Find related topics. | Discover entities. | See connections. | Build a topical map.
Building topical map: Starting…
GTFS: History & Applications
GTFS, or the General Transit Feed Specification, is a general transit feed specification which defines a common data format for public transportation schedules and associated geographic information. GTFS contains only static or scheduled information about public transport services, and is sometimes known as GTFS Static or GTFS Schedule to distinguish it…
Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.
More settings
GTFS topic overview
The analysis highlights History and Applications as prominent areas in the source structure around GTFS.
Related topics
32
Source areas
4
Connected nodes
36
Extracted relationships
81
Concept neighborhoods
14
Bridge connections
36
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Applications · 8 topics
History · 8 topics
Overview · 8 topics
Structure · 8 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Developed by
Google
Extended from
CSV
Initial release
27 September 2006 (19 years ago) (2006-09-27)
Open format?
Yes, CC BY 3.0
Standard
De facto standard
Type of format
Transit schedule format
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How GTFS connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around GTFS shows recurring relationship patterns in the source. For example, GTFS → Bibiana, Bibiana McHugh, Chris Harrelson, CSV, Google, Google Maps, Google Transit Feed Specification, Google Transit Trip Planner, Google's, In December, In September, IT, McHugh, Oregon, Portland, Tim, Tim McHugh, Transit Trip Planner, TriMet, TriMet's Another extracted example is GTFS → CSV, Each, European, Following, GTFS Realtime, However, In, It, Preferred, So, The, Together, Transmodel, UTF-8, VDV-45X. Use these groups to spot repeated connection types before inspecting the individual relationships.
GTFS
Top relations
related to history· 22
GTFS → Bibiana, Bibiana McHugh, Chris Harrelson, CSV, Google, Google Maps, Google Transit Feed Specification, Google Transit Trip Planner, Google's, In December, In September, IT, McHugh, Oregon, Portland, Tim, Tim McHugh, Transit Trip Planner, TriMet, TriMet's
related to Structure· 15
GTFS → CSV, Each, European, Following, GTFS Realtime, However, In, It, Preferred, So, The, Together, Transmodel, UTF-8, VDV-45X
related to Feed registries· 12
GTFS → APIs, April, GTFS Data Exchange Archived, GTFS Realtime, Interline Technologies, Mapzen, Mobility Database, Public GTFS, The, TransitFeeds, Transitland, Wayback Machine
related to Journey planning· 8
GTFS → ArcMap Network Analyst, Google Transit, GTFS-Realtime, In, OpenStreetMap, OpenTripPlanner, Other, This
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 81 structured relationships around GTFS. Examples in this analysis include GTFS → Developed by → Google and GTFS → Extended from → CSV. The table shows each extracted connection, where it came from and its confidence.
the ArcMap Network Analyst extension which can incorporate GTFS for transit routing.GTFS was originally designed for use in Google Transit
instance of
Other general purpose applications exist
0.80
text
an online multi-modal journey planning application.Accessibility researchGTFS is often used in research on transit accessibility where it is typically used to estimate travel times by transit from one point to many other points at different times of day
instance of
Other general purpose applications exist
0.80
text
an online multi-modal journey planning application
instance of
Other general purpose applications exist
0.80
text
analysis of service levels
instance of
but is also useful for other applications
0.80
text
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around GTFS bring nearby vocabulary together. In this analysis, examples include Transit, Data and Feed. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
GTFS
Transit
Data
Feed
Public
Applications
Format
Google
Used
Information
Extension
Realtime
Use
gtfs
Transit
Data
Feed
Public
Applications
Format
Google
Used
Information
Extension
Realtime
Use
common data format
Part
Fares
Journey
Transit
Google
Gtfs
Format
Public
Feed
Agency
Specification
Planning
gtfs realtime
Transit
Data
Feed
Website
Public
Applications
Format
Google
Used
Information
Extension
Realtime
data feed
Journey
Transit
Google
Gtfs
Specification
Format
Public
Data
Feed
Applications
General
Agency
applications
General
Planning
Journey
Feed
Transit
Extension
Gtfs
Schedule
Data
Specification
Google
Format
csv file
File
Extension
Id
Schedule
Table
Optional
Tables
Feed
Service
Google
Data
Rules
google
Journey
Transit
Schedule
Csv
Planning
Use
Specification
Gtfs
Optional
Rules
Stops
Routes
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For GTFS, one of the stronger structural bridges in this analysis connects GTFS with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
GTFS
Nodes37
Edges36
Triples81
Avg. degree1.95
Density0.054054
Components1
Source & methodology
TTTA analyzes the structure around GTFS to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History & Applications, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — GTFS · EN edition · Analysis: TopicsToTalkAbout
For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.
