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)