Tools for preparing and validating GMNS transportation network data

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Project description

GMNS Ready

Professional toolkit for preparing and validating GMNS transportation networks with complete zone connectivity.

GMNS Ready

Overview

gmns-ready is a comprehensive Python package that prepares, validates, and enhances GMNS (General Modeling Network Specification) transportation networks. It automates the critical but often manual process of connecting traffic analysis zones to road networks, ensuring your data is ready for traffic assignment and travel demand modeling.

Key Capabilities:

✅ Validate spatial alignment before processing
✅ Extract and process zone data from shapefiles with automatic detection
✅ Generate zone-to-network connectors following Forward Star structure
✅ Validate network integrity and accessibility using DTALite
✅ Enhance connectivity for zones with limited network access
✅ Prepare networks for traffic assignment with VDF parameter validation
✅ Cross-platform support (Windows, Linux, macOS)

Installation

pip install gmns-ready

This will automatically install all required dependencies including DTALite for accessibility validation.

New to gmns-ready? Check out our interactive tutorial on Google Colab - no installation required!

Or install from source:

git clone https://github.com/hhhhhenanZ/gmns_ready.git
cd gmns_ready
pip install -e .

Quick Start

Pre-flight check: `gr.quick_check()`

A read-only structural check across 7 levels of the GMNS validation prompt — the recommended warm-start before DTALite.

Inputs (current folder)                  Outputs (same folder)
────────────────────────────────         ───────────────────────────
node.csv                (required) ──┐
link.csv                (required) ──┼──>  validation_report.txt
demand.csv              (optional) ──┤     zone_connector_report.csv
settings.csv            (optional) ──┤
origin_accessibility.csv           ──┤     [Stage 2 outputs from DTALite
destination_accessibility.csv      ──┤      are auto-detected if present]
inaccessible_od.csv                ──┤
google_maps_od_distance.csv        ──┘

import gmns_ready as gr
gr.quick_check()       # run from the folder containing your inputs

Every finding is tagged with the prompt sub-section code (1D, 2A, 4B, ...) so you can paste it into the GMNS Network Validator Prompt on any GenAI platform (ChatGPT, Claude, Gemini) for automated fixes.

Full pipeline

import gmns_ready as gr

# Step 0 (recommended): read-only structural sanity check
gr.quick_check()

# Step 1: Validate inputs are spatially aligned
gr.validate_basemap()

# Step 2: Extract zones from shapefile (auto-detects .shp in data/ folder)
gr.extract_zones()

# Step 3: Build zone-connected network
gr.build_network()

# Step 4: Validate everything
gr.validate_network()
gr.validate_accessibility()  # Uses DTALite for traffic assignment
gr.validate_assignment()

📊 Supported Input Sources

gmns-ready is designed to work with data from multiple agencies and modeling tools:

Source	Typical Users	Status
OSM → osm2gmns	Researchers, universities, open-data projects	Supported
TransCAD Shapefiles (via shp2gmns)	State DOTs, MPOs	Supported
OSMNX + Network Wrangler	SFCTA, Caltrans, Bay Area agencies	Supported
TNTP Networks	Academic benchmarks	Supported
Custom CSV	Legacy systems, proprietary formats	Supported

Key Insight: All sources are converted to the same internal standardized structure, enabling seamless interoperability.

📋 Application Scenarios

Scenario 1: Traffic Assignment

Purpose: Connect zone centroids to physical transportation network for dynamic/static traffic assignment.

Input Files:

node.csv, link.csv — Generated by osm2gmns from OpenStreetMap data (Physical network)
zones.shp — TAZ (Traffic Analysis Zones), Census Tracts, or custom zone boundaries

Network Structure:

N = P ∪ Z  where P ∩ Z = ∅

Components:

Zone Centroids (Z): Trip generation/attraction points from planning layer
Physical Network Nodes (P): Infrastructure nodes from osm2gmns
Internal Activity Nodes (IA ⊂ P): Auto-identified access points within physical network
- Located at arterial/local intersections
- Network boundaries
- Functional class transition points
Connectors (L_C): Virtual links connecting Z → IA

Use Cases:

Regional traffic forecasting
Congestion analysis
Travel time estimation
Peak-hour flow simulation

Mathematical Framework:

Z = {z₁, z₂, ..., zₙ}  (zone centroids)
P = {p₁, p₂, ..., pₘ}  (physical network nodes)
IA ⊂ P                  (internal activity nodes - subset of physical nodes)
Connectors: C(Z, P)

Scenario 2: POI Accessibility Analysis

Purpose: Analyze accessibility to specific points of interest (parks, schools, hospitals, transit stops, markets).

Input Files:

node.csv, link.csv — Physical network from osm2gmns (same as Scenario 1)
poi.shp or poi.csv — External access points (bus stops, park gates, school entrances)
- Renamed as zone.csv in your local folder alongside node.csv and link.csv

Network Structure:

N = P ∪ Z_E  where all sets are disjoint

Key Transformation:

φ: EA → Z_E  (External access points promoted to zones)

Components:

External Access Points (EA): User-provided POI shapefile/CSV
External Zones (Z_E): Each EA converted to micro-zone via φ transformation
Connectors: Two types
- Z → IA (traditional trip loading)
- Z_E → P (nearest physical node connection)

Critical Constraint:

Z_E ∩ P = ∅  (External zones must NOT overlap with OSM network nodes)

Use Cases:

Transit equity studies (distance to bus stops)
Facility accessibility
15-minute city planning

🔄 The 7-Step Pipeline

This workflow applies to all input sources (OSM, TransCAD, Wrangler, TNTP, Custom):

STEP 1: Import Network

Supports multiple import pathways:

OSM2GMNS: node.csv, link.csv from OpenStreetMap
shp2gmns: TransCAD/ArcGIS shapefiles
osmnx: Graph export to CSV (nodes, edges)
Network Wrangler: OUT_DIR/hwy or OUT_DIR/trn outputs
TNTP: .tntp format (nodes.tntp, links.tntp)
Custom CSV: Generic CSV reader with configuration

All imports convert to the same internal object model.

STEP 2: Standardize to GMNS Schema

Automated transformations:

Rename fields to GMNS standard
Assign node types (boundary, signalized intersection, etc.)
Clean duplicate IDs
Establish consistent CRS (coordinate reference system)
Validate link direction and topology

STEP 3: Construct Zones (Z)

For Scenario 1 (DTA):

Load TAZ shapefile
Generate zone centroids
Assign zone attributes

For Scenario 2 (POI):

Load external access points (EA)
Transform: φ(EA) → Z_E
Verify Z_E ∩ P = ∅
Treat POIs as micro-zones

STEP 4: Identify Internal Activity Nodes (IA)

Auto-identification criteria:

Functional class boundaries (e.g., arterial ↔ local transition)
External boundaries (city limits, study area edges)
Entrance logic (freeway ramps, major intersections)
MPO-provided "access node" lists (if available)

Key principle: IA ⊂ P (not a separate file)

STEP 5: Convert EA → Z_E (Scenario 2 only)

External access nodes (park gates, bus stops, schools) are promoted to zones:

EA → Z_E transformation via function φ
Each Z_E connects to nearest physical node (P)
Avoids double-counting
Maintains clean model logic

STEP 6: Generate Connectors (C)

Two connector types:

Z → IA connectors (for trip loading)
Z_E → P connectors (for external POIs)
- Connects to nearest physical network node
- Ensures accessibility calculations include POIs

Validation:

Every zone must have at least one connector
No connector-less zones allowed

STEP 7: Build Final Unified Output

Output Files:

node.csv — Contains P, IA, Z, Z_E (all node types)
link.csv — Contains L_P (physical links) + L_C (connectors)
zone.csv — Zone definitions and attributes
connector_links.csv — Detailed connector information

Ready for:

Accessibility analysis
Traffic assignment and simulation
Network Wrangler scenario management

📐 Mathematical Framework

Notation Reference

Symbol	Type	Definition
P	Set	Physical network nodes (from osm2gmns or other sources)
Z	Set	Zone centroids (trip generation/attraction areas)
IA	Subset	Internal activity nodes: IA ⊂ P (auto-identified access points)
EA	Set	External access points (user-provided POIs)
Z_E	Set	External zones: φ(EA) = Z_E (EA promoted to zones)
L_P	Set	Physical links (road segments)
L_C	Set	Connectors (virtual links between zones and network)
φ	Function	Transformation: EA → Z_E (converts external points to zones)

Critical Constraints

Disjoint Sets:     P ∩ Z = ∅,  P ∩ Z_E = ∅,  Z ∩ Z_E = ∅
Completeness:      ∀z ∈ Z ∪ Z_E: ∃c ∈ L_C
Activity Nodes:    IA ⊂ P (not a separate node type)
Non-Overlapping:   External POI locations must not coincide with existing OSM nodes

Tutorial

Interactive Tutorial: Try our hands-on Google Colab tutorial to learn how to use gmns-ready with real examples.

Core Functions

1. Input Validation

validate_basemap() - Verify spatial alignment of input files

Checks that node.csv, link.csv, and zone shapefiles are in the same geographic area before processing. This prevents common errors from misaligned data sources and saves troubleshooting time.

import gmns_ready as gr

gr.validate_basemap()  # Checks files in current directory

When to use: FIRST step before any processing

Inputs:

node.csv and link.csv in current directory
Any .shp file in data/ folder

Output: data/base_map_validation_report.json

What it checks:

Coordinate system consistency
Geographic overlap of all datasets
Bounding box alignment

2. Zone Data Processing

extract_zones() - Extract zone centroids and boundaries from shapefile

Automatically detects and processes zone shapefiles (census tracts, TAZs, etc.) from the data/ folder. Calculates centroids, preserves boundaries, and generates GMNS-compliant zone.csv with automatic coordinate projection to EPSG:4326.

import gmns_ready as gr

gr.extract_zones()  # Auto-detects .shp file in data/ folder

Inputs:

Any .shp file in data/ folder (auto-detected)
Supports multiple shapefile types: census tracts, TAZ, custom zones

Outputs:

zone.csv with zone_id, x_coord, y_coord, boundary_geometry (WKT)

Features:

Auto-detects zone ID column (TRACTCE, GEOID, TAZ, etc.)
Reprojects to EPSG:4326 automatically
Preserves both centroid points and boundary polygons

extract_zones_pop() - Add population data to zones (US only)

Fetches and adds demographic data from ACS 2022 API for US zones. Outputs the same zone.csv with an additional population column.

import gmns_ready as gr

gr.extract_zones_pop()  # Uses .shp from data/, adds population column

Inputs:

Any .shp file in data/ folder (auto-detected)

Outputs:

zone.csv with all zone data + population column

Note: Only works for US locations. Population data does not affect network connectivity.

3. Network Building

build_network() - Generate zone-connected network with connectors

The core function that creates a complete zone-connected network following Forward Star Network Structure. Connects each zone to the nearest road network nodes and creates activity nodes for demand generation.

import gmns_ready as gr
# Uses zone.csv, node.csv, link.csv from current directory
# Default: 1000m search radius
gr.build_network()

# Custom search radius for different urban contexts
gr.build_network(zone_search_radius=500)   # Dense urban areas
gr.build_network(zone_search_radius=1500)  # Suburban areas
gr.build_network(zone_search_radius=None)  # Unlimited (rural areas)

Prerequisites:

zone.csv from extract_zones()
node.csv and link.csv from osm2gmns

Parameters:

zone_search_radius (float or None, default=1000): Search radius in meters for connecting zones without activities to road network
- 500m: Dense urban cores with high road density
- 1000m: Mixed urban/suburban areas (default, recommended)
- 1500m: Suburban or low-density areas
- None: Unlimited search (always find nearest link, best for rural areas)

Outputs: connected_network/ folder containing:

node.csv - Network nodes + activity nodes + zone centroids
link.csv - Road links + connector links
activity_node.csv - Activity nodes (trip generation points)
connector_links.csv - Connector links only

What it does:

Connects each zone centroid to nearest network nodes
Creates activity nodes from OSM POIs (residential, commercial, educational, transit locations)
Ensures bidirectional connectivity between zones and network
Maintains GMNS format compliance

Key concept: Activity nodes are OSM-derived points of interest that represent where trips begin or end in GMNS-based demand modeling.

4. Network Validation

validate_network() - Check network structure and topology

Validates network topology, node-link consistency, connectivity, and GMNS format compliance for the zone-connected network.

import gmns_ready as gr

gr.validate_network()  # Checks connected_network/ folder

Output: connected_network/network_validation_report.json

What it checks:

Node-link topology consistency
Network connectivity (all zones reachable)
GMNS format compliance
Data integrity

validate_accessibility() - Analyze zone-to-zone connectivity using DTALite

Runs traffic assignment using DTALite to compute zone-to-zone accessibility matrix and identify connectivity issues. This validation uses the DTALite Python package for cross-platform support (Windows, Linux, macOS).

import gmns_ready as gr

gr.validate_accessibility()  # Checks connected_network/ folder

Requirements:

DTALite package (auto-installed with gmns-ready)
settings.csv in GMNS_Tools folder or network directory
connected_network/node.csv and link.csv

Outputs:

connected_network/zone_accessibility.csv - Zone-to-zone connectivity metrics
connected_network/link_performance.csv - Traffic assignment results
connected_network/accessibility_validation_report.json - Validation summary

What it computes:

Zone-to-zone reachability matrix
Origin/destination connectivity for each zone
Identifies zones with poor accessibility (<10% of total zones)

Check results: Review the report to identify zones that may need additional connectors.

DTALite: DTALite is a fast, open-source traffic assignment engine that performs dynamic traffic assignment to compute realistic travel patterns and accessibility metrics.

validate_assignment() - Verify traffic assignment readiness

Validates VDF (Volume-Delay Function) parameters and link attributes required for traffic assignment by link type.

import gmns_ready as gr

gr.validate_assignment()  # Checks connected_network/ folder

Output: connected_network/assignment_validation_summary.json

What it checks:

VDF parameters: vdf_alpha, vdf_beta, vdf_plf, vdf_fftt
Link capacity by link_type
Parameter value ranges and consistency
Excludes connectors (link_type=0) from validation

5. Connectivity Enhancement

enhance_connectors() - Add connectors for poorly connected zones

Adds 10 additional connectors per zone to improve accessibility for zones with poor network connectivity (<10% of total zones). Distributes connectors across road hierarchy: 3 to highways, 3 to arterials, 2 to collectors, 2 to local roads.

import gmns_ready as gr
# Default: 1000m search radius, 10% threshold, 6 connectors per zone
gr.enhance_connectors()

# Urban area: smaller search radius
gr.enhance_connectors(search_radius=500)
# Suburban area: larger search radius
gr.enhance_connectors(search_radius=1500)
# More aggressive enhancement
gr.enhance_connectors(
    search_radius=1500,
    accessibility_threshold=0.15,  # Target zones below 15% connectivity
)

When to use:

After running validate_accessibility()
When zones show low connectivity scores (<10% of total zones)
To improve network coverage for isolated zones

Parameters:

search_radius (int, default=1000): Maximum search distance in meters for finding candidate network links
- 500m: Dense urban areas (more precise, fewer long connections)
- 1000m: Mixed areas (default, balanced approach)
- 1500m: Suburban/rural areas (wider search, more connection options)
accessibility_threshold (float, default=0.10): Connectivity target as percentage of total zones
- Zones connecting to fewer than this percentage are enhanced
- 0.10: Conservative (10% threshold - enhance only most isolated zones)

Outputs:

connected_network/link_updated.csv - Enhanced link file with additional connectors
connected_network/connector_editor_report.txt - Detailed report of added connectors

Workflow:

Run enhance_connectors()
Review link_updated.csv and report
Replace connected_network/link.csv with link_updated.csv
Re-run validate_accessibility() to verify improvements
Repeat if needed until accessibility requirements are met

Network Preparation

`clean_network()` - Remove disconnected components from OSM networks

OSM networks extracted via osm2gmns may contain disconnected islands or isolated segments due to data quality issues. This function identifies the main connected component and removes isolated parts, ensuring your network is fully traversable.

import gmns_ready as gr

gr.clean_network()  # Cleans node.csv and link.csv from osm2gmns

When to use:

BEFORE building zone-connected network
After extracting network from osm2gmns
When you suspect OSM data quality issues
To ensure complete network traversability

Inputs:

node.csv and link.csv (from osm2gmns in current directory)

Outputs: osm_network_connectivity_check/ folder containing:

Cleaned node.csv and link.csv (main connected component only)
network_connectivity_analysis.png - Before/after visualization
isolated_components_detail.png - Detailed view of removed components

After running: Replace your original node.csv and link.csv with the cleaned versions from osm_network_connectivity_check/ folder, then proceed to build_network().

Complete Workflow Example

import gmns_ready as gr
import osm2gmns as og

# ============================================================================
# STEP 0: Generate base network from OSM (using osm2gmns)
# ============================================================================
# net = og.getNetFromFile('map.osm')
# og.outputNetToCSV(net)  # Creates node.csv and link.csv

# ============================================================================
# STEP 0.5: Clean OSM network (recommended)
# ============================================================================
gr.clean_network()
# Copy cleaned files from osm_network_connectivity_check/ to project root

# ============================================================================
# STEP 1: Validate spatial alignment
# ============================================================================
gr.validate_basemap()
# Check: data/base_map_validation_report.json

# ============================================================================
# STEP 2: Extract zones
# ============================================================================
gr.extract_zones()
# Output: zone.csv

# Optional: Add population data (US only)
# gr.extract_zones_pop()
# Output: zone.csv with population column

# ============================================================================
# STEP 3: Build zone-connected network
# ============================================================================
gr.build_network()
# Output: connected_network/ folder with all network files

# ============================================================================
# STEP 4: Validate network
# ============================================================================
gr.validate_network()
# Check: connected_network/network_validation_report.json

gr.validate_accessibility()  # Uses DTALite for traffic assignment
# Check: connected_network/accessibility_validation_report.json

gr.validate_assignment()
# Check: connected_network/assignment_validation_summary.json

# ============================================================================
# STEP 5: Enhance connectivity if needed
# ============================================================================
# If accessibility report shows poorly connected zones:
gr.enhance_connectors()
# Output: connected_network/link_updated.csv

# Replace link.csv with link_updated.csv
# import shutil
# shutil.copy('connected_network/link_updated.csv', 'connected_network/link.csv')

# Re-validate
gr.validate_accessibility()

# Repeat enhancement if needed until all zones meet requirements

Project Structure

your_project/
├── data/
│   ├── zones.shp                    # Input: Zone shapefile (any name)
│   └── base_map_validation_report.json
├── GMNS_Tools/                      
│   └── settings.csv                 # DTALite configuration
├── node.csv                         # From osm2gmns
├── link.csv                         # From osm2gmns
├── zone.csv                         # Generated by extract_zones()
├── osm_network_connectivity_check/  # Optional: cleaned network
│   ├── node.csv
│   ├── link.csv
│   ├── network_connectivity_analysis.png
│   └── isolated_components_detail.png
└── connected_network/               # Final output
    ├── node.csv
    ├── link.csv
    ├── settings.csv                 # Copied from GMNS_Tools
    ├── activity_node.csv
    ├── connector_links.csv
    ├── zone_accessibility.csv       # From DTALite
    ├── link_performance.csv         # From DTALite
    ├── network_validation_report.json
    ├── accessibility_validation_report.json
    ├── assignment_validation_summary.json
    ├── link_updated.csv             # If enhanced
    └── connector_editor_report.txt  # If enhanced

Function Reference

Import Style

# Recommended: Import once, use all functions
import gmns_ready as gr

# Then call any function:
gr.validate_basemap()
gr.extract_zones()
gr.build_network()
gr.validate_network()
gr.validate_accessibility()
gr.validate_assignment()
gr.enhance_connectors()
gr.extract_zones_pop()
gr.clean_network()

Function Summary

Function	Purpose	When to Use
`validate_basemap()`	Check spatial alignment	FIRST - before any processing
`extract_zones()`	Extract zones from shapefile	After basemap validation
`extract_zones_pop()`	Add population to zones	Optional, US only
`build_network()`	Create zone-connected network	After zone extraction
`validate_network()`	Check network structure	After network building
`validate_accessibility()`	Analyze zone connectivity with DTALite	After network building
`validate_assignment()`	Check assignment readiness	After network building
`enhance_connectors()`	Add more connectors	When accessibility is poor
`clean_network()`	Remove isolated components	Before network building (optional)

🔗 Integration with Other Tools

Working with osm2gmns

osm2gmns is the primary tool for converting OpenStreetMap data to GMNS format:

Step 1: Use osm2gmns to download and convert OSM data
  → Produces: node.csv, link.csv

Step 2: Load into gmns-ready
  → Add zones (TAZ shapefile or POI data)
  → Identify activity nodes (automatic)
  → Generate connectors

Step 3: Export unified network
  → Ready for DTALite or other assignment tools

Compatibility: gmns-ready is designed as a downstream tool for osm2gmns outputs.

Working with shp2gmns (TransCAD/Shapefile Workflows)

Many MPOs and state DOTs use TransCAD or maintain networks as shapefiles:

Step 1: Use shp2gmns to convert shapefiles to GMNS
  Input:  network_links.shp, network_nodes.shp
  Output: node.csv, link.csv, segment.csv (if needed)

Step 2: Plug directly into gmns-ready pipeline
  → Standard processing applies
  → Generate zones and connectors
  → Export for assignment

Key benefit: Legacy TransCAD networks can be modernized to GMNS standard.

Working with OSMNX + Network Wrangler

Network Wrangler is used for scenario management by agencies like SFCTA and Caltrans:

Step 1: Use osmnx to create graph
  → Export to CSV (nodes, edges)

Step 2: Run Wrangler build script
  python build_network.py network_specification.py
  → Produces GMNS-like outputs in OUT_DIR/hwy or OUT_DIR/trn

Step 3: Ingest Wrangler outputs into gmns-ready
  → Standard format conversion
  → No conflicts — Wrangler is just another importer

Step 4: Apply gmns-ready pipeline
  → Zone integration
  → Connector generation
  → Final export

Key benefit: Scenario management (via Wrangler) + network preparation (via gmns-ready) work seamlessly together.

Working with TNTP Benchmark Networks

TNTP provides standardized test networks widely used in research:

Step 1: Download TNTP network
  Files: nodes.tntp, links.tntp

Step 2: Convert to GMNS format using gmns-ready
  → Parse TNTP format
  → Convert to node.csv, link.csv

Step 3: Optionally create synthetic zones
  → Grid-based zone generation for testing

Step 4: Standard pipeline
  → Generate connectors
  → Export unified format

Key benefit: Academic benchmarks become compatible with modern GMNS tools.

Requirements

python >= 3.7
pandas >= 1.3.0
geopandas >= 0.10.0
shapely >= 1.8.0
matplotlib >= 3.3.0
networkx >= 2.6.0
DTALite >= 0.8.1

All dependencies are automatically installed with pip install gmns-ready.

GMNS Compliance

This package follows the General Modeling Network Specification (GMNS) standard, ensuring compatibility with:

Traffic assignment tools (e.g., DTALite)
Travel demand models
Network visualization tools
Other GMNS-compliant software

Citation

If you use this package in your research, please cite:

@software{gmns_ready,
  author = {Zhu, Henan and Zhou, Xuesong and Zheng, Han},
  title  = {GMNS Ready: Professional Toolkit for GMNS Transportation Networks},
  year   = {2025},
  url    = {https://github.com/hhhhhenanZ/gmns_ready}
}

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details.

Authors

Henan Zhu, Xuesong Zhou, Han Zheng
Arizona State University

Contact:

Issues: GitHub Issues
Email: henanzhu@asu.edu, xzhou74@asu.edu

Acknowledgments

Zephyr Foundation for GMNS standards
ASU Transportation + AI Lab for developing osm2gmns and DTALite

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May 7, 2026

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gmns-ready 0.1.1

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GMNS Ready

Table of Contents

Overview

Installation

Quick Start

Pre-flight check: gr.quick_check()

Full pipeline

📊 Supported Input Sources

📋 Application Scenarios

Scenario 1: Traffic Assignment

Scenario 2: POI Accessibility Analysis

🔄 The 7-Step Pipeline

STEP 1: Import Network

STEP 2: Standardize to GMNS Schema

STEP 3: Construct Zones (Z)

STEP 4: Identify Internal Activity Nodes (IA)

STEP 5: Convert EA → Z_E (Scenario 2 only)

STEP 6: Generate Connectors (C)

STEP 7: Build Final Unified Output

📐 Mathematical Framework

Notation Reference

Critical Constraints

Tutorial

Core Functions

1. Input Validation

2. Zone Data Processing

3. Network Building

4. Network Validation

5. Connectivity Enhancement

Network Preparation

clean_network() - Remove disconnected components from OSM networks

Complete Workflow Example

Project Structure

Function Reference

Import Style

Function Summary

🔗 Integration with Other Tools

Working with osm2gmns

Working with shp2gmns (TransCAD/Shapefile Workflows)

Working with OSMNX + Network Wrangler

Working with TNTP Benchmark Networks

Key benefit: Academic benchmarks become compatible with modern GMNS tools.

Requirements

GMNS Compliance

Citation

Contributing

License

Authors

Acknowledgments

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

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Release history Release notifications | RSS feed

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Built Distribution

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Pre-flight check: `gr.quick_check()`

`clean_network()` - Remove disconnected components from OSM networks