Handling GTFS (General Transit Feed Specification) Data with DuckDB

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GTFS (General Transit Feed Specification) is a common format for public transportation schedules and geographic data. DuckDB, an in-process SQL OLAP database, offers efficient handling of GTFS datasets. This article explores how to process GTFS data using DuckDB with practical commands and code snippets.

You Should Know:

1. Installing DuckDB

DuckDB can be installed via command line:

 Linux/macOS 
wget https://github.com/duckdb/duckdb/releases/download/v0.9.2/duckdb_cli-linux-amd64.zip 
unzip duckdb_cli-linux-amd64.zip 
./duckdb

Windows (PowerShell) 
Invoke-WebRequest -Uri "https://github.com/duckdb/duckdb/releases/download/v0.9.2/duckdb_cli-windows-amd64.zip" -OutFile "duckdb.zip" 
Expand-Archive -Path "duckdb.zip" -DestinationPath . 
.\duckdb.exe

2. Loading GTFS Data

GTFS data is typically in CSV format. Use DuckDB to load it directly: 

-- Create a table from a GTFS stops.csv file 
CREATE TABLE stops AS SELECT  FROM read_csv('stops.csv');

-- Query stops data 
SELECT stop_name, stop_lat, stop_lon FROM stops LIMIT 10;

3. Efficient Querying with DuckDB

DuckDB supports advanced SQL operations for transit data analysis: 

-- Find the 10 most frequented stops 
SELECT stop_id, COUNT() as trip_count 
FROM stop_times 
GROUP BY stop_id 
ORDER BY trip_count DESC 
LIMIT 10;

-- Spatial query (if GTFS has coordinates) 
SELECT stop_name 
FROM stops 
WHERE ST_Distance( 
ST_Point(stop_lon, stop_lat), 
ST_Point(-74.0060, 40.7128) 
) < 1000; -- Within 1km of NYC

4. Exporting Processed Data

After analysis, export results to Parquet for efficient storage: 

COPY (SELECT  FROM stops) TO 'stops.parquet' (FORMAT PARQUET);

5. Automating with Bash

Use a shell script to process multiple GTFS files: 

!/bin/bash 
duckdb -c "CREATE TABLE routes AS SELECT  FROM read_csv('routes.csv');" 
duckdb -c "COPY (SELECT route_id, route_short_name FROM routes) TO 'routes_short.parquet' (FORMAT PARQUET);"

What Undercode Say:

DuckDB simplifies GTFS data processing by enabling SQL queries on CSV/Parquet files without a full database setup. For transit agencies, combining DuckDB with cloud storage (S3, GCS) improves scalability. Future enhancements may include real-time GTFS-RT processing with DuckDB’s streaming capabilities.

Prediction:

As cities adopt smarter transit systems, DuckDB will become a key tool for analyzing large-scale GTFS datasets efficiently, replacing traditional RDBMS for ad-hoc transit analytics.

Expected Output:

  • Processed GTFS data in Parquet format.
  • SQL-based transit analytics.
  • Automated data pipelines using DuckDB and shell scripts.

Reference: Handling GTFS data with DuckDB

References:

Reported By: Tobiasmuellerlg Handling – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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