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The Federal Highway Administration (FHWA) sought analysis against conflating NG911, state and other road datasets. They needed such to calculate intersection match scores and road match scores. The focus was on proximity, orientation and length in range ratings. The goal was to see if conflating intersections and roads could align geometries and acceleration data flow integration.

Problem and Solution

Xentity supported python-based dataset analysis to identify and evaluate the DOT LRS dataset and NG911 roads centerline. Both required identification of the pairs of features in each of the two datasets. Also, both would be conflated with each other. FHWA also needed a matrix of conflation complexity vs location type built. Furthermore, when conflating the intersection and roads to align the geometry, the algorithm created needed to be based on the classification of features based on the rating and score.

Xentity first conducted an evaluation and pre-process to prepare the data. Next, we created road feature pairs for comparison and conflation. Various intersection types and geometric factors were QA/QC’d: Remove Curves Arcs from submitted centerlines, dangles, driveways, bifurcations and other QC clean-ups.

We used three different methods: a spatial join source point feature class, a detect feature changes tool, and matching pairs of road intersection points. After the evaluation was complete, Xentity undertook geometry to determine the degree of matching between two edges. Finally, we worked to establish the computed overall weight road geometry. The analysts computed road feature classification matrix for road proximity, intersection match, relative road orientation, road length in range, etc. 

Outcome and Benefit

Upon completion of the aforementioned actions to understand geometry tests to determine the degree of matching between two edges, it was determined due to the lack of standards across varying intersection types at roundabouts, dual carriageways, ramps, etc., the road conflation effort would require much more work to see 100% geometrical alignment, as per their goal for the sake of accuracy.