| Solver | Description | CPU Parallel |
|---|---|---|
MARKOV_CHAIN | Matches sample points to the graph using the Hidden Markov Model (HMM)-based method, which conducts a range-tree closest-edge search to find the best combinations of possible road segments for each sample point to create the best route. The route is secured one point at a time, so the prediction is corrected after each point. This solution type is the most accurate, but also the most computationally intensive. | X |
MATCH_BATCH_SOLVES | Matches each provided sample source and destination pair using the shortest path between the points. | X |
MATCH_CHARGING_STATIONS | Matches a given sample source and destination pair to the optimal recharging stations along the route (for EVs). | X |
MATCH_CLUSTERS | Matches the graph nodes with a cluster index using the Louvain clustering algorithm. Parallel running of this solver is experimental and can be invoked with the parallel_clustering option. | X* |
MATCH_LOOPS | Matches closed loops (Eulerian paths) originating and ending at each graph node between min and max hops (levels). | X |
MATCH_OD_PAIRS | Matches sample points to find the most probable path between origin and destination (OD) pairs with given cost constraints. | X |
MATCH_SIMILARITY | Computes the Jaccard similarity between vertex pairs and N-level intersections within M hops. | X |
MATCH_SUPPLY_DEMAND | Matches sample generic supply depots to generic demand points using abstract transportation (referred to as trucks). Each route is determined by a truck’s ability (size) to service demand at each demand point. | X |