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# **Grid2demand**

GRID2DEMAND: A tool for generating zone-to-zone travel demand based on grid cells or TAZs and gravity model
Expand Down Expand Up @@ -104,25 +103,19 @@ Xiangyong Luo, Dustin Carlino, and Xuesong Simon Zhou. (2023). [xyluo25/grid2dem
If you have not used Grid2demand before, here are some advices to get started.

1. Read the user guide or watch the video at https://www.youtube.com/watch?v=EfjCERQQGTs.

2. Obtain a map.osm or map.osm.pbf file for your area of interest at https://extract.bbbike.org/ or openstreetmap.org

3. Look at the examples at Google colab environment https://github.com/asu-trans-ai-lab/grid2demand/blob/main/grid2demand_tutorial.ipynb

4. Install Python, Grid2demand and QGIS on your computer or using Google Colab environment to modify one of the examples to implement your own model.

5. Post your questions on the users group:

GitHub: https://github.com/asu-trans-ai-lab/grid2demand/issues

Google: groups.google.com/d/forum/grid2demand
GitHub: https://github.com/asu-trans-ai-lab/grid2demand/issues
GitHub: https://github.com/xyluo25/grid2demand/issues

Open-source tool of grid2demand aims to provide an open-source quick demand generation python package, which can work anywhere in the world, thanks to open data from [OpenStreetMap](Openstreetmap) users.

To know more about this tool, please check out the 3rd mini teaching lesson in our podcast series [https://www.youtube.com/watch?v=EfjCERQQGTs](https://www.youtube.com/watch?v=EfjCERQQGTs).

Gird2demand is an open-source trip generation and distribution tool for teaching transportation planning and applications. It generates zone-to-zone travel demand based on alphanumeric grid zones. Users can obtain zone-to-zone and
node-to-node travel demand with a few lines of python code based on OpenStreetMap and OSM2GMNS.
Gird2demand is an open-source trip generation and distribution tool for teaching transportation planning and applications. It generates zone-to-zone travel demand based on alphanumeric grid zones. Users can obtain zone-to-zone and node-to-node travel demand with a few lines of python code based on OpenStreetMap and OSM2GMNS.

For the python source code and sample network files, readers can visit the project homepage at ASU Trans+AI Lab Github
([https://github.com/asu-trans-ai-lab/grid2demand](https://github.com/asu-trans-ai-lab/grid2demand)). The Jupyter notebook example can be found at
Expand Down Expand Up @@ -208,16 +201,17 @@ When partitioning grid cells and calculating accessibility, World Geodetic Syste

**Description of Data Files**

| Step | Process | Input File or Parameter | Output File | Method |
| ---- | ---------------------------------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------- |
| 0 | Network files preparation | map file from OpenStreetMap | *node.csv, link.csv, poi.csv* | Osm2gmns tool |
| 1 | Input files reading | *node.csv, poi.csv* | | |
| 2 | Zone generation and grid partition | Number of blocks or grid scales in meters with latitude of the area of interest (optional) | *zone,csv, poi.csv* (update with zone id) | Alphanumeric grid |
| 3 | Trip generation | *poi_trip_rate.csv* (optional), trip purpose | *poi_trip_rate.csv* (output/update with utilization notes), *node.csv* (update with zone id and demand values) | Trip rate method |
| 4 | Accessibility calculation | *accessibility.csv* (optional), latitude of the area of interest | *accessibility.csv* | Simple straight-line distance between zone centroids |
| 5 | Trip distribution | Trip purpose, friction factor coefficients | *demand.csv, zone,csv* (update with total production and attraction in each zone) | Gravity model |
| 6 | Agent generation | *demand.csv* | *agent.csv* | Random sampling of node-to-node agents according to zone-to-zone demand |
| 7 | Visualization | | QGIS or NEXTA | |

| Step | Process | Input File or Parameter | Output File | Method |
| ------ | ------------------------------------ | ------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- |
| 0 | Network files preparation | map file from OpenStreetMap | *node.csv, link.csv, poi.csv* | Osm2gmns tool |
| 1 | Input files reading | *node.csv, poi.csv* | | |
| 2 | Zone generation and grid partition | Number of blocks or grid scales in defined units of the area of interest (optional) | *zone.csv, poi.csv* (update with zone id) | Alphanumeric grid |
| 3 | Trip generation | *poi_trip_rate.csv* (optional), trip purpose | *poi_trip_rate.csv* (output/update with utilization notes), *node.csv* (update with zone id and demand values) | Trip rate method |
| 4 | OD distance matrix | zone.csv | OD matrix | Distance between zone centroids |
| 5 | Trip distribution | Trip purpose, friction factor coefficients | *demand.csv, zone,csv* (update with total production and attraction in each zone) | Gravity model |
| 6 | Agent generation | *demand.csv* | *agent.csv* | Random sampling of node-to-node agents according to zone-to-zone demand |
| 7 | Visualization | | QGIS or NEXTA | |

### **Flowchart of grid2demand**

Expand Down Expand Up @@ -283,14 +277,13 @@ You can install the latest release of grid2demand at [PyPI](https://pypi.org/pro

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pip install grid2demand
`pip install grid2demand`

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After running the command above, the grid2demand package along with two required dependency packages (numpy, pandas) will be installed on your computer (if they have not been installed yet).

**Step 2: Determine the boundary of interest and download .osm file from
OpenStreetMap (https://www.openstreetmap.org/)**
**Step 2: Determine the boundary of interest and download .osm file from OpenStreetMap (https://www.openstreetmap.org/)**

1. Adjust the map to the location of interest and click on the “Export” button on the top.

Expand All @@ -308,9 +301,16 @@ Open the Python IDE, such as Pycharm, for a typical configuration. Then, use OSM

Notes: User guide for osm2gmns can be found at https://osm2gmns.readthedocs.io/en/latest/.

<img src="docs/media/6ea2cbc0b48354d4940c8e4f4c6e9f58.png" style="zoom:80%;" />
```python
import osm2gmns as og

# load network from osm file
net = og.getNetFromFile("map.osm", network_type=('railway', 'aeroway', 'auto', 'walk', 'bile'), POI=True, default_lanes=True, default_speed=True)
og.connectPOIWithNet(net)
og.outputNetToCSV(net, output_folder)
```

<img src="docs/media/bc783b917bac32c27b66e4649a472088.png" style="zoom:80%;" />
Your will see node.csv, poi.csv and link.csv in your output_folder<img src="docs/media/bc783b917bac32c27b66e4649a472088.png" style="zoom:80%;" />

<img src="docs/media/bff56bb4b68eef97dfb8ee4a337500bb.png" alt=" " style="zoom:80%;" />

Expand All @@ -333,14 +333,11 @@ net = gd.GRID2DEMAND(input_dir)

1. **Partition network into grid cells**

Users can customize the number of grid cells by setting “number_of_x_blocks” and
“number_of_y_blocks”. On the other hand, users can customize the cell’s width
and height in meters under the latitude of the area by setting “cell_width”,
“cell_height” and “latitude”).
Users can customize the number of grid cells by setting “num_x_blocks” and
“num_y_blocks”.

By default, “cell_width” and “cell_height” are set as the length on a flat
surface under a specific latitude corresponding to the degree of 0.006
(equivalent to 400 meters or 0.25 miles at latitude = 45 degree).
Users can customize the cell’s width and height in prefered units by setting “cell_width”,
“cell_height” and "unit").

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Expand All @@ -351,6 +348,12 @@ net.load_network()
# create zone
net.net2zone(num_x_blocks=10,num_y_blocks=10)

# or
# net.net2zone(net.node_dict, num_x_blocks=10, num_y_blocks=10)

# or generate zone based on grid size with 10 km width and 10 km height for each zone
# net.net2zone(cell_width=10, cell_height=10, unit='km')

# Synchronize geometry info between zone, node and poi
net.sync_geometry_between_zone_and_node_poi()

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