Network Analysis — Tool Reference

Build Network Topology

Function name: build_network_topology

No help documentation available for this tool.

Closest Facility Network

Function name: closest_facility_network

Experimental

Finds the minimum-cost network route from each incident point to its nearest reachable facility point.

vector network closest-facility routing

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp incidentsIncident/demand point layer.Requiredincidents.shp facilitiesFacility/supply point layer.Requiredfacilities.shp snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance from incident/facility points to nearest network node.Optional— edge_cost_fieldOptional numeric line field used as an impedance multiplier for segment length.Optional— one_way_fieldOptional line field marking one-way digitized edges (true/1/yes means from first to second vertex only).Optional— blocked_fieldOptional line field marking blocked/closed edges to exclude from routing (true/1/yes blocks).Optional— barriersOptional barrier point layer; nearest network nodes are blocked from traversal.Optional— barrier_snap_distanceOptional max distance from each barrier point to a network node for blocking.Optional— turn_penaltyOptional additive cost applied to non-straight turns at network nodes.Optional— u_turn_penaltyOptional additive cost applied to U-turn transitions.Optional— forbid_u_turnsIf true, disallow U-turn transitions.Optional— forbid_left_turnsIf true, disallow left-turn transitions.Optional— forbid_right_turnsIf true, disallow right-turn transitions.Optional— turn_restrictions_csvOptional CSV of turn transitions using columns prev_x,prev_y,node_x,node_y,next_x,next_y. Optional columns: forbidden (default true when no turn_cost column is provided) and turn_cost (or penalty/cost/extra_cost) for per-turn additive cost.Optional— temporal_cost_profileOptional CSV defining time-dependent edge costs (columns: edge_id,dow,start_minute,end_minute,value).Optional— temporal_edge_id_fieldOptional network field used to match temporal_cost_profile edge_id values (default EDGE_ID).Optional— departure_timeOptional RFC3339 departure time used for temporal profile lookup.Optional— temporal_modeOptional temporal interpretation mode: multiplier or absolute.Optional— temporal_fallbackOptional fallback when temporal row is missing: static_cost or error.Optional— temporal_profile_reportOptional JSON output path for temporal profile diagnostics (coverage, unmatched edges, fallback usage).Optional— outputOutput closest-facility route line vector path.Required—

Examples

Routes each incident point to the nearest reachable facility by network cost. wbe.closest_facility_network(facilities='facilities.shp', incidents='incidents.shp', input='network.shp', output='closest_facility_routes.shp')

Emergency Scenario Routing And Accessibility Simulator

Function name: emergency_scenario_routing_and_accessibility_simulator

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Emergency Accessibility Scenario Planning

Who It Is For

Emergency management teams planning resilience under flood/fire/closure scenarios.
Public safety routing analysts evaluating critical-facility reachability under disruptions.

Primary User

Emergency management, public safety operations, and municipal resilience planning teams.

What It Does

Simulates emergency network accessibility under multiple disruption scenarios.
Compares scenario accessibility against baseline service-area coverage.
Supports scenario-specific blocked-edge simulation using network attribute mapping.
Outputs baseline and worst-case service areas, scenario KPI table, and simulation report JSON.

How It Works

Runs baseline merged multi-ring service areas from critical facilities using network_service_area.
Reads scenario CSV (scenario_id,max_cost_multiplier[,blocked_value]).
For each scenario:
Scales max travel cost by max_cost_multiplier.
Optionally maps blocked_value to scenario-blocked edges using scenario_block_source_field.
Scope boundary: Emergency Accessibility Scenario Planning simulates disrupted-network conditions for critical facility coverage — it is the service-area tool for emergency resilience and response planning. Service Area Planning and Coverage Optimization (6.2) addresses public infrastructure coverage under normal operating conditions. Market Access and Site Planning (6.7) evaluates commercial expansion by drive-time access and competitive positioning. Choose this tool for resilience analysis, 6.2 for infrastructure coverage gaps, and 6.7 for commercial site decisions.
Computes scenario service area polygons and demand-point coverage percent.
Computes delta_from_baseline_pct per scenario and identifies best/worst scenario outcomes.

Inputs

ParameterTypeRequiredDescription networkLineVector pathRequiredNetwork layer for routing critical_facilitiesPointVector pathRequiredOrigin facilities (hospitals/fire/EMS/etc.) demand_pointsPointVector pathOptionalDemand points for scenario coverage KPIs ring_costsarray[float]RequiredService area ring costs (e.g., [5,10,15]) scenario_csvpathRequiredCSV: scenario_id,max_cost_multiplier[,blocked_value] scenario_templatestringOptionalScenario authoring template: custom | flood | wildfire | earthquake; applies template guardrails scenario_block_source_fieldstringOptionalNetwork attribute used to match scenario blocked_value baseline_service_areasvector pathRequiredOutput baseline service areas worst_case_service_areasvector pathRequiredOutput worst-scenario service areas scenario_summary_csvpathRequiredOutput scenario KPI summary CSV simulation_reportpathRequiredOutput simulation summary JSON

Outputs

OutputTypeContents baseline_service_areasVectorBaseline merged service area polygons worst_case_service_areasVectorWorst-performing scenario service area polygons scenario_summary_csvCSVscenario_id, blocked_value, covered_pct, delta_from_baseline_pct and related KPIs simulation_reportJSONbaseline stats, scenario comparisons, best/worst scenario summary

Python Example

`env = WbEnvironment(license_tier="pro")

result = env.run_tool("emergency_scenario_routing_and_accessibility_simulator", network="city_network.gpkg", critical_facilities="critical_facilities.gpkg", demand_points="demand_points.gpkg", ring_costs=[5, 10, 15], scenario_csv="scenarios.csv", scenario_block_source_field="STATUS", baseline_service_areas="output/baseline_service_areas.gpkg", worst_case_service_areas="output/worst_service_areas.gpkg", scenario_summary_csv="output/scenario_summary.csv", simulation_report="output/simulation_report.json", )

print(result)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Fleet Routing And Dispatch Optimizer

Function name: fleet_routing_and_dispatch_optimizer

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Fleet Routing and Dispatch Optimization

Who It Is For

Logistics dispatch teams planning daily vehicle assignments.
Municipal waste and field-maintenance operations with constrained fleet capacity/time.
Courier and distribution operations requiring transparent route exceptions.

Primary User

Logistics operations leaders, municipal service planners, and fleet-dispatch platform teams.

What It Does

Optimizes vehicle dispatch plans across depots and stops for logistics and field operations.
Builds feasible routes under capacity and shift-time constraints.
Produces assignment, KPI, and exception outputs for operations review.
Supports objective mode selection (minimize_distance, minimize_time, minimize_cost, balanced).

How It Works

Parses depot and stop layers plus fleet specifications from vehicles_csv.
Harmonizes depot/stop CRS to the network CRS when EPSG metadata is available and validates projected CRS for distance/time calculations.
Filters infeasible stops where demand exceeds max vehicle capacity.
Applies objective-aware greedy nearest-feasible stop construction for initial route assignment (distance, time, cost, balanced).
Applies optional edge restrictions from CSV (from_x,from_y,to_x,to_y,closed,penalty_factor) as closures or impedance penalties.
Runs local 2-opt sequence refinement for incremental route improvement.
Computes per-route and fleet KPIs and emits exceptions with reason codes.

Inputs

ParameterTypeRequiredDescription networkLineVector pathRequiredStreet/network layer used by routing workflow depotsPointVector pathRequiredDepot or start/end locations for vehicles stopsPointVector pathRequiredStops/tasks to assign to routes vehicles_csvpathRequiredFleet specs (vehicle_id, capacity, available_time_minutes, cost_per_minute, cost_per_km, depot_id) objectivestringOptionalObjective mode: minimize_distance, minimize_time, minimize_cost, balanced restrictionspathOptionalOptional restrictions CSV (from_x,from_y,to_x,to_y[,closed][,penalty_factor]) used for edge closures or impedance penalties routes_outputvector pathRequiredOutput route vector path assignment_csv_outputpathRequiredOutput stop-to-route assignment CSV route_kpis_csv_outputpathRequiredOutput per-route/fleet KPI CSV exceptions_csv_outputpathRequiredOutput infeasible stop diagnostics CSV

Outputs

OutputTypeContents routes_outputVectorRoute geometries and route-level summaries by vehicle assignment_csv_outputCSVstop_id, route_id, vehicle_id, sequence_order, arrival/departure times route_kpis_csv_outputCSVPer-route metrics and fleet roll-up summary exceptions_csv_outputCSVstop_id with reason code (demand_exceeds_max_vehicle_capacity, no_feasible_route)

Python Example

`env = WbEnvironment(license_tier="pro")

result = env.run_tool("fleet_routing_and_dispatch_optimizer", network="city_network.gpkg", depots="depots.gpkg", stops="daily_stops.gpkg", vehicles_csv="fleet_specs.csv", objective="minimize_cost", routes_output="output/routes.gpkg", assignment_csv_output="output/assignments.csv", route_kpis_csv_output="output/route_kpis.csv", exceptions_csv_output="output/exceptions.csv", )

print(result)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Generate Network Nodes

Function name: generate_network_nodes

No help documentation available for this tool.

K Shortest Paths Network

Function name: k_shortest_paths_network

Experimental

Finds the k shortest simple paths between start and end coordinates over a line network.

vector network k-shortest-paths

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp start_xStart x coordinate.Required0.0 start_yStart y coordinate.Required0.0 end_xEnd x coordinate.Required100.0 end_yEnd y coordinate.Required100.0 kNumber of shortest paths to return.Required3 snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance from start/end coordinates to nearest network node.Optional— edge_cost_fieldOptional numeric line field used as an impedance multiplier for segment length.Optional— one_way_fieldOptional line field marking one-way digitized edges (true/1/yes means from first to second vertex only).Optional— blocked_fieldOptional line field marking blocked/closed edges to exclude from routing (true/1/yes blocks).Optional— barriersOptional barrier point layer; nearest network nodes are blocked from traversal.Optional— barrier_snap_distanceOptional max distance from each barrier point to a network node for blocking.Optional— turn_penaltyOptional additive cost applied to non-straight turns at network nodes.Optional— u_turn_penaltyOptional additive cost applied to U-turn transitions.Optional— forbid_u_turnsIf true, disallow U-turn transitions.Optional— forbid_left_turnsIf true, disallow left-turn transitions.Optional— forbid_right_turnsIf true, disallow right-turn transitions.Optional— turn_restrictions_csvOptional CSV of turn transitions using columns prev_x,prev_y,node_x,node_y,next_x,next_y. Optional columns: forbidden (default true when no turn_cost column is provided) and turn_cost (or penalty/cost/extra_cost) for per-turn additive cost.Optional— temporal_cost_profileOptional CSV defining time-dependent edge costs (columns: edge_id,dow,start_minute,end_minute,value).Optional— temporal_edge_id_fieldOptional network field used to match temporal_cost_profile edge_id values (default EDGE_ID).Optional— departure_timeOptional RFC3339 departure time used for temporal profile lookup.Optional— temporal_modeOptional temporal interpretation mode: multiplier or absolute.Optional— temporal_fallbackOptional fallback when temporal row is missing: static_cost or error.Optional— temporal_profile_reportOptional JSON output path for temporal profile diagnostics (coverage, unmatched edges, fallback usage).Optional— outputOutput line vector path.Required—

Examples

Computes multiple alternative simple paths between two points on a line network. wbe.k_shortest_paths_network(end_x=100.0, end_y=100.0, input='network.shp', k=3, output='k_shortest_paths.shp', start_x=0.0, start_y=0.0)

Location Allocation Network

Function name: location_allocation_network

Experimental

Selects k facilities and allocates demand points by network cost with greedy or exact solving, optional capacities, and required/forbidden candidate constraints.

vector network location-allocation allocation

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp demand_pointsDemand point layer to allocate.Requireddemand.shp facilitiesCandidate facility point layer.Requiredfacilities.shp facility_countNumber of facilities to select (k).Required2 solver_modeSolver mode: auto, greedy, or exact (exact is intended for smaller problems).Optional— demand_weight_fieldOptional numeric demand weight field in demand_points (default weight=1).Optional— facility_capacity_fieldOptional numeric capacity field in facilities; capacity is consumed by demand_weight_field values.Optional— required_facility_fieldOptional boolean facility field marking candidates that must be selected.Optional— forbidden_facility_fieldOptional boolean facility field marking candidates that must not be selected.Optional— snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance from demand/facility points to nearest network node.Optional— edge_cost_fieldOptional numeric line field used as an impedance multiplier for segment length.Optional— one_way_fieldOptional line field marking one-way digitized edges (true/1/yes means from first to second vertex only).Optional— blocked_fieldOptional line field marking blocked/closed edges to exclude from routing (true/1/yes blocks).Optional— barriersOptional barrier point layer; nearest network nodes are blocked from traversal.Optional— barrier_snap_distanceOptional max distance from each barrier point to a network node for blocking.Optional— turn_penaltyOptional additive cost applied to non-straight turns at network nodes.Optional— u_turn_penaltyOptional additive cost applied to U-turn transitions.Optional— forbid_u_turnsIf true, disallow U-turn transitions.Optional— forbid_left_turnsIf true, disallow left-turn transitions.Optional— forbid_right_turnsIf true, disallow right-turn transitions.Optional— turn_restrictions_csvOptional CSV of turn transitions using columns prev_x,prev_y,node_x,node_y,next_x,next_y. Optional columns: forbidden (default true when no turn_cost column is provided) and turn_cost (or penalty/cost/extra_cost) for per-turn additive cost.Optional— temporal_cost_profileOptional CSV defining time-dependent edge costs (columns: edge_id,dow,start_minute,end_minute,value).Optional— temporal_edge_id_fieldOptional network field used to match temporal_cost_profile edge_id values (default EDGE_ID).Optional— departure_timeOptional RFC3339 departure time used for temporal profile lookup.Optional— temporal_modeOptional temporal interpretation mode: multiplier or absolute.Optional— temporal_fallbackOptional fallback when temporal row is missing: static_cost or error.Optional— temporal_profile_reportOptional JSON output path for temporal profile diagnostics (coverage, unmatched edges, fallback usage).Optional— outputOutput allocated route line vector path.Required—

Examples

Selects facilities and allocates demand points using network travel cost. wbe.location_allocation_network(demand_points='demand.shp', facilities='facilities.shp', facility_count=2, input='network.shp', output='location_allocation_routes.shp')

Map Matching v1

Function name: map_matching_v1

Experimental

Snaps trajectory points onto a line network and reconstructs an inferred route with diagnostics.

vector network map-matching

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp trajectory_pointsInput trajectory point layer.Requiredtrajectory_points.shp timestamp_fieldTrajectory field used for time ordering.Requiredtimestamp search_radiusOptional candidate search radius around each trajectory point.Optional25.0 candidate_kOptional number of nearest candidates retained per point.Optional5 snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance for snapping trajectory points to network nodes.Optional— edge_cost_fieldOptional numeric line field used as an impedance multiplier for segment length.Optional— one_way_fieldOptional line field marking one-way digitized edges (true/1/yes means from first to second vertex only).Optional— blocked_fieldOptional line field marking blocked/closed edges to exclude from routing (true/1/yes blocks).Optional— barriersOptional barrier point layer; nearest network nodes are blocked from traversal.Optional— barrier_snap_distanceOptional max distance from each barrier point to a network node for blocking.Optional— turn_penaltyOptional additive cost applied to non-straight turns at network nodes.Optional— u_turn_penaltyOptional additive cost applied to U-turn transitions.Optional— forbid_u_turnsIf true, disallow U-turn transitions.Optional— forbid_left_turnsIf true, disallow left-turn transitions.Optional— forbid_right_turnsIf true, disallow right-turn transitions.Optional— turn_restrictions_csvOptional CSV of turn transitions using columns prev_x,prev_y,node_x,node_y,next_x,next_y. Optional columns: forbidden (default true when no turn_cost column is provided) and turn_cost (or penalty/cost/extra_cost) for per-turn additive cost.Optional— matched_points_outputOptional output vector path for per-point diagnostics.Optional— match_reportOptional JSON output path for summary diagnostics.Optional— outputOutput line vector path for inferred route.Required—

Examples

Matches time-ordered trajectory points to a network and emits route and diagnostics outputs. wbe.map_matching_v1(candidate_k=5, input='network.shp', matched_points_output='matched_points.shp', output='matched_route.shp', search_radius=25.0, timestamp_field='timestamp', trajectory_points='trajectory_points.shp')

Market Access And Site Intelligence Workflow

Function name: market_access_and_site_intelligence_workflow

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Market Access and Site Planning

Who It Is For

Retail chains evaluating expansion locations based on demand accessibility and competitive saturation.
Healthcare networks rating candidate clinic/hospital sites for market coverage and competitive positioning.
Franchise developers assessing new territory opportunities via catchment analysis.

Primary User

Commercial real estate, retail operations, healthcare network planning, franchise development.

What It Does

Evaluates candidate site locations for commercial expansion (retail, healthcare, franchise).
Computes drive-time catchment areas for each candidate.
Measures demand coverage and competitive overlap positioning.
Ranks candidates by composite score: 50% demand coverage + 25% accessibility + 25% low competitive overlap.
Outputs ranked candidates, competitive analysis, and executive summary with decision metrics.
Adds opportunity bands and optional market action queue output for expansion triage.

How It Works

For each candidate site:
Compute demand coverage (% of demand points within max ring cost).
Compute average distance to demand points (accessibility proxy).
Compute competitive overlap vs existing/competitor sites (% within catchment radius).
Existing-site baseline coverage is computed from the existing site layer, not from candidate seed sites.
Accessibility score is normalized across the evaluated candidate set.
Composite rank score = 0.50 × coverage + 0.25 × accessibility + 0.25 × (100 - overlap).
Scope boundary: Market Access and Site Planning evaluates candidate commercial sites for expansion using drive-time access, demand coverage, and competitive positioning — it is the service-area tool for commercial decisions. Service Area Planning and Coverage Optimization (6.2) addresses public infrastructure coverage diagnostics. Emergency Accessibility Scenario Planning (6.6) simulates disrupted-network scenarios for resilience analysis. Choose this tool for commercial expansion, 6.2 for infrastructure planning, and 6.6 for emergency response planning.
Rank candidates by composite score; classify each site as expand_now, pilot, monitor, or saturated; emit top candidates + decision gate (coverage > 70% AND overlap ParameterTypeRequiredDescription networkLineVector pathRequiredStreet/transit network for routing sites_existingPointVector pathRequiredExisting own or benchmark competitive sites sites_candidatesPointVector pathRequiredCandidate expansion site locations demand_surfacePointVector pathRequiredDemand locations (customers, population centroids) competition_sitesPointVector pathOptionalCompetitor locations (separate from own/benchmark) ring_costsarray[float]RequiredDrive-time costs for catchments (e.g., [5, 10, 15]) catchments_outputvector pathRequiredOutput drive-time catchment polygons overlap_analysis_outputvector pathRequiredOutput competitive overlap analysis layer candidate_rank_csvpathRequiredOutput CSV: ranked candidates with KPIs executive_summary_jsonpathRequiredOutput JSON: market metrics and decision gate market_action_queue_csvpathOptionalOptional prioritized expansion action queue CSV

Important input roles: sites_existing is the incumbent baseline used for existing coverage and coverage-gain calculations. competition_sites is an optional separate competitor layer used for overlap pressure. If competition_sites is omitted, overlap falls back to sites_existing.

Outputs

OutputTypeContents catchments_outputVectorDrive-time catchment polygons per candidate overlap_analysis_outputVector/GeoJSONCandidate-level overlap visualization with coverage gain and opportunity band candidate_rank_csvCSVrank, site_id, x, y, demand_coverage_pct, coverage_gain_pct, avg_distance_to_demand, competitive_overlap_pct, accessibility_score, composite_rank_score, opportunity_band executive_summary_jsonJSONtotal_candidates, market_metrics, top_candidates, recommendation, decision_gate, decision_rationale market_action_queue_csvCSVPrioritized expansion actions by candidate/opportunity band

Map interpretation: catchments_output contains one candidate-level polygon per candidate (not separate ring-band polygons). Catchments are convex-hull trade areas of covered demand plus candidate location; if a hull cannot be formed, a small fallback square polygon may be written. overlap_analysis_output is a point layer, and square symbols there are usually marker style.

Python Example

`env = WbEnvironment(license_tier="pro")

result = env.run_tool("market_access_and_site_intelligence_workflow", network="city_network.gpkg", sites_existing="existing_retail.gpkg", sites_candidates="candidate_expansion_sites.gpkg", demand_surface="customer_demand_points.gpkg", competition_sites="competitor_locations.gpkg", ring_costs=[5, 10, 15], catchments_output="output/candidate_catchments.gpkg", overlap_analysis_output="output/competitive_overlap.gpkg", candidate_rank_csv="output/candidate_ranking.csv", executive_summary_json="output/market_summary.json", market_action_queue_csv="output/market_action_queue.csv", )

print(result)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Multimodal OD Cost Matrix

Function name: multimodal_od_cost_matrix

Experimental

Computes batched multimodal OD costs and mode summaries between origin and destination point sets.

vector network multimodal od-matrix

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp originsOrigin point layer.Requiredorigins.shp destinationsDestination point layer.Requireddestinations.shp mode_fieldLine attribute field that identifies travel mode per segment.RequiredMODE snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance from origin/destination points to nearest network node.Optional— default_mode_speedDefault mode speed in coordinate-units per time unit (default: 1).Optional1.0 mode_speed_overridesOptional comma-separated mode:speed overrides (for example: walk:1.4,drive:12,transit:8).Optional— allowed_modesOptional comma-separated allow-list of modes to include in routing.Optional— transfer_penaltyOptional additive penalty applied each time the route changes mode.Optional0.0 temporal_cost_profileOptional CSV defining time-dependent edge costs (columns: edge_id,dow,start_minute,end_minute,value).Optional— temporal_edge_id_fieldOptional network field used to match temporal_cost_profile edge_id values (default EDGE_ID).Optional— departure_timeOptional RFC3339 departure time used for temporal profile lookup.Optional— temporal_modeOptional temporal interpretation mode: multiplier or absolute.Optional— temporal_fallbackOptional fallback when temporal row is missing: static_cost or error.Optional— temporal_profile_reportOptional JSON output path for temporal profile diagnostics when using direct temporal input.Optional— scenario_bundle_csvOptional CSV listing named temporal scenarios for comparative multi-scenario OD output.Optional— outputOutput CSV path.Required—

Examples

Creates a multimodal OD matrix with route cost and mode-sequence summaries. wbe.multimodal_od_cost_matrix(default_mode_speed=1.0, destinations='destinations.shp', input='network.shp', mode_field='MODE', mode_speed_overrides='walk:1.4,transit:8', origins='origins.shp', output='multimodal_od_matrix.csv', transfer_penalty=0.0)

Multimodal Routes From OD

Function name: multimodal_routes_from_od

Experimental

Builds route geometries for multimodal origin-destination point pairs with per-route mode summaries.

vector network multimodal routes

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp originsOrigin point layer.Requiredorigins.shp destinationsDestination point layer.Requireddestinations.shp mode_fieldLine attribute field that identifies travel mode per segment.RequiredMODE snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance from origin/destination points to nearest network node.Optional— default_mode_speedDefault mode speed in coordinate-units per time unit (default: 1).Optional1.0 mode_speed_overridesOptional comma-separated mode:speed overrides (for example: walk:1.4,drive:12,transit:8).Optional— allowed_modesOptional comma-separated allow-list of modes to include in routing.Optional— transfer_penaltyOptional additive penalty applied each time the route changes mode.Optional0.0 temporal_cost_profileOptional CSV defining time-dependent edge costs (columns: edge_id,dow,start_minute,end_minute,value).Optional— temporal_edge_id_fieldOptional network field used to match temporal_cost_profile edge_id values (default EDGE_ID).Optional— departure_timeOptional RFC3339 departure time used for temporal profile lookup.Optional— temporal_modeOptional temporal interpretation mode: multiplier or absolute.Optional— temporal_fallbackOptional fallback when temporal row is missing: static_cost or error.Optional— temporal_profile_reportOptional JSON output path for temporal profile diagnostics when using direct temporal input.Optional— scenario_bundle_csvOptional CSV listing named temporal scenarios for comparative multi-scenario route output.Optional— outputOutput route line vector path.Required—

Examples

Creates route lines for each reachable multimodal origin-destination pair. wbe.multimodal_routes_from_od(default_mode_speed=1.0, destinations='destinations.shp', input='network.shp', mode_field='MODE', mode_speed_overrides='walk:1.4,transit:8', origins='origins.shp', output='multimodal_routes_from_od.gpkg', transfer_penalty=0.0)

Multimodal Shortest Path

Function name: multimodal_shortest_path

Experimental

Finds a mode-aware shortest path over a line network with configurable transfer penalties.

vector network multimodal shortest-path

Parameters

Examples

Routes between two coordinates using mode-aware costs and transfer penalties. wbe.multimodal_shortest_path(default_mode_speed=1.0, end_x=100.0, end_y=100.0, input='network.shp', mode_field='MODE', mode_speed_overrides='walk:1.4,transit:8', output='multimodal_shortest_path.shp', start_x=0.0, start_y=0.0, transfer_penalty=0.0)

Demonstrates walk-drive routing with mode filtering and transfer penalty. wbe.multimodal_shortest_path(allowed_modes='walk,drive', default_mode_speed=1.0, end_x=100.0, end_y=100.0, input='network.shp', mode_field='MODE', mode_speed_overrides='walk:1.4,drive:12', output='multimodal_walk_drive_path.shp', start_x=0.0, start_y=0.0, transfer_penalty=2.0)

Demonstrates walk-transit routing with mode filtering and transfer penalty. wbe.multimodal_shortest_path(allowed_modes='walk,transit', default_mode_speed=1.0, end_x=100.0, end_y=100.0, input='network.shp', mode_field='MODE', mode_speed_overrides='walk:1.4,transit:8', output='multimodal_walk_transit_path.shp', start_x=0.0, start_y=0.0, transfer_penalty=1.0)

Network Accessibility Metrics

Function name: network_accessibility_metrics

Experimental

Computes accessibility indices for origin points based on reachability to destinations with optional impedance cutoffs and decay functions.

vector network accessibility

Parameters

Examples

Computes accessibility index for origins to destinations within cutoff distance. wbe.network_accessibility_metrics(decay_function='none', destinations='destinations.shp', input='network.shp', origins='origins.shp', output='origins_accessibility.shp', snap_tolerance=0.0)

Network Centrality Metrics

Function name: network_centrality_metrics

Experimental

Computes baseline degree, closeness, and betweenness centrality metrics for network nodes.

vector network centrality

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp snap_toleranceOptional node snapping tolerance for graph construction.Optional0.0 edge_cost_fieldOptional numeric line field used as an impedance multiplier for segment length.Optional— one_way_fieldOptional line field marking one-way digitized edges (true/1/yes means from first to second vertex only).Optional— blocked_fieldOptional line field marking blocked/closed edges to exclude from graph construction (true/1/yes blocks).Optional— outputOutput point vector path.Required—

Examples

Computes node-level centrality metrics for a line network. wbe.network_centrality_metrics(input='network.shp', output='network_centrality.gpkg', snap_tolerance=0.0)

Network Connected Components

Function name: network_connected_components

Experimental

Assigns a connected-component ID to each line feature in a network.

vector network components

Parameters

Examples

Labels disconnected subnetworks with unique component IDs. wbe.network_connected_components(input='network.shp', output='network_components.shp', snap_tolerance=0.0)

Network Node Degree

Function name: network_node_degree

Experimental

Extracts network nodes from line features and computes node degree and node type.

vector network topology

Parameters

Examples

Creates a node point layer with network degree attributes. wbe.network_node_degree(input='network.shp', output='network_nodes.shp', snap_tolerance=0.0)

Network OD Cost Matrix

Function name: network_od_cost_matrix

Experimental

Compute origin-destination cost matrix between point pairs. Calculates travel distance or cost along network paths.

vector network od-matrix

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp originsOrigin point layer.Requiredorigins.shp destinationsDestination point layer.Requireddestinations.shp snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance from origin/destination points to nearest network node.Optional— edge_cost_fieldOptional numeric line field used as an impedance multiplier for segment length.Optional— one_way_fieldOptional line field marking one-way digitized edges (true/1/yes means from first to second vertex only).Optional— blocked_fieldOptional line field marking blocked/closed edges to exclude from routing (true/1/yes blocks).Optional— barriersOptional barrier point layer; nearest network nodes are blocked from traversal.Optional— barrier_snap_distanceOptional max distance from each barrier point to a network node for blocking.Optional— turn_penaltyOptional additive cost applied to non-straight turns at network nodes.Optional— u_turn_penaltyOptional additive cost applied to U-turn transitions.Optional— forbid_u_turnsIf true, disallow U-turn transitions.Optional— forbid_left_turnsIf true, disallow left-turn transitions.Optional— forbid_right_turnsIf true, disallow right-turn transitions.Optional— turn_restrictions_csvOptional CSV of turn transitions using columns prev_x,prev_y,node_x,node_y,next_x,next_y. Optional columns: forbidden (default true when no turn_cost column is provided) and turn_cost (or penalty/cost/extra_cost) for per-turn additive cost.Optional— temporal_cost_profileOptional CSV defining time-dependent edge costs (columns: edge_id,dow,start_minute,end_minute,value).Optional— temporal_edge_id_fieldOptional network field used to match temporal_cost_profile edge_id values (default EDGE_ID).Optional— departure_timeOptional RFC3339 departure time used for temporal profile lookup.Optional— temporal_modeOptional temporal interpretation mode: multiplier or absolute.Optional— temporal_fallbackOptional fallback when temporal row is missing: static_cost or error.Optional— temporal_profile_reportOptional JSON output path for temporal profile diagnostics (coverage, unmatched edges, fallback usage).Optional— outputOutput CSV path.Required—

Examples

Creates an OD cost matrix from origins and destinations on a line network. wbe.network_od_cost_matrix(destinations='destinations.shp', input='network.shp', origins='origins.shp', output='od_matrix.csv')

Network Readiness And Diagnostics Intelligence

Function name: network_readiness_and_diagnostics_intelligence

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Network Readiness and Diagnostics

Problem It Solves

Is this network structurally and cost-wise ready for reliable routing and service optimization workflows?

Who It Is For

Routing analysts, municipal transportation planning teams, and utility network operations.

Primary User

Municipal/public works GIS teams, utilities, and logistics operations requiring reproducible network QA gates.

What It Does

Audits line-based transportation/utility networks for operational routing readiness.
Detects dead-end concentration and cost-consistency anomalies before routing runs.
Emits a machine-readable readiness score with pass/fail quality gate and diagnostics outputs.

How It Works

Loads a line network and validates geometry is line-based (LineString/MultiLineString).
Builds node degree counts from line endpoints to quantify dead-end prevalence.
Computes per-segment length costs and assesses variance using z-score outlier detection.
Computes weighted readiness score from connectivity and cost-consistency components.
Indicative formula: overall = 0.6 * connectivity_score + 0.4 * cost_consistency_score.

Why It Wins

Replaces manual topology spot-checks with a reproducible score + diagnostics package that is machine-checkable and reportable.

Typical Buying Trigger

Teams encounter unstable routing outcomes and need an auditable pre-routing network quality gate.

Typical Presets

default: balanced scoring for day-to-day network readiness checks.
pre-routing gate: run before route optimization or service-area generation.
data onboarding QA: run when ingesting new road/utility centerline deliveries.

Inputs

ParameterOptionalDescription networknoInput line network layer (street, transit, or utility network). qa_reportnoOutput CSV path for detailed QA findings and issue counts. diagnostics_layernoOutput GeoJSON/GeoPackage path containing diagnostic geometries. readiness_scorenoOutput JSON path with readiness score, component scores, and pass/fail gate.

Outputs

ParameterTypeDescription qa_reportCSVTabular QA findings including severity, check type, count, and descriptive diagnostics. diagnostics_layerGeoJSON/GeoPackageSpatial diagnostics layer for issue visualization and spatial triage. readiness_scoreJSONMachine-readable readiness contract with overall score, component scores, penalties, and pass/fail gate. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

result = wbe.network_readiness_and_diagnostics_intelligence( network="data/street_network.shp", qa_report="output/network_readiness_qa.csv", diagnostics_layer="output/network_readiness_diagnostics.geojson", readiness_score="output/network_readiness_score.json", )

print(result)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Network Routes From OD

Function name: network_routes_from_od

Experimental

Builds route geometries for origin-destination point pairs over a line network.

vector network routes

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp originsOrigin point layer.Requiredorigins.shp destinationsDestination point layer.Requireddestinations.shp snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance from origin/destination points to nearest network node.Optional— edge_cost_fieldOptional numeric line field used as an impedance multiplier for segment length.Optional— one_way_fieldOptional line field marking one-way digitized edges (true/1/yes means from first to second vertex only).Optional— blocked_fieldOptional line field marking blocked/closed edges to exclude from routing (true/1/yes blocks).Optional— barriersOptional barrier point layer; nearest network nodes are blocked from traversal.Optional— barrier_snap_distanceOptional max distance from each barrier point to a network node for blocking.Optional— turn_penaltyOptional additive cost applied to non-straight turns at network nodes.Optional— u_turn_penaltyOptional additive cost applied to U-turn transitions.Optional— forbid_u_turnsIf true, disallow U-turn transitions.Optional— forbid_left_turnsIf true, disallow left-turn transitions.Optional— forbid_right_turnsIf true, disallow right-turn transitions.Optional— turn_restrictions_csvOptional CSV of turn transitions using columns prev_x,prev_y,node_x,node_y,next_x,next_y. Optional columns: forbidden (default true when no turn_cost column is provided) and turn_cost (or penalty/cost/extra_cost) for per-turn additive cost.Optional— temporal_cost_profileOptional CSV defining time-dependent edge costs (columns: edge_id,dow,start_minute,end_minute,value).Optional— temporal_edge_id_fieldOptional network field used to match temporal_cost_profile edge_id values (default EDGE_ID).Optional— departure_timeOptional RFC3339 departure time used for temporal profile lookup.Optional— temporal_modeOptional temporal interpretation mode: multiplier or absolute.Optional— temporal_fallbackOptional fallback when temporal row is missing: static_cost or error.Optional— temporal_profile_reportOptional JSON output path for temporal profile diagnostics (coverage, unmatched edges, fallback usage).Optional— outputOutput route line vector path.Required—

Examples

Creates route line features for OD point pairs on a network. wbe.network_routes_from_od(destinations='destinations.shp', input='network.shp', origins='origins.shp', output='network_routes.shp')

Network Service Area

Function name: network_service_area

Experimental

Computes reachable network nodes from origin points within a maximum network cost.

vector network service-area

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp originsOrigin point layer.Requiredorigins.shp max_costMaximum reachable path cost.Required1000.0 ring_costsOptional comma-separated ring thresholds for multi-ring outputs (for example: 5,10,15).Optional— snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance from origin points to nearest network node.Optional— output_modeOutput mode: 'nodes' (default), 'edges' for cost-trimmed reachable edge segments, or 'polygons' for per-origin isochrone-like polygons from reachable edge envelopes.Optional— polygon_merge_originsIf true and output_mode='polygons', dissolve overlapping origin polygons into merged coverage per ring instead of emitting one polygon per origin.Optional— mode_fieldOptional line attribute field identifying travel mode per segment; enables mode-aware service-area costs.Optional— default_mode_speedDefault mode speed in coordinate-units per time unit when mode_field is provided (default: 1).Optional— mode_speed_overridesOptional comma-separated mode:speed overrides (for example: walk:1.4,drive:12).Optional— allowed_modesOptional comma-separated allow-list of modes to include when mode_field is provided.Optional— edge_cost_fieldOptional numeric line field used as an impedance multiplier for segment length.Optional— one_way_fieldOptional line field marking one-way digitized edges (true/1/yes means from first to second vertex only).Optional— blocked_fieldOptional line field marking blocked/closed edges to exclude from routing (true/1/yes blocks).Optional— barriersOptional barrier point layer; nearest network nodes are blocked from traversal.Optional— barrier_snap_distanceOptional max distance from each barrier point to a network node for blocking.Optional— turn_penaltyOptional additive cost applied to non-straight turns at network nodes.Optional— u_turn_penaltyOptional additive cost applied to U-turn transitions.Optional— forbid_u_turnsIf true, disallow U-turn transitions.Optional— forbid_left_turnsIf true, disallow left-turn transitions.Optional— forbid_right_turnsIf true, disallow right-turn transitions.Optional— turn_restrictions_csvOptional CSV of turn transitions using columns prev_x,prev_y,node_x,node_y,next_x,next_y. Optional columns: forbidden (default true when no turn_cost column is provided) and turn_cost (or penalty/cost/extra_cost) for per-turn additive cost.Optional— temporal_cost_profileOptional CSV defining time-dependent edge costs (columns: edge_id,dow,start_minute,end_minute,value).Optional— temporal_edge_id_fieldOptional network field used to match temporal_cost_profile edge_id values (default EDGE_ID).Optional— departure_timeOptional RFC3339 departure time used for temporal profile lookup.Optional— temporal_modeOptional temporal interpretation mode: multiplier or absolute.Optional— temporal_fallbackOptional fallback when temporal row is missing: static_cost or error.Optional— temporal_profile_reportOptional JSON output path for temporal profile diagnostics (coverage, unmatched edges, fallback usage).Optional— outputOutput service-area vector path.Required—

Examples

Finds all nodes reachable from origins within max_cost. wbe.network_service_area(input='network.shp', max_cost=1000.0, origins='origins.shp', output='service_area_nodes.shp')

Network Topology Audit

Function name: network_topology_audit

Experimental

Audits a line network for topology anomalies—disconnected components, dead ends, and degree anomalies—that cause routing failures.

vector network diagnostics topology

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp snap_toleranceOptional node snapping tolerance for graph construction.Optional— one_way_fieldOptional line field marking one-way edges for directional analysis.Optional— blocked_fieldOptional line field marking blocked edges to exclude from analysis.Optional— reportOptional JSON output path for the audit summary report.Optional— outputOutput point vector path for per-node diagnostics.Required—

Examples

Writes per-node degree and component diagnostics and a summary JSON report for the input network. wbe.network_topology_audit(input='network.shp', output='network_node_audit.shp', report='audit_report.json')

OD Sensitivity Analysis

Function name: od_sensitivity_analysis

Experimental

Computes OD shortest-path costs with impedance perturbations and outputs sensitivity statistics via Monte Carlo sampling.

vector network sensitivity

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp originsOrigin point layer.Requiredorigins.shp destinationsDestination point layer.Requireddestinations.shp edge_cost_fieldRequired numeric line field used as an impedance multiplier for perturbation analysis.Requiredcost impedance_disturbance_rangeRange for cost perturbation as 'min_factor,max_factor' (e.g., '0.8,1.2' for ±20% variation).Optional0.8,1.2 monte_carlo_samplesNumber of Monte Carlo samples for perturbation analysis (default 1, max 100).Optional10 snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance from origin/destination points to nearest network node.Optional— one_way_fieldOptional line field marking one-way digitized edges.Optional— blocked_fieldOptional line field marking blocked/closed edges.Optional— parallel_executionIf true (default), evaluates origin searches in parallel for baseline and perturbed OD runs.Optional— outputOutput CSV path with OD pairs and sensitivity statistics.Required—

Examples

Computes OD costs with Monte Carlo impedance perturbation sensitivity. wbe.od_sensitivity_analysis(destinations='destinations.shp', edge_cost_field='cost', impedance_disturbance_range='0.8,1.2', input='network.shp', monte_carlo_samples=10, origins='origins.shp', output='od_sensitivity.csv')

Shortest Path Network

Function name: shortest_path_network

Experimental

Finds the shortest path between start and end coordinates over a line network.

vector network shortest-path

Parameters

NameDescriptionRequiredDefault inputInput line network layer.Requirednetwork.shp start_xStart x coordinate.Required0.0 start_yStart y coordinate.Required0.0 end_xEnd x coordinate.Required100.0 end_yEnd y coordinate.Required100.0 snap_toleranceOptional node snapping tolerance for graph construction.Optional— max_snap_distanceOptional max distance from start/end coordinates to nearest network node.Optional— edge_cost_fieldOptional numeric line field used as an impedance multiplier for segment length.Optional— one_way_fieldOptional line field marking one-way digitized edges (true/1/yes means from first to second vertex only).Optional— blocked_fieldOptional line field marking blocked/closed edges to exclude from routing (true/1/yes blocks).Optional— barriersOptional barrier point layer; nearest network nodes are blocked from traversal.Optional— barrier_snap_distanceOptional max distance from each barrier point to a network node for blocking.Optional— turn_penaltyOptional additive cost applied to non-straight turns at network nodes.Optional— u_turn_penaltyOptional additive cost applied to U-turn transitions.Optional— forbid_u_turnsIf true, disallow U-turn transitions.Optional— forbid_left_turnsIf true, disallow left-turn transitions.Optional— forbid_right_turnsIf true, disallow right-turn transitions.Optional— turn_restrictions_csvOptional CSV of turn transitions using columns prev_x,prev_y,node_x,node_y,next_x,next_y. Optional columns: forbidden (default true when no turn_cost column is provided) and turn_cost (or penalty/cost/extra_cost) for per-turn additive cost.Optional— temporal_cost_profileOptional CSV defining time-dependent edge costs (columns: edge_id,dow,start_minute,end_minute,value).Optional— temporal_edge_id_fieldOptional network field used to match temporal_cost_profile edge_id values (default EDGE_ID).Optional— departure_timeOptional RFC3339 departure time used for temporal profile lookup.Optional— temporal_modeOptional temporal interpretation mode: multiplier or absolute.Optional— temporal_fallbackOptional fallback when temporal row is missing: static_cost or error.Optional— temporal_profile_reportOptional JSON output path for temporal profile diagnostics (coverage, unmatched edges, fallback usage).Optional— outputOutput line vector path.Required—

Examples

Computes shortest path between two points on a line network. wbe.shortest_path_network(end_x=100.0, end_y=100.0, input='network.shp', output='shortest_path.shp', start_x=0.0, start_y=0.0)

Split Lines At Intersections

Function name: split_lines_at_intersections

No help documentation available for this tool.

Snap Points To Network

Function name: snap_points_to_network

No help documentation available for this tool.

Service Area Planning And Coverage Optimization

Function name: service_area_planning_and_coverage_optimization

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Service Area Planning and Coverage Optimization

Problem It Solves

Which facilities and scenarios provide the strongest service coverage, and where do unmet demand gaps remain?

Who It Is For

Accessibility planners, emergency coverage teams, and utility service design analysts.

Primary User

Municipal/public safety GIS teams, utilities, and logistics planners managing service-area targets.

What It Does

Builds network-based multi-ring service-area polygons from facility points over a line network.
Flags uncovered demand points outside baseline service coverage.
Produces scenario summary and candidate ranking CSV outputs for open/close planning workflows.

How It Works

Validates network/facility geometry types and parses ring costs.
Runs network service-area generation using the OSS network engine with polygon outputs.
Computes demand coverage and uncovered demand diagnostics against generated polygons.
Optionally evaluates scenario CSV variants (scenario_id,facility_id,is_open[,capacity]) and exports comparative KPI rows.
Indicative KPI formula: coverage_pct = 100 * covered_demand / total_demand.

Why It Wins

Replaces disconnected manual GIS steps with a reproducible network-derived coverage workflow and explicit planning artifacts.

Typical Buying Trigger

Teams need auditable facility coverage plans with scenario comparison outputs for governance or budget review.

Typical Presets

baseline-only: generate default service areas + uncovered demand.
scenario-planning: include open/close scenario CSV for option analysis.
candidate-screening: rank facilities by demand coverage proxy for expansion planning.

Inputs

ParameterOptionalDescription networknoInput line network layer (roads, trails, utility lines). facilitiesnoInput facility point layer used as service origins. demand_pointsyesOptional demand point layer used for covered/uncovered diagnostics and KPI generation. ring_costsnoNumeric array of travel-cost ring thresholds (e.g., [5, 10, 15]). scenariosyesOptional CSV with scenario_id,facility_id,is_open[,capacity] for open/close scenario runs. service_areasnoOutput vector path for service-area polygons. uncovered_demandnoOutput vector path for uncovered demand points. scenario_summary_csvnoOutput CSV path for scenario KPIs. ranked_candidates_csvnoOutput CSV path for candidate ranking metrics.

Outputs

ParameterTypeDescription service_areasGeoJSON/GeoPackage/ShapefileBaseline network-derived multi-ring service-area polygons. uncovered_demandGeoJSON/GeoPackage/ShapefileDemand points outside baseline service-area coverage. scenario_summary_csvCSVScenario-level KPI table (scenario_id,total_demand_covered_pct,avg_accessibility,outlier_count). ranked_candidates_csvCSVCandidate ranking table (candidate_id,coverage_gain_pct,avg_distance_improvement,rank).

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

result = wbe.service_area_planning_and_coverage_optimization( network="data/street_network.shp", facilities="data/facilities.shp", demand_points="data/demand_points.shp", ring_costs=[5.0, 10.0, 15.0], scenarios="data/service_scenarios.csv", service_areas="output/service_areas.geojson", uncovered_demand="output/uncovered_demand.geojson", scenario_summary_csv="output/scenario_summary.csv", ranked_candidates_csv="output/ranked_candidates.csv", )

print(result)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Transfer Attributes

Function name: transfer_attributes

No help documentation available for this tool.

Travelling Salesman Problem

Function name: travelling_salesman_problem

This tool finds approximate solutions to travelling salesman problems, the goal of which is to identify the shortest route connecting a set of locations. The tool uses an algorithm that applies a 2-opt heuristic and a 3-opt heuristic as a fall-back if the initial approach takes too long. The user must specify the names of the input points vector (input) and output lines vector file (output), as well as the duration, in seconds, over which the algorithm is allowed to search for improved solutions (duration). The tool works in parallel to find more optimal solutions.

Python API

def travelling_salesman_problem(self, input: Vector, duration: int = 60) -> Vector:

Vehicle Routing CVRP

Function name: vehicle_routing_cvrp

Experimental

Builds capacity-constrained multi-depot delivery routes with heterogeneous fleet controls, objective modes, and optional local optimization.

vector network routing optimization

Parameters

NameDescriptionRequiredDefault networkInput line network layer (validated for contract parity).Requirednetwork.gpkg depot_pointsDepot point layer; each point can contribute one or more vehicles.Requireddepots.gpkg stop_pointsDelivery stop point layer.Requiredstops.gpkg demand_fieldNumeric demand field in stop_points (default: demand).Optionaldemand priority_fieldOptional stop priority field using values like required/high/normal/low or numeric ranks.Optionalpriority allowed_vehicle_profiles_fieldOptional stop field listing compatible vehicle profiles (comma/semicolon/pipe-delimited).Optional— allowed_route_classes_fieldOptional alias of allowed_vehicle_profiles_field for route-class compatibility rules.Optional— depot_id_fieldOptional depot ID field used in route/assignment outputs.Optional— vehicle_count_fieldOptional depot field for number of vehicles spawned at each depot.Optional— vehicle_capacity_fieldOptional depot field overriding vehicle_capacity per depot/vehicle template.Optional— vehicle_fixed_cost_fieldOptional depot field overriding vehicle_fixed_cost per depot/vehicle template.Optional— travel_speed_fieldOptional depot field overriding travel_speed per depot/vehicle template.Optional— max_route_distance_fieldOptional depot field overriding max_route_distance per depot/vehicle template.Optional— max_route_time_fieldOptional depot field overriding max_route_time per depot/vehicle template.Optional— vehicle_profile_fieldOptional depot field defining vehicle profile/category token used for stop compatibility.Optional— vehicle_route_class_fieldOptional alias of vehicle_profile_field for route-class compatibility rules.Optional— vehicle_capacityPer-vehicle capacity (> 0).Required100.0 vehicle_fixed_costOptional fixed cost charged per dispatched vehicle/route (default: 0).Optional0.0 max_vehiclesOptional maximum number of vehicles/routes to construct.Optional— max_route_distanceOptional maximum travel distance per route, including return to depot.Optional— travel_speedTravel speed in coordinate-units per time unit (default: 1).Optional— max_route_timeOptional maximum route duration in model time units, including return to depot.Optional— max_stops_per_vehicleOptional maximum number of stops assigned to each vehicle route.Optional— objective_modeRoute-construction objective: minimize_distance, minimize_vehicles, or minimize_cost.Optionalminimize_distance apply_local_optimizationWhen true, applies a deterministic 2-opt local improvement pass to each constructed route (default: true).OptionalTrue apply_simulated_annealingWhen true, applies a seeded simulated annealing refinement pass per route after greedy/local optimization (default: false).OptionalFalse sa_iterationsMaximum simulated annealing iterations per route when apply_simulated_annealing=true (default: 1500).Optional1500 sa_initial_temperatureInitial simulated annealing temperature (> 0, default: 1.0).Optional1.0 sa_cooling_rateSimulated annealing cooling multiplier in (0, 1); default 0.995.Optional0.995 sa_seedOptional deterministic random seed for simulated annealing (default: 42).Optional42 outputOutput route line vector path.Required— assignment_outputOptional stop assignment point output with visit order/load diagnostics.Optional—

Examples

Builds CVRP routes and writes route lines with deterministic local optimization and optional simulated annealing controls. wbe.vehicle_routing_cvrp(apply_local_optimization=True, apply_simulated_annealing=False, demand_field='demand', depot_points='depots.gpkg', network='network.gpkg', objective_mode='minimize_distance', output='cvrp_routes.gpkg', priority_field='priority', sa_cooling_rate=0.995, sa_initial_temperature=1.0, sa_iterations=1500, sa_seed=42, stop_points='stops.gpkg', vehicle_capacity=100.0, vehicle_fixed_cost=0.0)

Vehicle Routing Pickup Delivery

Function name: vehicle_routing_pickup_delivery

Experimental

Builds paired pickup-delivery routes with precedence and capacity constraints using a deterministic nearest-neighbour baseline.

vector network routing optimization pickup-delivery

Parameters

NameDescriptionRequiredDefault networkInput line network layer (validated for contract parity).Requirednetwork.gpkg depot_pointsDepot point layer; first point is used as the active depot in this baseline implementation.Requireddepots.gpkg stop_pointsStop point layer containing paired pickup and delivery records.Requiredstops.gpkg request_id_fieldRequest identifier field in stop_points used to pair pickup and delivery records (default: request_id).Optionalrequest_id stop_type_fieldStop type field in stop_points containing pickup/delivery labels (default: stop_type).Optionalstop_type demand_fieldNumeric demand field in stop_points; pickup demand is loaded and delivered demand is ignored (default: demand).Optionaldemand vehicle_capacityPer-vehicle capacity (> 0).Required100.0 max_vehiclesOptional maximum number of vehicles/routes to construct.Optional— outputOutput route line vector path.Required— assignment_outputOptional stop assignment point output with request and precedence diagnostics.Optional—

Examples

Builds baseline pickup-delivery routes and writes route lines. wbe.vehicle_routing_pickup_delivery(demand_field='demand', depot_points='depots.gpkg', network='network.gpkg', output='pickup_delivery_routes.gpkg', request_id_field='request_id', stop_points='stops.gpkg', stop_type_field='stop_type', vehicle_capacity=100.0)

Vehicle Routing VRPTW

Function name: vehicle_routing_vrptw

Experimental

Builds capacity-constrained multi-depot VRPTW routes with heterogeneous fleet settings, break windows, and objective-mode controls.

vector network routing optimization time-window

Parameters

NameDescriptionRequiredDefault networkInput line network layer (validated for contract parity).Requirednetwork.gpkg depot_pointsDepot point layer; each point can contribute one or more vehicles.Requireddepots.gpkg stop_pointsDelivery stop point layer with demand and time-window fields.Requiredstops.gpkg demand_fieldNumeric demand field in stop_points (default: demand).Optionaldemand priority_fieldOptional stop priority field using values like required/high/normal/low or numeric ranks.Optionalpriority allowed_vehicle_profiles_fieldOptional stop field listing compatible vehicle profiles (comma/semicolon/pipe-delimited).Optional— allowed_route_classes_fieldOptional alias of allowed_vehicle_profiles_field for route-class compatibility rules.Optional— tw_start_fieldNumeric time-window start field in stop_points (default: tw_start).Optionaltw_start tw_end_fieldNumeric time-window end field in stop_points (default: tw_end).Optionaltw_end service_time_fieldNumeric per-stop service time field in stop_points (default: service_time).Optionalservice_time depot_id_fieldOptional depot ID field used in route/assignment outputs.Optional— vehicle_count_fieldOptional depot field for number of vehicles spawned at each depot.Optional— vehicle_capacity_fieldOptional depot field overriding vehicle_capacity per depot/vehicle template.Optional— vehicle_fixed_cost_fieldOptional depot field overriding vehicle_fixed_cost per depot/vehicle template.Optional— travel_speed_fieldOptional depot field overriding travel_speed per depot/vehicle template.Optional— max_route_distance_fieldOptional depot field overriding max_route_distance per depot/vehicle template.Optional— max_route_time_fieldOptional depot field overriding max_route_time per depot/vehicle template.Optional— vehicle_profile_fieldOptional depot field defining vehicle profile/category token used for stop compatibility.Optional— vehicle_route_class_fieldOptional alias of vehicle_profile_field for route-class compatibility rules.Optional— depot_close_time_fieldOptional depot field overriding depot_close_time per depot/vehicle template.Optional— break_start_fieldOptional depot field overriding break_start_time per depot/vehicle template.Optional— break_end_fieldOptional depot field overriding break_end_time per depot/vehicle template.Optional— break_duration_fieldOptional depot field overriding break_duration per depot/vehicle template.Optional— vehicle_capacityPer-vehicle capacity (> 0).Required100.0 vehicle_fixed_costOptional fixed cost charged per dispatched vehicle/route (default: 0).Optional0.0 start_timeRoute start time in model time units (default: 0).Optional0.0 travel_speedTravel speed in coordinate-units per time unit (default: 1).Optional1.0 enforce_time_windowsWhen true, only stops with lateness OptionalFalse allowed_latenessMaximum lateness tolerated when enforce_time_windows=true (default: 0).Optional0.0 depot_close_timeOptional hard close time by which each route must return to depot.Optional— break_start_timeOptional global break-window start time for all vehicles.Optional— break_end_timeOptional global break-window end time for all vehicles.Optional— break_durationOptional global break duration applied once per route when break window is intersected.Optional— use_priority_scoringWhen true, ranks feasible candidates by projected lateness/slack before travel distance; when false, uses nearest-neighbour baseline (default: true).OptionalTrue max_vehiclesOptional maximum number of vehicles/routes to construct.Optional— max_route_distanceOptional maximum route travel distance, including return to depot.Optional— max_route_timeOptional maximum route duration in model time units, including return to depot.Optional— max_stops_per_vehicleOptional maximum number of stops assigned to each vehicle route.Optional— objective_modeRoute-construction objective: minimize_lateness, minimize_distance, minimize_vehicles, or minimize_cost.Optionalminimize_lateness outputOutput route line vector path.Required— assignment_outputOptional stop assignment point output with time-window diagnostics.Optional—

Examples

Builds baseline VRPTW routes and reports time-window diagnostics. wbe.vehicle_routing_vrptw(allowed_lateness=0.0, demand_field='demand', depot_points='depots.gpkg', enforce_time_windows=False, network='network.gpkg', objective_mode='minimize_lateness', output='vrptw_routes.gpkg', priority_field='priority', service_time_field='service_time', start_time=0.0, stop_points='stops.gpkg', travel_speed=1.0, tw_end_field='tw_end', tw_start_field='tw_start', use_priority_scoring=True, vehicle_capacity=100.0, vehicle_fixed_cost=0.0)

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