GRID2DEMAND: A tool for generating zone-to-zone travel demand based on grid cells or TAZs and gravity model
Grid2demand is an open-source quick demand generation tool based on the trip generation and trip distribution methods of the standard 4-step travel model.
By taking advantage of OSM2GMNS tool to obtain route-able transportation network from OpenStreetMap, Grid2demand aims to further utilize Point of Interest (POI) data to construct trip demand matrix aligned with standard travel models.
You can get access to the introduction video with the link: https://www.youtube.com/watch?v=EfjCERQQGTs&t=1021s
You can find base-knowledge tutorial with the link: Base Knowledge such as transportation 4 stages planning
You can find the tutorial code witht the link: How To Use Grid2demand
pip install grid2demand
If you meet installation issues, please reach out to our developers for solutions.
[!IMPORTANT] node.csv and poi.csv should follow the GMNS standard and you can generate node.csv and poi.csv using osm2gmns.
- Create zone from node.csv (the boundary of nodes), this will generate grid cells (num_x_blocks, num_y_blocks, or x length and y length in km for each grid cell)
- Generate demands for between zones (utilize nodes and pois)
from __future__ import absolute_import
import grid2demand as gd
if __name__ == "__main__":
# Specify input directory
input_dir = "your-data-folder"
# Initialize a GRID2DEMAND object
net = gd.GRID2DEMAND(input_dir=input_dir, use_zone_id=True, mode_type="auto")
# load network: node and poi
net.load_network()
# Generate zone.csv from node boundary by specifying number of x blocks and y blocks
net.net2grid(num_x_blocks=10, num_y_blocks=10)
# net.net2grid(cell_width=10, cell_height=10, unit="km")
# Load zone from zone.csv
net.taz2zone()
# Map zones with nodes and pois, viseversa
net.map_mapping_between_zone_and_node_poi()
# Calculate zone-to-zone distance matrix
net.calc_zone_od_distance_matrix(pct=1)
# Calculate demand by running gravity model
net.run_gravity_model()
# Save demand, zone, updated node, updated poi to csv
net.save_results_to_csv(agent=True, overwrite_file=False)
from __future__ import absolute_import
import grid2demand as gd
if __name__ == "__main__":
# Specify input directory
input_dir = "your-data-folder"
# Initialize a GRID2DEMAND object
net = gd.GRID2DEMAND(input_dir=input_dir, use_zone_id=True, mode_type="auto")
# Load network: node and poi
net.load_network()
# Load zone from zone.csv
net.taz2zone()
# Map zones with nodes and pois, viseversa
net.map_mapping_between_zone_and_node_poi()
# Calculate zone-to-zone distance matrix
net.calc_zone_od_distance_matrix(pct=1)
# Calculate demand by running gravity model
net.run_gravity_model()
# Save demand, zone, updated node, updated poi to csv
net.save_results_to_csv(node=Frale, zone=False, agent=False, overwrite_file=False)
The grid2demand project welcomes your expertise and enthusiasm!
Small improvements or fixes are always appreciated. If you are considering larger contributions to the source code, please contact us through email: Xiangyong Luo, Dr. Xuesong Simon Zhou
Writing code isn't the only way to contribute to grid2demand. You can also:
- review pull requests
- help us stay on top of new and old issues
- develop tutorials, presentations, and other educational materials
- develop graphic design for our brand assets and promotional materials
- translate website content
- help with outreach and onboard new contributors
- write grant proposals and help with other fundraising efforts
For more information about the ways you can contribute to grid2demand, visit our GitHub. If you' re unsure where to start or how your skills fit in, reach out! You can ask by opening a new issue or leaving a comment on a relevant issue that is already open on GitHub.
If you use grid2demand in your research please use the following BibTeX entry:
Xiangyong Luo, Xuesiong Simon Zhou (2023). xyluo25/grid2demand: Zenodo. https://doi.org/10.5281/zenodo.11212556