Traffic obstruction detection

A computer readable medium comprising instructions which when executed perform a method for determining a category of traffic obstruction present on a first road segment, 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 of road segments;
• and
• evaluating the set of features using a trained traffic obstruction identification model created from traffic obstruction identification labels corresponding to traffic obstructions configured to control a flow of traffic and disposed at fixed locations along the road segments to determine whether the first road segment, that is not part of the road segments, has a first category of traffic obstruction or a second category of traffic obstruction, wherein the evaluating comprises, for the first road segment:
• comparing a traffic flow pattern of the first road segment to the traffic flow patterns of the road segments;
• classifying the first road segment has having the first category of traffic obstruction when the traffic flow pattern of the first road segment matches the traffic flow pattern of a second road segment of the road segments having the first category of traffic obstruction;
• and
• classifying the first road segment has having the second category of traffic obstruction when the traffic flow pattern of the first road segment matches the traffic flow pattern of a third road segment of the road segments having the second category of traffic obstruction.

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, wherein the traffic obstruction identification labels correspond to traffic obstructions configured to control a flow of traffic and disposed at fixed locations along road segments;
• 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, the traffic flow patterns indicative of a flow of traffic along a first set of one or more road segments;
• and
• train the 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 classifying uncategorized traffic obstructions along a second set of one or more road segments different than the first set of one or more road segments into one or more categories of traffic obstructions based upon traffic flow patterns of vehicles encountering the uncategorized traffic obstructions along the second set of one or more road segments.