Using the Python TRACC Library for Urban Transportation Accessibility Analysis

As cities grow, transportation accessibility becomes a key indicator for urban development and planning. By analyzing transport networks and destination distributions, we can assess how easily people can reach jobs, services, and other opportunities.

TRACC Overview

TRACC is an open‑source Python library focused on city‑scale accessibility analysis. It combines land‑use data (e.g., jobs, population, shops, medical facilities) with pre‑computed travel costs (e.g., travel time, transit fares) to generate accessibility indicators. Most operations are performed on pandas DataFrames.

The library can compute three types of accessibility:

Potential accessibility: weighted sum of opportunities reachable within a given travel time.

Passive accessibility: weighted sum of population that can reach a location within a given travel time.

Minimum travel‑cost accessibility: the smallest travel cost to reach a set number of opportunities.

Additional features include estimating regional travel costs, filling gaps in travel‑cost matrices using spatial weight matrices, generating impedance functions (cumulative, linear, negative exponential, inverse power), and calculating generalized costs.

Installation

pip install tracc

Data Preparation

Two datasets are required: destination data (e.g., employment counts from LEHD for Boston) and travel‑cost data (e.g., public‑transport travel‑time matrix between zones).

Analysis Workflow

Load data : # Load destination data dfo = tracc.supply( supply_df=pd.read_csv("examples/test_data/boston/destination_employment_lehd.csv"), columns=["block_group_id", "C000"] # C000 = total jobs ) # Load travel‑cost data dft = tracc.costs( pd.read_csv("examples/test_data/boston/transit_time_matrix_8am_30_06_2020.zip", compression='zip') ) dft.data.time = dft.data.time / 60 # convert seconds to minutes

Compute impedance function : dft.impedence_calc( cost_column="time", impedence_func="cumulative", impedence_func_params=45, output_col_name="fCij_c45", prune_output=False )

Set up accessibility object : acc = tracc.accessibility( travelcosts_df=dft.data, supply_df=dfo.data, travelcosts_ids=["o_block", "d_block"], supply_ids="block_group_id" )

Calculate accessibility : dfa = acc.potential( opportunity="C000", impedence="fCij_c45" )

The first five rows of dfa show, for example, that from block group 250056001001 a person can reach 4,061 jobs within a 45‑minute travel time.

<code>    o_block       A_C000_fCij_c45
---------------------------------
0   250056001001   4061.0
1   250056001002   3960.0
2   250056002021   3608.0
3   250056002022   7845.0
4   250056002023   5124.0
</code>

These results can be visualized in Python, QGIS, or any GIS software to map job accessibility across the city, aiding planners and policymakers in optimizing transport resources and improving quality of life.

TRACC provides a powerful, flexible tool for transportation accessibility studies and can be extended for various research purposes.