based on the predicted causes; and one or more output devices, located at the traffic control center, configured to display respective data selected by the one or more network computing devices. 2. The system according to claim 1, wherein processing the data from the plurality of data sources to predict causes associated with predicted traffic incidents includes training an Automatic Incident Cause Prediction (AICP) model using historical data from the plurality of data sources. 3. The system according to claim 2, wherein the one or more network computing devices are further configured to, before training the AICP model, perform a feature selection to reduce a number of variables considered when training the AICP model. 4. The system according to claim 2, wherein the AICP model is trained using the C4.5 algorithm and enhanced via a boosting technique. 5. The system according to claim 2, wherein the one or more network computing devices are further configured to, after training the AICP model, to perform post-processing by linearizing a decision tree generated by the training into a rule set. 6. The system according to claim 5, wherein the post-processing further comprises pruning the rule set based on an amount of support for each rule from the historical data. 7. The system according to claim 1, wherein selecting data from the plurality data sources based on the predicted causes further comprises: assigning scores to locations based on the predicted causes; and ranking the locations based on the assigned scores. 8. A non-transitory computer-readable medium having processor-executable instructions stored thereon for filtering data for a traffic control center, the processor-executable instructions, when executed by a processor, facilitating performance of the following: receiving traffic-related data and weather-related data from a plurality of data sources; processing the traffic-related data and the weather-related data to predict causes associated with predicted traffic incidents; selecting data from one or more traffic-related data sources to be output to the traffic control center based on the predicted causes; and outputting the selected data to the traffic control center. 9. The non-transitory computer-readable medium according to claim 8, wherein processing the data from the plurality of data sources to predict causes associated with predicted traffic incidents includes training an Automatic Incident Cause Prediction (AICP) model using historical data from the plurality of data sources. 10. The non-transitory computer-readable medium according to claim 9, wherein the processor-executable instructions, when executed, further facilitate: before training the AICP model, performing a feature selection to reduce a number of variables considered when training the AICP model. 11. The non-transitory computer-readable medium according to claim 8, wherein selecting data from the plurality data sources based on the predicted causes further comprises: assigning scores to locations based on the predicted causes; and ranking the locations based on the assigned scores. 12. A method for filtering data for a traffic control center, the method comprising; receiving, by a network computing system, traffic-related data and weather-related data from a plurality of data sources; processing, by the network computing system, the traffic-related data and the weather-related data to predict causes associated with predicted traffic incidents; selecting, by the network computing system, data from one or more traffic-related data sources to be output to the traffic control center based on the predicted causes; and outputting, by the network computing system, the selected data to the traffic control center. 13. The method according to claim 12, wherein processing the data from the plurality of data sources to predict causes associated with predicted traffic incidents includes training an Automatic Incident Cause Prediction (AICP) model using historical data from the plurality of data sources. 14. The method according to claim 13, wherein the method further comprises: before training the AICP model, performing a feature selection to reduce a number of variables considered when training the AICP model. 15. The method according to claim 12, wherein selecting data from the plurality data sources based on the predicted causes further comprises: assigning scores to locations based on the predicted causes; and ranking the locations based on the assigned scores.