grid2demand 0.2.5

A tool for generating zone-to-zone travel demand based on grid cells

GRID2DEMAND: A tool for generating zone-to-zone travel demand based on grid cells

Introduction

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 for teaching transportation planning and applications. By taking advantage of OSM2GMNS tool to obtain routable 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

Quick Start

Users can refer to the code template and test data set to have a quick start.

Installation

pip install grid2demand

If you meet installation issues, please refer to the user guide for solutions.

Simple Example

import grid2demand as gd

"Step 1: Read Input Network Data"
gd.ReadNetworkFiles()

"Step 2: Zone Partition"
# Option 1: Partition grid into cells (users can customize number of grid cells or cell's width and height in meters)
gd.PartitionGrid(number_of_x_blocks=None, number_of_y_blocks=None, cell_width=1000, cell_height=1000)
# Option 2: Partition based on Traffic Analysis Zone boundaries with an external input TAZ.csv
gd.TAZ2zone()

"Step 3: Get Production/Attraction Rates of Each Land Use Type with a Specific Trip Purpose"
gd.GetPoiTripRate(trip_purpose=1)
# users can customize trip purpose and poi_trip_rate.csv

"Step 4: Define Production/Attraction Value of Each Node According to POI Type"
gd.GetNodeDemand(residential_production=20, residential_attraction=20, boundary_production=1000, boundary_attraction=1000)
# users can customize production and attraction values of residential nodes and boundary nodes

"Step 5: Calculate Zone-to-zone Accessibility Matrix by Centroid-to-centroid Straight Distance"
gd.ProduceAccessMatrix()

"Step 6: Apply Gravity Model to Perform Trip Distribution"
gd.RunGravityModel(trip_purpose=1, a=None, b=None, c=None)
# users can customize friction factor coefficients under a specific trip purpose

"Step 7: Generate Agent"
gd.GenerateAgentBasedDemand()

Visualization

Option 1: Open QGIS and add Delimited Text Layer of the files.

Option 2: Upload files to the website of ASU Trans+AI Lab and view input and output files.

Option 3: Import input_agent.csv to A/B Street and view dynamic simulation of the demand.

User guide

Users can check the user guide for a detailed introduction of grid2demand.