How to install and set up gtfs_functions for GTFS analysis?

Install via pip with 'pip install gtfs_functions' and ensure Python>=3.8. Use a conda environment (e.g., 'conda create -n new-env python=3.8') as specified in the README for compatibility.

What's the best way to map GTFS data with gtfs_functions?

Use the map_gdf function for basic stop or line frequency maps, or integrate with kepler.gl for interactive visualizations. The README shows code snippets for both approaches, including passing GeoDataFrames to kepler.gl.

gtfs_functions vs partridge: which is better for GTFS parsing?

gtfs_functions offers a simpler, dependency-light interface with built-in geospatial outputs, while partridge provides more features for complex GTFS manipulations. Choose gtfs_functions for streamlined analysis and mapping, partridge for advanced data handling.

Can I use gtfs_functions to analyze real-time transit performance?

No, it only handles scheduled GTFS data. For real-time analysis, you'd need to combine it with other libraries or APIs that process live feeds, as it focuses on schedule-based insights.

How to fix Null values in GTFS data for gtfs_functions?

Preprocess your GTFS files using tools like pandas to clean stop_times.txt, ensuring no Nulls in arrival_time and departure_time columns, as the library may otherwise fail, per the README warning.

What are some practical examples of using gtfs_functions in transit planning?

Common examples include calculating stop frequencies for service adjustments, mapping route frequencies to identify high-demand corridors, and analyzing scheduled speeds for performance reports, as detailed in the best-for scenarios.

Open-Awesome

gtfs_functions

MITPython

A Python package for parsing GTFS data and creating geospatial visualizations of transit frequencies, speeds, and segments.

GitHub

140 stars33 forks0 contributors

What is gtfs_functions?

GTFS functions is a Python package that parses GTFS (General Transit Feed Specification) data and creates geospatial visualizations. It transforms raw transit schedules into analyzable layers like stop frequencies, line frequencies, and segment speeds, helping users understand transit network performance and patterns.

Target Audience

Transit planners, data analysts, researchers, and developers working with public transportation data who need to analyze and visualize GTFS schedules.

Value Proposition

It offers a simplified, dependency-light approach to GTFS analysis with an intuitive class-based interface, built-in geospatial outputs, and visualization tools, making it easier to derive insights than working with raw GTFS files directly.

Overview

Package with useful functions to create geo-spatial visualizations from a GTFS.

Use Cases

Best For

Calculating stop-level transit frequencies for service planning
Mapping route frequencies and identifying high-demand corridors
Analyzing scheduled speeds and performance across transit segments
Creating geospatial visualizations of GTFS data for reports or dashboards
Extracting bus segments and distances for network analysis
Integrating GTFS analysis into Python-based transit research workflows

Not Ideal For

Projects requiring real-time transit data integration or live feed analysis
Applications built in non-Python environments like JavaScript or web-based dashboards
Teams needing extensive custom statistical modeling beyond frequency and speed calculations
Scenarios where GTFS data is dirty or contains Null values without preprocessing

Pros & Cons

Pros

Clean Object-Oriented Interface

The Feed class exposes GTFS files as properties (e.g., feed.routes, feed.stops), simplifying data access and manipulation, as demonstrated in the README examples with easy DataFrame queries.

Built-in Geospatial Outputs

Functions return GeoDataFrames with Point or LineString geometries (e.g., stop_freq with Points, line_freq with LineStrings), ready for mapping without extra conversion steps.

Minimal Dependency Footprint

Removed external dependencies like partridge for more control and stability, reducing failure points, as noted in the March 2023 update prioritizing simplicity.

Integrated Visualization Tools

Includes map_gdf for basic plotting and seamless kepler.gl integration for advanced maps, shown in the README with examples for speeds and segment frequencies.

Cons

Data Quality Assumptions

Fails if stop_times.txt has Null values in arrival_time/departure_time, requiring manual data cleaning—a significant limitation highlighted in the warning section.

No Real-Time Capabilities

Only processes scheduled GTFS data, lacking support for real-time transit updates or historical performance tracking, limiting dynamic analysis use cases.

External Visualization Dependency

Advanced mapping relies on kepler.gl, which demands separate installation and learning, adding complexity for users wanting out-of-the-box visuals.

Frequently Asked Questions

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