One or more techniques and/or systems are provided for training and/or utilizing a traffic obstruction identification model for identifying traffic obstructions based upon vehicle location point data. For example, a training dataset, comprising sample vehicle location points (e.g., global positioning system location points of vehicles) and traffic obstruction identification labels (e.g., locations of known traffic obstructions such as stop signs, crosswalks, stop lights, etc.), may be evaluated to extract a set of training features indicative of traffic flow patterns. The set of training features and the traffic obstruction identification labels may be used to train a traffic obstruction identification model to create a trained traffic obstruction identification model. The trained traffic obstruction identification model may be used to determine whether a road segment has a traffic obstruction or not.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a traffic obstruction identification model, comprising: obtaining a training dataset comprising sample vehicle location points and traffic obstruction identification labels; extracting a set of training features from the training dataset based upon the sample vehicle location points, the set of training features indicative of traffic flow patterns; and training a traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create a trained traffic obstruction identification model for identifying traffic obstructions based upon vehicle location point data. 2. The method of claim 1, comprising: obtaining a dataset comprising vehicle location points; extracting a set of features from the dataset based upon the vehicle location points, the set of features indicative of traffic flow patterns; and evaluating the set of features using the trained traffic obstruction identification model to determine whether a road segment has a traffic obstruction. 3. The method of claim 2, comprising: determining whether a current traffic flow pattern is a result of congestion or the traffic obstruction based upon whether the road segment has the traffic obstruction. 4. The method of claim 1, the traffic obstruction comprising at least one of a stop light, a stop sign, a crosswalk, a railroad crossing, a traffic flow impediment, a temporary obstruction, or a permanent obstruction. 5. The method of claim 1, the extracting a set of training features comprising: evaluating the sample vehicle location points to identify a count of vehicles having speeds below a speed threshold; and comparing the count of vehicles to a total count of vehicles to determine a vehicle speed feature for inclusion within the set of training features. 6. The method of claim 1, the extracting a set of training features comprising: evaluating the sample vehicle location points to determine a median speed; and identifying a standard deviation from the median speed to determine a median average deviation feature for inclusion within the set of training features. 7. The method of claim 1, the extracting a set of training features comprising: identifying a first count of vehicle location points within a first road segment; and comparing the first count of vehicle location points to counts of vehicle location points within one or more neighboring road segments to determine a relative point density feature for inclusion within the set of training features. 8. The method of claim 2, the obtaining a dataset comprising vehicle location points comprising: receiving a first set of global positioning system (GPS) location points from a first vehicle; receiving a second set of GPS location points from a second vehicle; and including the first set of GPS location points and the second set of GPS location points within the dataset. 9. The method of claim 1, the training a traffic obstruction identification model comprising: identifying one or more parameters for use by the trained traffic obstruction identification model based upon the training dataset and the set of training features. 10. The method of claim 1, the extracting a set of training features comprising: extracting a first set of training features for a first road segment; and extracting a second set of training features for a second road segment. 11. The method of claim 8, the first set of GPS location points collected at a first time period and the second set of GPS location points collected at a second time period. 12. The method of claim 2, the evaluating the set of features using the trained traffic obstruction identification model comprising: classifying the road segment as having or not having the traffic obstruction based upon values of features within the set of features and one or more parameters of the trained traffic obstruction identification model. 13. The method of claim 2, the set of features independent of a sampling rate of the dataset. 14. A computer readable medium comprising instructions which when executed perform a method for determining whether a road segment has a traffic obstruction, comprising: obtaining a dataset comprising vehicle location points; extracting a set of features from the dataset based upon the vehicle location points, the set of features indicative of traffic flow patterns; and evaluating the set of features using a trained traffic obstruction identification model to determine whether a road segment has a traffic obstruction. 15. The method of claim 14, comprising: determining whether a current traffic flow pattern is a result of congestion or the traffic obstruction based upon whether the road segment has the traffic obstruction. 16. The method of claim 14, the extracting a set of features comprising: evaluating the vehicle location points to identify a count of vehicles having speeds below a speed threshold; and comparing the count of vehicles to a total count of vehicles to determine a vehicle speed feature for inclusion within the set of features. 17. The method of claim 14, the extracting a set of features comprising: evaluating the vehicle location points to determine a median speed; and identifying a standard deviation from the median speed to determine a median average deviation feature for inclusion within the set of training feature. 18. The method of claim 14, the extracting a set of features comprising: identifying a first count of vehicle location points within a first road segment; and comparing the first count of vehicle location points to counts of vehicle location points within one or more neighboring road segments to determine a relative point density feature for inclusion within the set of features. 19. A system for training a traffic obstruction identification model, comprising: a model training component configured to: obtain a training dataset comprising sample vehicle location points and traffic obstruction identification labels; extract a set of training features from the training dataset based upon the sample vehicle location points, the set of training features indicative of traffic flow patterns; and train a traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create a trained traffic obstruction identification model for identifying traffic obstructions based upon vehicle location point data. 20. The system of claim 19, comprising: a traffic obstruction identification component configured to: obtain a dataset comprising vehicle location points; extract a set of features from the dataset based upon the vehicle location points, the set of features indicative of traffic flow patterns; and evaluate the set of features using the trained traffic obstruction identification model to determine whether a road segment has a traffic obstruction.