A transportation system is not just the sum of its parts but also how and where those parts interact with one another. Measuring the effectiveness of that interaction contributes to everything from understanding the overall system to analyzing key components at the most disaggregate level of the system possible – in this case, the segments of roadway between stops. This presentation will show how analysts at TransLink developed a new foundational dataset by combining three existing datasets: Compass travel, the road network, and our bus location data. This new dataset describes bus and passenger travel at the street-segment level. We will walk the audience through three phases: how the need for the model was identified and what data considerations were important to success; the FME workflow process and some important lessons learned, and how the end results are being used to visualize and analyze our system in all-new ways. The emphasis will be on the FME process where data was pulled from multiple sources/types, assets/services were snapped and aligned, and the spatial relationship between assets and services were analyzed to produce the final output deliverables. Sections and Segment relationships are maintained, and attributes are easily aligned with other datasets such as route patterns, speed limit, vehicle type, frequency, on-time performance, etc.

1. Measuring Bus & Bus-Passenger
Travel by Road Segment

2. FME
User
Conference
20
22
Senior GIS Administrator, Customer Insights
GIS Specialist, App Developer, Data Manager, Transportation
Planner, Interface Graphic Designer,
Data Accessibility Champion and Innovator
Lead Planner, Transportation Analytics
Innovator, Modeller, Compass Tap Data Translator
(transit journeys), Fare Policy Analytics, Self-Serve
Dashboard and App Developer, Transportation Planner
Graeme Brown
Susanne Bell

3. 20
22
FME
User
Conference
THE WHERE
MATTERS!
The big picture is multiple where’s
The answers lie in their relationships

4. 20
22
FME
User
Conference
When you optimize what you
know about the physical
environment of the transit
system, you get
high-performance spatial
analytics.

5. 20
22
FME
User
Conference
Agenda
● The Bus Network
why the where matters
● The Physical Environment
how we create the (bus) where
● Spatial Analytics
what the where(s) can tell us

6. 20
22
FME
User
Conference
The Bus Network – Why The Where Matters

7. 20
22
FME
User
Conference
A Bus Network
is an active web of connections and destinations
Interactive System Maps | TransLink

8. 20
22
FME
User
Conference
● Where are passengers coming from?
● Where are passengers going to?
● How many passengers?
● How often do buses traverse the road?
● How many buses traverse the road?
● What vehicle types?
● Where is bus movement impeded?
How Does the Network Flow?

9. 20
22
FME
User
Conference
Insights of Where
• Better origin-destination connection
• Improve bus priority measures
• Evaluate stop spacing
• Measure frequency networks
• Analyze passenger volumes and
comfort levels
• Evaluate road wear and tear
• Better future planning

10. 20
22
FME
User
Conference
The Challenge?
Our bus routes and stops
were not easily linked to road
network data.

11. 20
22
FME
User
Conference
FME – How We Create the Where

12. 20
22
FME
User
Conference
General Workspace
Readers
Bus Routes
and Patterns
Roads (DRA)
From-To Stop
Pairs
Transit Road
Network
Topology Builder
Shortest Path Finder
List Exploder
Remove Duplicates
Writers
Segments
Road stop to stop
(overlapping)
Segment/section
Membership
Sections
Road node to node
(non-overlapping)

13. 20
22
FME
User
Conference
11 Part Process
● Data is read from SQL and SQL_SDE (spatial data) servers
● Results are written to the SQL data warehouse with a geometry attribute

14. 20
22
FME
User
Conference
Transformation Highlight: Snap Stops to Roads

15. 20
22
FME
User
Conference
Transformation Highlight: Stop Pairs to Lines

16. 20
22
FME
User
Conference
Transformation Highlight: Road Alignment
As the crow flies
stop to stop lines
Roads

17. 20
22
FME
User
Conference
The Data – Segments
Route
A
Route
B
Segments
• A stop-to-stop path on the road
network traversed by one or more bus
routes
• Derived from shortest paths on the
road network between From/To stops
• Can overlap with each other
• Unique on FromStop/ToStop/Sheet
Bus
stop

18. 20
22
FME
User
Conference
The Data – Sections
Sections
• A portion of roadway traversed by one or
more segments (~block)
• Sections do not overlap one another
• Derived from the road network dataset, split
at bus stops
• Relatable to GIS road network dataset
(shared ID field)
Section-Segment Membership
• Table describing which sections belong to
which segments (many-to-many
relationship)
Bus
stop

19. 20
22
FME
User
Conference
The Data – Table Samples
Sections
Segments
Section-Segment Membership

20. 20
22
FME
User
Conference
Spatial Analytics – What The Where Can Tell Us

21. 20
22
FME
User
Conference
Example Applications – Trip Origin
Passenger volumes for highlighted corridors, by journey origin
municipality (AM Peak):
Burrard St,
Southbound
4th
Ave,
Westbound
41st
Ave,
Westbound
49th
Ave,
Westbound
Willingdon Ave,
Southbound
Broadway,
Westbound
Data are for one week in September

22. 20
22
FME
User
Conference
Example Applications – Passenger Volumes
Segment level
corridor volumes
(beta, volumes not
verified).
Bus routes only.

23. 20
22
FME
User
Conference
Example Applications – Route Frequency
Segment level stop to stop frequency (September 2019)

24. 20
22
FME
User
Conference
Example Applications – Delay
Section Route Delays (September 2021) - Submitted by Allen Shi, Data Scientist, Bus Priority Programs

25. 20
22
FME
User
Conference

26. 20
22
FME
User
Conference
Establishing spatial relationships in
datasets creates a powerful
integration framework that enables
us to measure, understand and
visualize everything from the
smallest detail to the whole picture.
Where matters.

27. Thank You!
Susanne.bell@translink.ca