Match Graph

Matches a directed route implied by a given set of latitude/longitude points to an existing underlying road network graph using a given solution type.

IMPORTANT: It's highly recommended that you review the Graphs & Solvers concepts documentation, the Graph REST Tutorial, and/or some /match/graph examples before using this endpoint.

Input Parameter Description

Name

Type

Description

graph_name

string

Name of the underlying geospatial graph resource to match to using input parameter sample_points.

sample_points

array of strings

Sample points used to match to an underlying geospatial graph. Sample points must be specified using identifiers; identifiers are grouped as combinations. Identifiers can be used with: existing column names, e.g., 'table.column AS SAMPLE_X'; expressions, e.g., 'ST_MAKEPOINT(table.x, table.y) AS SAMPLE_WKTPOINT'; or constant values, e.g., '{1, 2, 10} AS SAMPLE_TRIPID'.

solve_method

string

The type of solver to use for graph matching. The default value is markov_chain.

Supported Values	Description
markov_chain	Matches input parameter 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 (num_segments) for each sample point to create the best route. The route is secured one point at a time while looking ahead chain_width number of points, so the prediction is corrected after each point. This solution type is the most accurate but also the most computationally intensive. Related options: num_segments and chain_width.
match_od_pairs	Matches input parameter sample_points to find the most probable path between origin and destination pairs with cost constraints.
match_supply_demand	Matches input parameter sample_points to optimize scheduling multiple supplies (trucks) with varying sizes to varying demand sites with varying capacities per depot. Related options: partial_loading and max_combinations.
match_batch_solves	Matches input parameter sample_points source and destination pairs for the shortest path solves in batch mode.
match_loops	Matches closed loops (Eulerian paths) originating and ending at each graph node within min and max hops (levels).
match_charging_stations	Matches an optimal path across a number of ev-charging stations between source and target locations.
match_similarity	Matches the intersection set(s) by computing the Jaccard similarity score between node pairs.
match_pickup_dropoff	Matches the pickups and dropoffs by optimizing the total trip costs
match_clusters	Matches the graph nodes with a cluster index using Louvain clustering algorithm
match_pattern	Matches a pattern in the graph
match_embedding	Creates vector node embeddings

solution_table

string

The name of the table used to store the results, in [schema_name.]table_name format, using standard name resolution rules and meeting table naming criteria. This table contains a track of geospatial points for the matched portion of the graph, a track ID, and a score value. Also outputs a details table containing a trip ID (that matches the track ID), the latitude/longitude pair, the timestamp the point was recorded at, and an edge ID corresponding to the matched road segment. Must not be an existing table of the same name. The default value is ''.

options

map of string to strings

Additional parameters. The default value is an empty map ( {} ).

Supported Parameters (keys)

Parameter Description

gps_noise

GPS noise value (in meters) to remove redundant sample points. Use -1 to disable noise reduction. The default value accounts for 95% of point variation (+ or -5 meters). The default value is '5.0'.

num_segments

Maximum number of potentially matching road segments for each sample point. For the markov_chain solver, the default is 3. The default value is '3'.

search_radius

Maximum search radius used when snapping sample points onto potentially matching surrounding segments. The default value corresponds to approximately 100 meters. The default value is '0.001'.

chain_width

For the markov_chain solver only. Length of the sample points lookahead window within the Markov kernel; the larger the number, the more accurate the solution. The default value is '9'.

source

Optional WKT starting point from input parameter sample_points for the solver. The default behavior for the endpoint is to use time to determine the starting point. The default value is 'POINT NULL'.

destination

Optional WKT ending point from input parameter sample_points for the solver. The default behavior for the endpoint is to use time to determine the destination point. The default value is 'POINT NULL'.

partial_loading

For the match_supply_demand solver only. When false (non-default), trucks do not off-load at the demand (store) side if the remainder is less than the store's need The default value is true.

Supported Values	Description
true	Partial off-loading at multiple store (demand) locations
false	No partial off-loading allowed if supply is less than the store's demand.

max_combinations

For the match_supply_demand solver only. This is the cutoff for the number of generated combinations for sequencing the demand locations - can increase this up to 2M. The default value is '10000'.

max_supply_combinations

For the match_supply_demand solver only. This is the cutoff for the number of generated combinations for sequencing the supply locations if/when 'permute_supplies' is true. The default value is '10000'.

left_turn_penalty

This will add an additonal weight over the edges labelled as 'left turn' if the 'add_turn' option parameter of the Create Graph was invoked at graph creation. The default value is '0.0'.

right_turn_penalty

This will add an additonal weight over the edges labelled as' right turn' if the 'add_turn' option parameter of the Create Graph was invoked at graph creation. The default value is '0.0'.

intersection_penalty

This will add an additonal weight over the edges labelled as 'intersection' if the 'add_turn' option parameter of the Create Graph was invoked at graph creation. The default value is '0.0'.

sharp_turn_penalty

This will add an additonal weight over the edges labelled as 'sharp turn' or 'u-turn' if the 'add_turn' option parameter of the Create Graph was invoked at graph creation. The default value is '0.0'.

aggregated_output

For the match_supply_demand solver only. When it is true (default), each record in the output table shows a particular truck's scheduled cumulative round trip path (MULTILINESTRING) and the corresponding aggregated cost. Otherwise, each record shows a single scheduled truck route (LINESTRING) towards a particular demand location (store id) with its corresponding cost. The default value is 'true'.

output_tracks

For the match_supply_demand solver only. When it is true (non-default), the output will be in tracks format for all the round trips of each truck in which the timestamps are populated directly from the edge weights starting from their originating depots. The default value is 'false'.

max_trip_cost

For the match_supply_demand and match_pickup_dropoff solvers only. If this constraint is greater than zero (default) then the trucks/rides will skip travelling from one demand/pick location to another if the cost between them is greater than this number (distance or time). Zero (default) value means no check is performed. The default value is '0.0'.

filter_folding_paths

For the markov_chain solver only. When true (non-default), the paths per sequence combination is checked for folding over patterns and can significantly increase the execution time depending on the chain width and the number of gps samples. The default value is false.

Supported Values	Description
true	Filter out the folded paths.
false	Do not filter out the folded paths

unit_unloading_cost

For the match_supply_demand solver only. The unit cost per load amount to be delivered. If this value is greater than zero (default) then the additional cost of this unit load multiplied by the total dropped load will be added over to the trip cost to the demand location. The default value is '0.0'.

max_num_threads

For the markov_chain solver only. If specified (greater than zero), the maximum number of threads will not be greater than the specified value. It can be lower due to the memory and the number cores available. Default value of zero allows the algorithm to set the maximal number of threads within these constraints. The default value is '0'.

service_limit

For the match_supply_demand solver only. If specified (greater than zero), any supply actor's total service cost (distance or time) will be limited by the specified value including multiple rounds (if set). The default value is '0.0'.

enable_reuse

For the match_supply_demand solver only. If specified (true), all supply actors can be scheduled for second rounds from their originating depots. The default value is false.

Supported Values	Description
true	Allows reusing supply actors (trucks, e.g.) for scheduling again.
false	Supply actors are scheduled only once from their depots.

max_stops

For the match_supply_demand solver only. If specified (greater than zero), a supply actor (truck) can at most have this many stops (demand locations) in one round trip. Otherwise, it is unlimited. If 'enable_truck_reuse' is on, this condition will be applied separately at each round trip use of the same truck. The default value is '0'.

service_radius

For the match_supply_demand and match_pickup_dropoff solvers only. If specified (greater than zero), it filters the demands/picks outside this radius centered around the supply actor/ride's originating location (distance or time). The default value is '0.0'.

permute_supplies

For the match_supply_demand solver only. If specified (true), supply side actors are permuted for the demand combinations during msdo optimization - note that this option increases optimization time significantly - use of 'max_combinations' option is recommended to prevent prohibitively long runs The default value is true.

Supported Values	Description
true	Generates sequences over supply side permutations if total supply is less than twice the total demand
false	Permutations are not performed, rather a specific order of supplies based on capacity is computed

batch_tsm_mode

For the match_supply_demand solver only. When enabled, it sets the number of visits on each demand location by a single salesman at each trip is considered to be (one) 1, otherwise there is no bound. The default value is false.

Supported Values	Description
true	Sets only one visit per demand location by a salesman (tsm mode)
false	No preset limit (usual msdo mode)

round_trip

For the match_supply_demand solver only. When enabled, the supply will have to return back to the origination location. The default value is true.

Supported Values	Description
true	The optimization is done for trips in round trip manner always returning to originating locations
false	Supplies do not have to come back to their originating locations in their routes. The routes are considered finished at the final dropoff.

num_cycles

For the match_clusters solver only. Terminates the cluster exchange iterations across 2-step-cycles (outer loop) when quality does not improve during iterations. The default value is '10'.

num_loops_per_cycle

For the match_clusters and match_embedding solvers only. Terminates the cluster exchanges within the first step iterations of a cycle (inner loop) unless convergence is reached. The default value is '10'.

num_output_clusters

For the match_clusters solver only. Limits the output to the top 'num_output_clusters' clusters based on density. Default value of zero outputs all clusters. The default value is '0'.

max_num_clusters

For the match_clusters and match_embedding solvers only. If set (value greater than zero), it terminates when the number of clusters goes below than this number. For embedding solver the default is 8. The default value is '0'.

cluster_quality_metric

For the match_clusters solver only. The quality metric for Louvain modularity optimization solver. The default value is girvan.

Supported Values	Description
girvan	Uses the Newman Girvan quality metric for cluster solver
spectral	Applies recursive spectral bisection (RSB) partitioning solver

restricted_type

For the match_supply_demand solver only. Optimization is performed by restricting routes labeled by 'MSDO_ODDEVEN_RESTRICTED' only for this supply actor (truck) type The default value is none.

Supported Values	Description
odd	Applies odd/even rule restrictions to odd tagged vehicles.
even	Applies odd/even rule restrictions to even tagged vehicles.
none	Does not apply odd/even rule restrictions to any vehicles.

server_id

Indicates which graph server(s) to send the request to. Default is to send to the server, amongst those containing the corresponding graph, that has the most computational bandwidth. The default value is ''.

inverse_solve

For the match_batch_solves solver only. Solves source-destination pairs using inverse shortest path solver. The default value is false.

Supported Values	Description
true	Solves using inverse shortest path solver.
false	Solves using direct shortest path solver.

min_loop_level

For the match_loops solver only. Finds closed loops around each node deducible not less than this minimal hop (level) deep. The default value is '0'.

max_loop_level

For the match_loops solver only. Finds closed loops around each node deducible not more than this maximal hop (level) deep. The default value is '5'.

search_limit

For the match_loops solver only. Searches within this limit of nodes per vertex to detect loops. The value zero means there is no limit. The default value is '10000'.

output_batch_size

For the match_loops solver only. Uses this value as the batch size of the number of loops in flushing(inserting) to the output table. The default value is '1000'.

charging_capacity

For the match_charging_stations solver only. This is the maximum ev-charging capacity of a vehicle (distance in meters or time in seconds depending on the unit of the graph weights). The default value is '300000.0'.

charging_candidates

For the match_charging_stations solver only. Solver searches for this many number of stations closest around each base charging location found by capacity. The default value is '10'.

charging_penalty

For the match_charging_stations solver only. This is the penalty for full charging. The default value is '30000.0'.

max_hops

For the match_similarity and match_embedding solvers only. Searches within this maximum hops for source and target node pairs to compute the Jaccard scores. The default value is '3'.

traversal_node_limit

For the match_similarity solver only. Limits the traversal depth if it reaches this many number of nodes. The default value is '1000'.

paired_similarity

For the match_similarity solver only. If true, it computes Jaccard score between each pair, otherwise it will compute Jaccard from the intersection set between the source and target nodes The default value is true. The supported values are:

true
false

force_undirected

For the match_pattern and match_embedding solvers only. Pattern matching will be using both pattern and graph as undirected if set to true. The default value is false. The supported values are:

true
false

max_vector_dimension

For the match_embedding solver only. Limits the number of dimensions in node vector embeddings. The default value is '1000'.

optimize_embedding_weights

For the match_embedding solvers only. Solves to find the optimal weights per sub feature in vector emdeddings. The default value is false. The supported values are:

true
false

embedding_weights

For the match_embedding solver only. User specified weights per sub feature in vector embeddings. The string contains the comma separated float values for each sub-feature in the vector space. These values will ONLY be used if 'optimize_embedding_weights' is false. The default value is '1.0,1.0,1.0,1.0'.

optimization_sampling_size

For the match_embedding solver only. Sets the number of random nodes from the graph for solving the weights using stochastic gradient descent. The default value is '1000'.

optimization_max_iterations

For the match_embedding solver only. When the iterations (epochs) for the convergence of the stochastic gradient descent algorithm reaches this number it bails out unless relative error between consecutive iterations is below the 'optimization_error_tolerance' option. The default value is '1000'.

optimization_error_tolerance

For the match_embedding solver only. When the relative error between all of the weights' consecutive iterations falls below this threshold the optimization cycle is interrupted unless the number of iterations reaches the limit set by the option 'max_optimization_iterations'. The default value is '0.001'.

optimization_iteration_rate

For the match_embedding solver only. It is otherwise known as the learning rate, which is the proportionality constant in fornt of the gradient term in successive iterations. The default value is '0.3'.

Output Parameter Description

Name	Type	Description
result	boolean	Indicates a successful solution.
match_score	float	The mean square error calculation representing the map matching score. Values closer to zero are better.
info	map of string to strings	Additional information.