grid2demand 0.3.4

A tool for generating zone-to-zone travel demand based on grid zones and gravity model

Project description

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

from __future__ import absolute_import
from grid2demand import GRID2DEMAND


if __name__ == "__main__":
    path_node = "./dataset/ASU/node.csv"
    path_poi = "./dataset/ASU/poi.csv"
    input_dir = "./dataset/ASU"

    # Step 1: Load node and poi files from input directory
    # There are two ways to load node and poi files: 1. Load from input directory; 2. Load from specified path
    gd = GRID2DEMAND(input_dir)

    # Step 1.1: Load from specified path
    node_dict = gd.read_node("./dataset/ASU/node.csv")
    poi_dict = gd.read_poi("./dataset/ASU/poi.csv")

    # Step 2: Generate zone dictionary from node dictionary by specifying number of x blocks and y blocks
    # To be noticed: num_x_blocks and num_y_blocks have higher priority than cell_width and cell_height
    # if num_x_blocks and num_y_blocks are specified, cell_width and cell_height will be ignored
    zone_dict = gd.net2zone(node_dict, num_x_blocks=10, num_y_blocks=10, cell_width=0, cell_height=0)
    # zone_dict = gd.net2zone(node_dict, cell_width=10, cell_height=10)  # This will generate zone based on grid size 10km width and 10km height

    # Step 3: synchronize zone with node and poi
    # will add zone_id to node and poi dictionaries
    # Will also add node_list and poi_list to zone dictionary
    # Step 3.1: synchronize zone with node
    update_dict = gd.sync_geometry_between_zone_and_node_poi(zone_dict, node_dict, poi_dict)
    zone_dict_update = update_dict.get('zone_dict')
    node_dict_update = update_dict.get('node_dict')
    poi_dict_update = update_dict.get('poi_dict')

    # Step 4: Generate zone-to-zone od distance matrix
    zone_od_distance_matrix = gd.calc_zone_od_distance_matrix(zone_dict_update)

    # Step 5: Generate poi trip rate for each poi
    poi_trip_rate = gd.gen_poi_trip_rate(poi_dict_update)

    # Step 6: Generate node production attraction for each node based on poi_trip_rate
    node_prod_attr = gd.gen_node_prod_attr(node_dict_update, poi_trip_rate)

    # Step 6.1: Calculate zone production and attraction based on node production and attraction
    zone_prod_attr = gd.calc_zone_production_attraction(node_prod_attr, zone_dict_update)

    # Step 7: Run gravity model to generate agent-based demand
    df_demand = gd.run_gravity_model(zone_prod_attr, zone_od_distance_matrix)

    # Step 8: generate agent-based demand
    df_agent = gd.gen_agent_based_demand(node_prod_attr, zone_prod_attr, df_demand=df_demand)

    # You can also view and edit the package setting by using gd.pkg_settings
    print(gd.pkg_settings)

    # Step 9: Output demand, agent, zone, zone_od_dist_table, zone_od_dist_matrix files to output directory
    gd.save_demand
    gd.save_agent
    gd.save_zone
    gd.save_zone_od_dist_table
    gd.save_zone_od_dist_matrix

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.