Use examples#

This page is still under development, so if you have developed interesting use cases, please consider contributing them.

Note

The examples provided here are not meant as a through description of AequilibraE’s capabilities. For that, please look into the API documentation or email aequilibrae@googlegroups.com

Sample Data#

We have compiled two very distinct example datasets imported from the TNTP instances.

While the Sioux Falls network is probably the most traditional example network available for evaluating network algorithms, the Chicago Regional model is a good example of a real-world sized model, with roughly 1,800 zones.

Each instance contains the following folder structure and contents:

0_tntp_data:

  • Data imported from TNTP instances.

  • matrices in openmatrix and AequilibraE formats

  • vectors computed from the matrix in question and in AequilibraE format

  • No alterations made to the data

1_project

  • AequilibraE project result of the import of the links and nodes layers

2_skim_results:

  • Skim results for distance and free_flow_travel_time computed by minimizing free_flow_travel_time

  • Result matrices in openmatrix and AequilibraE formats

3_desire_lines

  • Layers for desire lines and delaunay lines, each one in a separate geopackage file

  • Desire lines flow map

  • Delaunay Lines flow map

4_assignment_results

  • Outputs from traffic assignment to a relative gap of 1e-5 and with skimming enabled

  • Link flows in csv and AequilibraE formats

  • Skim matrices in openmatrix and AequilibraE formats

  • Assignment flow map in png format

5_distribution_results

  • Models calibrated for inverse power and negative exponential deterrence functions

  • Convergence logs for the calibration of each model

  • Trip length frequency distribution chart for original matrix

  • Trip length frequency distribution chart for model with negative exponential deterrence function

  • Trip length frequency distribution chart for model with inverse power deterrence function

  • Inputs are the original demand matrix and the skim for TIME (final iteration) from the ASSIGNMENT

6_forecast

  • Synthetic future vectors generated with a random growth from 0 to 10% in each cell on top of the original matrix vectors

  • Application of both gravity models calibrated plus IPF to the synthetic future vectors

7_future_year_assignment

  • Traffic assignment

    • Outputs from traffic assignment to a relative gap of 1e-5 and with skimming enabled

    • Link flows in csv and AequilibraE formats

    • Skim matrices in openmatrix and AequilibraE formats

  • Scenario comparison flow map of absolute differences

  • Composite scenario comparison flow map (gray is flow maintained in both scenarios, red is flow growth and green is flow decline)