gtfs-via-postgres

Import GTFS Static/Schedule datasets into a PostgreSQL database, to allow for efficient querying and analysis.

✅ handles daylight saving time correctly but retains reasonable lookup performance
✅ supports frequencies.txt
✨ joins stop_times.txt/frequencies.txt, calendar.txt/calendar_dates.txt, trips.txt, route.txt & stops.txt into views for straightforward data analysis (see below)
🚀 is carefully optimised to let PostgreSQL's query planner do its magic, yielding quick lookups even with large datasets (see performance section)
✅ validates and imports translations.txt
✨ exposes (almost) all data via GraphQL using PostGraphile, and as a RESTful API using PostgREST

Installation

npm install -g gtfs-via-postgres

Or use npx. ✨

There are also prebuilt binaries and Docker images available.

Note: gtfs-via-postgres needs PostgreSQL >=14 to work, as it uses the WITH … AS NOT MATERIALIZED syntax. You can check your PostgreSQL server's version with psql -t -c 'SELECT version()'.

Getting Started

If you have a .zip GTFS feed, unzip it into individual files.

We're going to use the 2022-07-01 VBB feed as an example, which consists of individual files already.

wget --compression auto \
    -r --no-parent --no-directories -R .csv.gz \
    -P gtfs -N 'https://vbb-gtfs.jannisr.de/2022-07-01/'
# …
# Downloaded 14 files in 20s.
ls -lh gtfs
# 3.3K agency.csv
#  97K calendar.csv
# 1.1M calendar_dates.csv
# 2.5K datapackage.json
#  64B frequencies.csv
# 5.9K levels.csv
# 246B license
# 8.3M pathways.csv
#  49K routes.csv
# 146M shapes.csv
# 368M stop_times.csv
# 5.0M stops.csv
# 4.7M transfers.csv
#  16M trips.csv

Depending on your specific setup, configure access to the PostgreSQL database via PG* environment variables:

export PGUSER=postgres
export PGPASSWORD=password
env PGDATABASE=postgres psql -c 'create database vbb_2022_02_25'
export PGDATABASE=vbb_2022_02_25

Install gtfs-via-postgres and use it to import the GTFS data:

npm install -D gtfs-via-postgres
npm exec -- gtfs-to-sql --require-dependencies -- gtfs/*.csv | sponge | psql -b
# agency
# calendar
# CREATE EXTENSION
# BEGIN
# CREATE TABLE
# COPY 37
# …
# CREATE INDEX
# CREATE VIEW
# COMMIT

Importing will take 10s to 10m, depending on the size of the feed. On an M1 MacBook Air, importing the above feed takes about 4m; Importing the 260kb 2021-10-06 Amtrak feed takes 6s.

In addition to a table for each GTFS file, gtfs-via-postgres adds these views to help with real-world analysis:

service_days (materialized) "applies" calendar_dates to calendar to give you all days of operation for each "service" defined in calendar.
arrivals_departures "applies" stop_times/frequencies to trips and service_days to give you all arrivals/departures at each stop with their absolute dates & times. It also resolves each stop's parent station ID & name.
connections "applies" stop_times/frequencies to trips and service_days, just like arrivals_departures, but gives you departure (at stop A) & arrival (at stop B) pairs.
shapes_aggregates aggregates individual shape points in shapes into a PostGIS LineString.
stats_by_route_date provides the number of arrivals/departures by route ID and date. – read more
stats_by_agency_route_stop_hour provides the number of arrivals/departures by agency ID, route ID, stop ID & hour. – read more
In contrast to stats_by_route_date & stats_by_agency_route_stop_hour, stats_active_trips_by_hour provides the number of currently running trips for each hour in the feeds period of time.

As an example, we're going to use the arrivals_departures view to query all absolute departures at de:11000:900120003 (S Ostkreuz Bhf (Berlin)) between 2022-03-23T12:30+01 and 2022-03-23T12:35+01:

SELECT *
FROM arrivals_departures
WHERE station_id = 'de:11000:900120003'
AND t_departure >= '2022-03-23T12:30+01' AND t_departure <= '2022-03-23T12:35+01'

`route_id`	`route_short_name`	`route_type`	`trip_id`	`date`	`stop_sequence`	`t_arrival`	`t_departure`	`stop_id`	`stop_name`	`station_id`	`station_name`
`10148_109`	`S3`	`109`	`169035756`	`2022-03-23 00:00:00`	`19`	`2022-03-23 12:31:24+01`	`2022-03-23 12:32:12+01`	`de:11000:900120003:2:53`	`S Ostkreuz Bhf (Berlin)`	`de:11000:900120003`	`S Ostkreuz Bhf (Berlin)`
`10148_109`	`S3`	`109`	`169035899`	`2022-03-23 00:00:00`	`10`	`2022-03-23 12:33:06+01`	`2022-03-23 12:33:54+01`	`de:11000:900120003:3:55`	`S Ostkreuz Bhf (Berlin)`	`de:11000:900120003`	`S Ostkreuz Bhf (Berlin)`
`10162_109`	`S7`	`109`	`169128381`	`2022-03-23 00:00:00`	`19`	`2022-03-23 12:33:54+01`	`2022-03-23 12:34:42+01`	`de:11000:900120003:2:53`	`S Ostkreuz Bhf (Berlin)`	`de:11000:900120003`	`S Ostkreuz Bhf (Berlin)`
`10162_109`	`S7`	`109`	`169128495`	`2022-03-23 00:00:00`	`9`	`2022-03-23 12:30:36+01`	`2022-03-23 12:31:24+01`	`de:11000:900120003:3:55`	`S Ostkreuz Bhf (Berlin)`	`de:11000:900120003`	`S Ostkreuz Bhf (Berlin)`
`10223_109`	`S41`	`109`	`169054370`	`2022-03-23 00:00:00`	`21`	`2022-03-23 12:30:24+01`	`2022-03-23 12:31:12+01`	`de:11000:900120003:5:58`	`S Ostkreuz Bhf (Berlin)`	`de:11000:900120003`	`S Ostkreuz Bhf (Berlin)`
`10227_109`	`S42`	`109`	`169071882`	`2022-03-23 00:00:00`	`6`	`2022-03-23 12:30:30+01`	`2022-03-23 12:31:12+01`	`de:11000:900120003:5:59`	`S Ostkreuz Bhf (Berlin)`	`de:11000:900120003`	`S Ostkreuz Bhf (Berlin)`
`19040_100`	`RB14`	`100`	`178748721`	`2022-03-23 00:00:00`	`13`	`2022-03-23 12:30:00+01`	`2022-03-23 12:30:00+01`	`de:11000:900120003:1:50`	`S Ostkreuz Bhf (Berlin)`	`de:11000:900120003`	`S Ostkreuz Bhf (Berlin)`
`22664_2`	`FEX`	`2`	`178748125`	`2022-03-23 00:00:00`	`1`	`2022-03-23 12:32:00+01`	`2022-03-23 12:34:00+01`	`de:11000:900120003:4:57`	`S Ostkreuz Bhf (Berlin)`	`de:11000:900120003`	`S Ostkreuz Bhf (Berlin)`

translations

There are some …_translated views (e.g. stops_translated, arrivals_departures_translated) that

join their respective source table with translations, so that each (translatable) field is translated in every provided language,
add a …_lang column for each translated column (e.g. stop_name_lang for stop_name) that indicates the language of the translation.

Assuming a dataset with translations.csv, let's query all stops with a de-CE translation, falling back to the untranslated values:

SELECT
    stop_id,
    stop_name, stop_name_lang,
    stop_url,
FROM stops_translated
WHERE (stop_name_lang = 'de-CH' OR stop_name_lang IS NULL)
AND (stop_url_lang = 'de-CH' OR stop_url_lang IS NULL)

Usage

Usage:
    gtfs-to-sql [options] [--] <gtfs-file> ...
Options:
    --silent                  -s  Don't show files being converted.
    --require-dependencies    -d  Require files that the specified GTFS files depend
                                  on to be specified as well (e.g. stop_times.txt
                                  requires trips.txt). Default: false
    --ignore-unsupported      -u  Ignore unsupported files. Default: false
    --route-types-scheme          Set of route_type values to support.
                                    - basic: core route types in the GTFS spec
                                    - google-extended: Extended GTFS Route Types [1]
                                    - tpeg-pti: proposed TPEG-PTI-based route types [2]
                                    Default: google-extended
    --trips-without-shape-id      Don't require trips.txt items to have a shape_id.
                                    Default if shapes.txt has not been provided.
    --routes-without-agency-id    Don't require routes.txt items to have an agency_id.
    --stops-without-level-id      Don't require stops.txt items to have a level_id.
                                    Default if levels.txt has not been provided.
    --stops-location-index        Create a spatial index on stops.stop_loc for efficient
                                    queries by geolocation.
    --lower-case-lang-codes       Accept Language Codes (e.g. in feed_info.feed_lang)
                                    with a different casing than the official BCP-47
                                    language tags (as specified by the GTFS spec),
                                    by lower-casing all of them before validating.
                                    http://www.rfc-editor.org/rfc/bcp/bcp47.txt
                                    http://www.w3.org/International/articles/language-tags/
    --stats-by-route-date         Wether to generate a stats_by_route_date view
                                    letting you analyze all data per routes and/or date:
                                    - none: Don't generate a view.
                                    - view: Fast generation, slow access.
                                    - materialized-view: Slow generation, fast access.
                                    Default: none
    --stats-by-agency-route-stop-hour
                                  Generate a view letting you analyze arrivals/
                                    departures per route, stop and hour.
                                    The flag works like --stats-by-route-date.
    --stats-active-trips-by-hour  Generate a view letting you analyze the number of
                                    currently running trips over time, by hour.
                                    Like --stats-by-route-date, this flag accepts
                                    none, view & materialized-view.
    --schema                      The schema to use for the database. Default: public
                                    Even when importing into a schema other than `public`,
                                    a function `public.gtfs_via_postgres_import_version()`
                                    gets created, to ensure that multiple imports into the
                                    same database are all made using the same version. See
                                    also multiple-datasets.md in the docs.
    --postgraphile                Tweak generated SQL for PostGraphile usage.
                                    https://www.graphile.org/postgraphile/
    --postgraphile-password       Password for the PostGraphile PostgreSQL user.
                                    Default: $POSTGRAPHILE_PGPASSWORD, fallback random.
    --postgrest                   Tweak generated SQL for PostgREST usage.
                                    Please combine it with --schema.
                                    https://postgrest.org/
    --postgrest-password          Password for the PostgREST PostgreSQL user `web_anon`.
                                    Default: $POSTGREST_PGPASSWORD, fallback random.
    --postgrest-query-cost-limit  Define a cost limit [1] for queries executed by PostgREST
                                    on behalf of a user. It is only enforced if
                                    pg_plan_filter [2] is installed in the database!
                                    Must be a positive float. Default: none
                                    [1] https://www.postgresql.org/docs/14/using-explain.html
                                    [2] https://github.com/pgexperts/pg_plan_filter
    --import-metadata             Create functions returning import metadata:
                                    - gtfs_data_imported_at (timestamp with time zone)
                                    - gtfs_via_postgres_version (text)
                                    - gtfs_via_postgres_options (jsonb)
Examples:
    gtfs-to-sql some-gtfs/*.txt | sponge | psql -b # import into PostgreSQL
    gtfs-to-sql -u -- some-gtfs/*.txt | gzip >gtfs.sql.gz # generate a gzipped SQL dump

[1] https://developers.google.com/transit/gtfs/reference/extended-route-types
[2] https://groups.google.com/g/gtfs-changes/c/keT5rTPS7Y0/m/71uMz2l6ke0J

Some notable limitations mentioned in the PostgreSQL 14 documentation on date/time types:

For timestamp with time zone, the internally stored value is always in UTC (Universal Coordinated Time, traditionally known as Greenwich Mean Time, GMT). An input value that has an explicit time zone specified is converted to UTC using the appropriate offset for that time zone.

When a timestamp with time zone value is output, it is always converted from UTC to the current timezone zone, and displayed as local time in that zone. To see the time in another time zone, either change timezone or use the AT TIME ZONE construct […].

You can run queries with date+time values in any timezone (offset) and they will be processed correctly, but the output will always be in the database timezone (offset), unless you have explicitly used AT TIME ZONE.

With Docker

Note: Just like the npm-installed variant, the Docker integration too assumes that your GTFS dataset consists of individual files (i.e. unzipped).

Instead of installing via npm, you can use the ghcr.io/public-transport/gtfs-via-postgres Docker image:

# variant A: use Docker image just to convert GTFS to SQL
docker run --rm --volume /path/to/gtfs:/gtfs \
	ghcr.io/public-transport/gtfs-via-postgres --require-dependencies -- '/gtfs/*.csv' \
    | sponge | psql -b

Note: Remember to pass the /gtfs/*.csv glob as a string (with '), so that it gets evaluated inside the Docker container.

With the code above, the psql -b process will run outside of the Docker container, so your host machine needs access to PostgreSQL.

If you want to directly import the GTFS data from within the Docker container, you need add psql to the image and run it from inside. To do that, write a new Dockerfile that extends the ghcr.io/public-transport/gtfs-via-postgres image:

FROM ghcr.io/public-transport/gtfs-via-postgres
ENV PGPORT=5432 PGUSER=postgres
WORKDIR /gtfs
# pass all arguments into gtfs-via-postgres, pipe output into psql:
ENTRYPOINT ["/bin/sh", "-c", "gtfs-via-postgres $0 $@ | sponge | psql -b"]

# start PostgreSQL DB in another container "db"
docker run --name db -p 5432:5432 -e POSTGRES_PASSWORD=password postgis/postgis

# variant B: use Docker image to convert GTFS to SQL and import it directly
docker build -t import-gtfs . # build helper Docker image from Dockerfile
docker run --rm --volume /path/to/gtfs:/gtfs \
	--link db -e PGHOST=db -e PGPASSWORD=password \
	import-gtfs --require-dependencies -- '/gtfs/*.csv'

Exporting data efficiently

If you want to export data from the database, use the COPY command; On an M1 MacBook Air, PostgreSQL 14 can export about 500k connections rows per second.

psql -c 'COPY (SELECT * FROM connections) TO STDOUT csv HEADER' >connections.csv

In the nested SELECT query, you can use features like WHERE, ORDER BY and LIMIT. Because psql passes on the exported data right away, you could stream it into another process.

Querying stops by location efficiently

If you want to find stops by (geo)location, run gtfs-via-postgres with --stops-location-index. This will create a spatial index on stops.stop_loc, so that most PostGIS functions & operators make use of it.

GraphQL support

The --postgraphile flag changes the SQL generated by gtfs-via-postgres slightly, so that you get a reasonably idiomatic GraphQL API out-of-the-box when running PostGraphile v4 on it:

# import data into PostgreSQL with PostGraphile tweaks
npm exec -- gtfs-to-sql -d --postgraphile -- gtfs/*.csv | sponge | psql -b

In line with the intended PostGraphile usage, gtfs-via-postgres will create a PostgreSQL role/user postgraphile with read-only access to the DB. You can set the postgraphile's password with the --postgraphile-password option, or using the $POSTGRAPHILE_PGPASSWORD environment variable; By default, it will use (and log) a random password.

gtfs-via-postgres doesn't specify PostGraphile as a regular dependency, but as peerDependencies, in order to stay lightweight for users who don't need the GraphQL interface. Some versions of some package managers install unmet peer dependencies, some don't. Let's make sure that PostGraphile (and its plugins) are installed:

npm install \
    postgraphile@4.12 \
    @graphile-contrib/pg-simplify-inflector@^6.1 \
    @graphile/postgis@^0.2.0-0

The serve-gtfs-via-graphql helper script configures and runs PostGraphile. With NODE_ENV=development, it will

serve a fully configured GraphiQL UI at /graphiql
provide more errors on database & query errors
allow using PostgreSQL's EXPLAIN via GraphQL

# listens on port 3000, this can be changed using $PORT
env NODE_ENV=development npm exec -- serve-gtfs-via-graphql

As an example for the GraphQL API, check out the test query or open the GraphiQL UI served at localhost:3000/graphiql.

REST API support

With the --postgrest flag, gtfs-via-postgres will augment the schema with a web_anon role and some comments, so that when running PostgREST on the database, you will get a powerful REST API.

more guides

The docs directory contains more instructions on how to use gtfs-via-postgres.

Correctness vs. Speed regarding GTFS Time Values

When matching time values from stop_times against dates from calendar/calendar_dates, you have to take into account that GTFS Time values can be >24h and are not relative to the beginning of the day but relative to noon - 12h. (There are a few libraries that don't do this.)

This means that, in order to determine all absolute points in time where a particular trip departs at a particular stop, you cannot just loop over all "service dates" and add the time value (as in beginning_of_date + departure_time); Instead, for each date, you have to determine noon, subtract 12h and then apply the time, which might extend arbitrarily far into the following days.

Let's consider two examples:

A departure_time of 26:59:00 with a trip running on 2021-03-01: The time, applied to this specific date, "extends" into the following day, so it actually departs at 2021-03-02T02:59+01.
A departure time of 03:01:00 with a trip running on 2021-03-28: This is when the standard -> DST switch happens in the Europe/Berlin timezone. Because the dep. time refers to noon - 12h (not to midnight), it actually happens at 2021-03-28T03:01+02 which is not 3h1m after 2021-03-28T00:00+01.

gtfs-via-postgres always prioritizes correctness over speed. Because it follows the GTFS semantics, when filtering arrivals_departures by absolute departure date+time, it cannot automatically filter service_days (which is calendar and calendar_dates combined), because even a date before the date of the desired departure time frame might still end up within, when combined with a departure_time of e.g. 27:30:00; Instead, it has to consider all service_days and apply the departure_time to all of them to check if they're within the range.

However, if you determine your feed's largest arrival_time/departure_time, you can filter on date when querying arrivals_departures; This allows PostgreSQL to reduce the number of joins and calendar calculations by orders of magnitude, speeding up your queries significantly. gtfs-via-postgres provides two helper functions largest_arrival_time() & largest_departure_time() for this.

For example, when querying all absolute departures at de:11000:900120003 (S Ostkreuz Bhf (Berlin)) between 2022-03-23T12:30+01 and 2022-03-23T12:35+01 within the 2022-02-25 VBB feed, filtering by date speeds it up nicely (Apple M1, PostgreSQL 14.2):

`station_id` filter	`date` filter	query time	nr of results
`de:11000:900120003`	none	230ms	~574k
`de:11000:900120003`	`2022-03-13` >= `date` < `2022-04-08`	105ms	~51k
`de:11000:900120003`	`2022-03-23` >= `date` < `2022-03-24`	55ms	~2k
`de:11000:900120003`	`2022-03-22` > `date` < `2022-03-24`	55ms	~2k
none	none	192s	370m
none	`2022-03-13` >= `date` < `2022-04-08`	34s	~35m
none	`2022-03-22` > `date` < `2022-03-24`	2.4s	~1523k

gtfs-via-postgres provides 2 helper functions dates_filter_min(t_min) & dates_filter_max(t_max) for this. When filtering by t_departure (absolute departure date+time), t_min is the lower t_departure bound, whereas t_max is the upper bound. The VBB example above can be queried like this:

SELECT *
FROM arrivals_departures
-- filter by absolute departure date+time
WHERE t_departure >= '2022-03-23T12:30+01' AND t_departure <= '2022-03-23T12:35+01'
-- allow "cutoffs" by filtering by date
AND "date" >= dates_filter_min('2022-03-23T12:30+01') -- evaluates to 2023-03-22
AND "date" <= dates_filter_max('2022-03-23T12:35+01') -- evaluates to 2023-03-23

Performance

With all use cases I could think of, gtfs-via-postgres is reasonably fast. If there's a particular kind of query that you think should be faster, please open an Issue!

The following benchmarks were run with the 2022-07-01 VBB GTFS dataset (41k stops, 6m stop_times, 207m arrivals/departures) using gtfs-via-postgres@4.7.4 and PostgreSQL 14.7 on an M2 laptop running macOS 12.6.8; All measurements are in milliseconds.

query	avg	min	p25	p50	p75	p95	p99	max	iterations
SELECT * FROM stops ORDER BY ST_Distance(stop_loc::geometry, ST_SetSRID(ST_MakePoint(9.7, 50.547), 4326)) ASC LIMIT 100	15	14.982	15	15	15	15	15	15.488	100
SELECT * FROM arrivals_departures WHERE route_short_name = 'S1' AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= dates_filter_min('2022-08-09T07:10+02') AND date <= dates_filter_max('2022-08-09T07:30+02')	61	60.901	61	61	61	61	62	61.778	100
SELECT * FROM arrivals_departures WHERE station_id = 'de:11000:900100001' -- S+U Friedrichstr. (Berlin) AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= dates_filter_min('2022-08-09T07:10+02') AND date <= dates_filter_max('2022-08-09T07:30+02')	33	33.129	33	33	33	33	33	33.342	40
SELECT * FROM arrivals_departures WHERE station_id = 'de:11000:900100001' -- S+U Friedrichstr. (Berlin) AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= dates_filter_min('2022-08-09T07:10+02') AND date <= dates_filter_max('2022-08-09T07:30+02') AND stop_sequence = 0	5	4.548	5	5	5	5	5	4.598	50
SELECT * FROM arrivals_departures WHERE stop_id = 'de:11000:900100001::4' -- S+U Friedrichstr. (Berlin) AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= dates_filter_min('2022-08-09T07:10+02') AND date <= dates_filter_max('2022-08-09T07:30+02')	8	8.038	8	8	8	8	8	8.164	100
SELECT * FROM arrivals_departures WHERE trip_id = '168977951' AND date > '2022-08-08' AND date <= '2022-08-09'	2	1.878	2	2	2	2	2	1.911	100
SELECT count(*) FROM arrivals_departures WHERE stop_id = 'de:11000:900100001::4' -- S+U Friedrichstr. (Berlin)	58	57.485	58	58	58	58	58	57.789	100
SELECT count(*) FROM arrivals_departures WHERE stop_id = 'definitely-non-existent'	2	1.832	2	2	2	2	2	1.876	100
SELECT * FROM arrivals_departures WHERE t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= dates_filter_min('2022-08-09T07:10+02'::timestamp with time zone) AND date <= dates_filter_max('2022-08-09T07:30+02'::timestamp with time zone)	6310	6238.819	6241	6262	6311	6503	6560	6573.768	10
SELECT * FROM arrivals_departures WHERE t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= '2022-08-08' AND date <= '2022-08-09'	4931	4914.388	4925	4928	4937	4946	4948	4948.689	10
SELECT * FROM connections WHERE route_short_name = 'S1' AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= dates_filter_min('2022-08-09T07:10+02') AND date <= dates_filter_max('2022-08-09T07:30+02')	164	163.018	163	164	164	164	165	166.568	100
SELECT * FROM connections WHERE from_station_id = 'de:11000:900100001' -- S+U Friedrichstr. (Berlin) AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= dates_filter_min('2022-08-09T07:10+02') AND date <= dates_filter_max('2022-08-09T07:30+02')	59	58.137	58	58	59	60	61	61.461	40
SELECT * FROM connections WHERE from_station_id = 'de:11000:900100001' -- S+U Friedrichstr. (Berlin) AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= dates_filter_min('2022-08-09T07:10+02') AND date <= dates_filter_max('2022-08-09T07:30+02') AND from_stop_sequence = 0	7	7.439	7	7	7	7	7	7.49	50
SELECT * FROM connections WHERE from_stop_id = 'de:11000:900100001::4' -- S+U Friedrichstr. (Berlin) AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= dates_filter_min('2022-08-09T07:10+02') AND date <= dates_filter_max('2022-08-09T07:30+02')	15	14.529	15	15	15	15	15	14.698	100
SELECT * FROM connections WHERE trip_id = '168977951' AND date > '2022-08-08' AND date <= '2022-08-09'	3	2.86	3	3	3	3	3	2.931	100
SELECT count(*) FROM connections WHERE from_stop_id = 'de:11000:900100001::4' -- S+U Friedrichstr. (Berlin)	73	72.687	73	73	73	73	73	73.35	100
SELECT count(*) FROM connections WHERE from_stop_id = 'definitely-non-existent'	3	3.428	3	3	3	3	4	3.525	100
SELECT * FROM connections WHERE t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= dates_filter_min('2022-08-09T07:10+02'::timestamp with time zone) AND date <= dates_filter_max('2022-08-09T07:30+02'::timestamp with time zone) ORDER BY t_departure LIMIT 100	13127	13056.841	13086	13125	13170	13194	13199	13200.027	7
SELECT * FROM connections WHERE t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02' AND date >= '2022-08-08' AND date <= '2022-08-09' ORDER BY t_departure LIMIT 100	6417	6237.932	6346	6394	6512	6562	6570	6571.455	7
SELECT * FROM stats_by_route_date WHERE route_id = '17452_900' -- M4 AND date >= '2022-08-08' AND date <= '2022-08-14' AND is_effective = true	2862	2853.972	2860	2863	2863	2867	2867	2866.798	10

Related Projects

There are some projects that are very similar to gtfs-via-postgres:

Node-GTFS

Node-GTFS (gtfs npm package) is widely used. It covers three use cases: importing GTFS into an SQLite DB, exporting GTFS/GeoJSON from it, and generating HTML or charts for humans.

I don't use it though because

it doesn't handle GTFS Time values correctly (1/2, checked on 2022-03-01)
it doesn't always work in a streaming/iterative way (1/2, checked on 2022-03-01)
sometimes does synchronous fs calls (1/2, checked on 2022-03-01)

gtfs-sequelize

gtfs-sequelize uses sequelize.js to import a GTFS feed and query the DB.

I don't use it because

it doesn't handle GTFS Time values correctly (1/2, cheked on 2022-03-01)
it doesn't provide much tooling for analyzing all arrivals/departures (checked on 2022-03-01)
some of its operations are quite slow, because they fetch related records of a record via JS instead of using JOINs

gtfs-sql-importer

There are several forks of the original outdated project; fitnr's fork seems to be the most recent one.

The project has a slightly different goal than gtfs-via-postgres: While gtfs-sql-importer is designed to import multiple versions of a GTFS dataset in an idempotent fashion, gtfs-via-postgres assumes that one (version of a) GTFS dataset is imported into one DB exactly once.

gtfs-via-postgres aims to provide more tools – e.g. the arrivals_departures & connections views – to help with the analysis of a GTFS dataset, whereas gtfs-sql-importer just imports the data.

other related projects

gtfsdb – Python library for converting GTFS files into a relational database.
gtfspy – Public transport network analysis using Python and SQLite.
GTFS Kit – A Python 3.6+ tool kit for analyzing General Transit Feed Specification (GTFS) data.
GtfsToSql – Parses a GTFS feed into an SQL database (Java)
gtfs-to-sqlite – A tool for generating an SQLite database from a GTFS feed. (Java)
gtfs-lib – Java library & CLI for importing GTFS files into a PostgreSQL database.
gtfs-schema – PostgreSQL schemas for GTFS feeds. (plain SQL)
markusvalo/HSLtraffic – Scripts to create a PostgreSQL database for HSL GTFS-data. (plain SQL)

License

This project is dual-licensed: My (@derhuerst) contributions are licensed under the Prosperity Public License, contributions of other people are licensed as Apache 2.0.

This license allows you to use and share this software for noncommercial purposes for free and to try this software for commercial purposes for thirty days.

Personal use for research, experiment, and testing for the benefit of public knowledge, personal study, private entertainment, hobby projects, amateur pursuits, or religious observance, without any anticipated commercial application, doesn’t count as use for a commercial purpose.

Get in touch with me to buy a commercial license or read more about why I sell private licenses for my projects.

Contributing

If you have a question or need support using gtfs-via-postgres, please double-check your code and setup first. If you think you have found a bug or want to propose a feature, use the issues page.

By contributing, you agree to release your modifications under the Apache 2.0 license.

gtfs-via-postgres

gtfs-via-postgres

Installation

Getting Started

translations

Usage

With Docker

Exporting data efficiently

Querying stops by location efficiently

GraphQL support

REST API support

more guides

Correctness vs. Speed regarding GTFS Time Values

Performance

Related Projects

Node-GTFS

gtfs-sequelize

gtfs-sql-importer

other related projects

License

Contributing

Readme

Keywords

Package Sidebar

Install

Repository

Homepage

Weekly Downloads

Version

License

Unpacked Size

Total Files

Last publish

Collaborators

gtfs-via-postgres

gtfs-via-postgres

Installation

Getting Started

translations

Usage

With Docker

Exporting data efficiently

Querying stops by location efficiently

GraphQL support

REST API support

more guides

Correctness vs. Speed regarding GTFS Time Values

Performance

Related Projects

Node-GTFS

gtfs-sequelize

gtfs-sql-importer

other related projects

License

Contributing

Readme

Keywords

Package Sidebar

Install

Repository

Homepage

DownloadsWeekly Downloads

Version

License

Unpacked Size

Total Files

Last publish

Collaborators

Weekly Downloads