Using the Python TRACC Library for Urban Transportation Accessibility Analysis

As cities grow, transportation accessibility becomes a key indicator for urban planning; the TRACC library offers powerful tools to load, process, and analyze transport data for evaluating accessibility across regions.

TRACC Overview – an open‑source Python package focused on urban transport accessibility, combining land‑use data (e.g., jobs, services) with pre‑computed travel‑cost matrices to generate three main metrics: potential accessibility, passive accessibility, and minimum travel‑cost accessibility.

Key Features

Estimate travel costs within an area

Fill gaps in travel‑cost matrices using spatial weight matrices

Generate impedance functions (cumulative, linear, negative exponential, inverse power)

Compute generalized costs

Installation

pip install tracc

Data Preparation – two datasets are required: destination data (e.g., employment counts from LEHD) and travel‑cost data (e.g., public‑transport travel‑time matrix for Boston).

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.

These results can be visualized in Python, QGIS, or other GIS tools to help planners understand travel patterns and improve resource allocation.

TRACC thus provides a convenient, extensible framework for transportation accessibility analysis, supporting customized studies and visualizations for urban planners and policymakers.