As smart data gradually become mainline data for transportation planning, some obvious flaws in infrastructure decision making become apparent when comparing traditional static data and the dynamic nature of human travel. The static survey, a common source of transportation, encouraged to assign a greater portion of longer trips and predicting more road widening and highways. In reality, shorter trips are dominant in cities. Shared mobility options could provide options for shorter trips. These short trips should be properly corrected and assign in our infrastructure projections when travel demand modeling is developed. Smart data is paving the way to open the door of a new possibility towards shared multimodal cities.

1. STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL
Application for Smart Data for Multimodal
Transportation Planning Studies
Dewan Karim
Integrated Mobility Specialist,
Dillon Consulting
dkarim@Dillon.ca
@Com_mobility

2. STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 2
Agenda
1. Overall Approach
2. Understanding Smart Data
3. Different Findings Using Smart
Data
4. Towards Shared Multimodal Cities
5. Q&A

3. Why Smart Data Approach was Adopted in
Toronto/Ontario since 2014 and onwards
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 3
Overall Approach
Section I

4. Key Study Locations: Testing Different
Applications of Smart Data
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 4
To r o n t o E x a m p l e s
Area Plans/Gateway Mobility Studies
Q U I C K LY
Available in minutes
O t h e r O n t a r i o E x a m p l e s
Master Plan, Transit, Bridges
Keele Finch
Plus
Don Mills Crossing
Rouge National
Park
Transit Master Plan
Fenelon Falls Crossing Study
Guelph Transportation Master Plan

5. Multimodal City: True Nature of Human Mobility
Graphics @Copyright to Dewan Karim, Prohibited to Use Replicate or Redistribute without Permission

6. Why We Used Big Data and Processing Software from
StreetLight For These Studies
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 6
New Data
Approach
1
Local
Constraints
2
Moving
Static to
Dynamic
3
Lack of Areawide Origin-
Destination, Lack of true
human mobility pattern,
Construction Disruption
1st Time – Our Trial
Run to Test the Data
Source
Potential to Understand
“Hard-to-Study” Dynamic
Travel Patterns (i.e.:
multimodal nature, trip
origins, passby trip etc.)

7. Understanding Vehicle Behavior is Key for Multimodal
Transportation Planning
Where are the best
opportunities to
convert vehicle trips
to other sustainable
modes?
How should shared
mobility modes and
new technologies be
integrated?
What streets/routes
could create safer and
comfortable active
transportation?
?
How might vehicle
travel patterns change
when new options are
available ?

8. StreetLight InSight was Used to Obtain Four Key
Types of Information
Identify sources of trips,
origin-destination, actual
streets/ intersections used in
study area
1 Develop quantitative
multimodal travel demand
forecasting
2
Candidate trips for transit
route planning, shared
mobility for 24 hr-seasonal
variations
4
Measure travel times, trip
length, pattern to develop
multimodal mode share
3

9. Study Area, Trip Attributes, Calibration and Verification
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 9
Understanding Smart
Data
Section II

10. Selection of Study
Zones
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 10
General selection process:
Selection of Area and Zones:
• Characteristics and proximity
• Nature of land use
• Matching local traffic zone boundary
Streetlight Platform
• Built-in dashboard
• GIS format

11. Selection of Data
Configuration
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 11
Streetlight Platform:
• Customized dashboard to combination of attributes
Origin
Destination
Pass-by
1. Origin-Destination
2. Trip Attributes
Travel Time,
Length,
Speed
Share of Trip
Source
Trip Distribution
in Network
Binned By:
Trip Type:
• Personal
• Commercial
Day Types:
• Average Day (Mon – Sun)
• Average Weekday (Mon – Fri)
• Average Weekend (Sat –
Sun)
Day Parts:
• All Day (12 am – 12 am)
• Early AM (12 am – 6 am)
• Peak AM ( 6 am – 9 am)
• Mid-Day ( 9 am – 4 pm)
• Peak PM ( 4 pm – 7 pm )
• Late PM ( 7 pm – 12 am)

12. Data Checking &
Calibration
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 12
Calibration/Verification process:
• Use traditional (TMC/AADT/Cordon)
for calibration/verification
• Verify percent of data in SL platform
captured
• Convert into approximate total trips
Streetlight Platform gives
• Raw data trip index
• Two types of expansion facto: Local
and overall
• Built-in AADT Data
Trip Index
Trip Package
Raw
Data
Counter
Points for
Each Street/
Intersections
1. With Count Data
Location-
specific
Expansion
Factor
xxx
xxx
2. With TTS/ Area Survey (Cordon/Screenline) Data
Total
Area
Trips
xxx
xxx
Overall Expansion
Factor

13. Combining Different Data Sources
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 13
General platform facility:
Overlay of census data with the
Streetlight data provides additional
interesting insights
• Trip attributes
• Traveller attributes
• Premium visualization
Streetlight Platform
• Built-in visualization
• Import external data

14. Solving Different Challenge Using Smart Data
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 14
Different Findings
Using Smart Data
Section III

15. Overall Area Travel: Personal vs Goods Movement
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 15
Keele-Finch
Core Area
Total Daily Trips
27,450
91,221
Extended
Study Area 2,765
8,315
26,680
86,060
1,380
4,155
189,740
4.5%
Area Total
Pass-by Trips
62%
38%
Overall Findings – Area Travel Patterns:
– 2/3 of vehicle trips pass through the area without stopping (20-25% higher
than other areas of city).
– Commercial vehicles constitute 4.5% of vehicle trips, and the majority
originated from Vaughan, GTA western region and Etobicoke.
– Commercial O-D patterns are opposite of personal trips.
Findings: Dramatically Different Travel Patterns in Keele-Finch Area

16. STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 16
Multimodal Infrastructure Priority
Findings: Source of Traffic – Difference in Inner and Outer Suburbs
Transit and Active Transportation produce maximum benefits to local community
<20%
External
Trips
>60%
External
Trips
~33%
Internal
Trips
+

17. Internal Trips: Complete Community & Mixed-Use Policies
Findings: Big Data and City Policies
60%
Internal
Vehicle
Trips
10% 5%
~75%
Total
Internal
Trips
10%
5%
9%
3%
3%
1%6%
Using Big Data
+
Using TTS Data
=
Higher Internal Trips
are associated with:
• Higher population
• Higher income
Source: What’s Driving Your County’s Vehicles Miles Traveled? U.S. Streetlight Report, 2016. https://www.streetlightdata.com/whats-driving-your-countys-vehicles-miles-traveled
Short and Shared
Mobility Trip Candidates:
• Connectivity Index
• Walkshed
• AT
• Shared Mobility
Modeling

18. Paradigm Change in Travel Demand Forecasting
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 18
Clash of Titans - Static vs. Smart Data:
One city, two different travel pattern:
• Short trips are unreported in TTS
• Long-trips are overreported in TTS
• Streetlight data shows real world
travel pattern if calibrated properly
Seismic Shift in Travel Demand
Forecasting
• Less future trips to Highway/Long-haul
transit
• Proper allocation of trips to short
distance trips including shared mobility

19. Origin-Destination of Trips: Travel Pattern and Changes
Findings: Drastically Different Travel Behaviour
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 19
Smart Data helps to:
Big Data helps to:
1. Avoid random assumption of
distribution of trips
2. Accurately project the trips to be
converted to transit, specially after
opening of Spadina subway
3. Enable quantitative analysis of short
trip and associated infrastructures

20. Goods Movements: Travel Pattern and Demand Management
Findings: Commercial Truck Behaviour
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 20
Smart Data helps to:
1. Temporal distribution
2. Peak travel pattern
3. Managing demand
(vs. expanding infrastructure)
Vaughan
+North of Hwy 401
50%
GTA West
+Etobicoke
37%

21. Transit Master Plan
Findings: High Demand Routes – Windsor
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 21
Smart Data helps to:
1. Highest demand origin-destination
2. Which route are used for real world
travel by vehicle i.e. replicate same
route for transit
3. Scale of demand to increase transit
service frequency
4. Analysis time and budget was
significantly lower than traditional
transit master plan ChryslerCanada
CityCentre
Devonshire
DevonshireMall
EastRiverside
EastWindsor
Fontainbleu
ForestGlade_Ind
ForestGlade_Res
Malden
Ojibway
RemingtonPark
Riverside
Roseland
Sandwich
SandwichSouth
SouthCameron
SouthCentral
SouthWalkerville
SouthWindsor
StClairCollege
TecumsehMall
University
UniversityofWindsor
WalkerFarm
Walkerville
Lasalle
TecumsehInd
TecumsehRes
GrandTotal
Chrysler Canada 0.0% 0.0% 0.2% 0.0% 0.1% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 0.1% 1.6%
City Centre 0.0% 0.0% 0.1% 0.0% 0.0% 0.2% 0.1% 0.1% 0.1% 0.0% 0.0% 0.1% 0.1% 0.0% 0.2% 0.0% 0.1% 0.4% 0.1% 0.1% 0.0% 0.1% 0.2% 0.2% 0.1% 0.4% 0.1% 0.1% 0.1% 3.0%
Devonshire 0.2% 0.1% 0.0% 0.1% 0.0% 0.1% 0.2% 0.2% 0.1% 0.0% 0.1% 0.4% 0.1% 0.2% 0.1% 0.1% 0.1% 0.2% 0.2% 0.3% 0.1% 0.0% 0.1% 0.0% 0.3% 0.1% 0.2% 0.3% 0.1% 4.0%
Devonshire Mall 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.4%
East Riverside 0.1% 0.4% 0.1% 0.0% 0.0% 0.1% 0.1% 0.2% 0.7% 0.0% 0.0% 0.2% 0.4% 0.1% 0.0% 0.1% 0.0% 0.1% 0.0% 0.1% 0.0% 0.1% 0.0% 0.1% 0.2% 0.1% 0.0% 0.1% 0.6% 4.0%
East Windsor 0.3% 0.4% 0.3% 0.1% 0.0% 0.0% 0.8% 0.4% 0.4% 0.0% 0.0% 0.3% 0.4% 0.1% 0.3% 0.1% 0.1% 0.2% 0.3% 0.1% 0.1% 0.1% 0.1% 0.1% 0.4% 0.5% 0.2% 0.5% 0.2% 7.0%
Fontainbleu 0.2% 0.2% 0.3% 0.1% 0.0% 0.5% 0.0% 0.5% 0.2% 0.0% 0.0% 0.2% 0.3% 0.1% 0.1% 0.0% 0.0% 0.2% 0.1% 0.2% 0.0% 0.1% 0.0% 0.1% 0.4% 0.2% 0.1% 0.2% 0.2% 4.7%
Forest Glade_Ind 0.0% 0.0% 0.1% 0.0% 0.0% 0.2% 0.2% 0.0% 0.2% 0.0% 0.0% 0.0% 0.2% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 0.0% 0.1% 1.7%
Forest Glade_Res 0.2% 0.3% 0.2% 0.1% 0.1% 0.2% 0.3% 0.6% 0.0% 0.0% 0.1% 0.2% 0.5% 0.0% 0.1% 0.1% 0.0% 0.2% 0.3% 0.1% 0.1% 0.3% 0.0% 0.0% 0.4% 0.2% 0.1% 0.2% 0.5% 5.3%
Malden 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.5%
Ojibway 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.5%
Remington Park 0.2% 0.2% 0.2% 0.1% 0.0% 0.2% 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 0.1% 0.3% 0.2% 0.2% 0.1% 0.0% 0.1% 0.1% 0.1% 0.2% 0.1% 0.1% 0.1% 3.2%
Riverside 0.3% 0.5% 0.2% 0.1% 0.2% 0.7% 0.5% 0.8% 0.8% 0.0% 0.1% 0.3% 0.0% 0.1% 0.2% 0.1% 0.1% 0.2% 0.2% 0.1% 0.1% 0.2% 0.0% 0.1% 0.5% 0.6% 0.2% 0.2% 0.3% 7.7%
Roseland 0.2% 0.3% 0.8% 0.1% 0.0% 0.2% 0.2% 0.1% 0.1% 0.0% 0.1% 0.3% 0.0% 0.0% 0.1% 0.0% 0.2% 0.2% 0.1% 1.0% 0.3% 0.0% 0.2% 0.2% 0.5% 0.2% 0.5% 0.7% 0.2% 6.9%
Sandwich 0.0% 0.3% 0.1% 0.1% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.2% 0.1% 0.0% 0.0% 0.0% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.4% 0.3% 0.2% 0.1% 0.2% 0.2% 0.1% 3.5%
Sandwich South 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 0.6%
South Cameron 0.0% 0.4% 0.1% 0.1% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.2% 0.1% 0.1% 0.3% 0.0% 0.0% 0.2% 0.1% 0.5% 0.1% 0.0% 0.3% 0.3% 0.1% 0.1% 0.1% 0.2% 0.0% 3.5%
South Central 0.1% 0.7% 0.1% 0.0% 0.0% 0.2% 0.1% 0.0% 0.2% 0.0% 0.0% 0.4% 0.1% 0.1% 0.1% 0.0% 0.1% 0.0% 0.3% 0.1% 0.1% 0.0% 0.2% 0.1% 0.1% 0.4% 0.2% 0.2% 0.0% 3.8%
South Walkerville 0.2% 0.2% 0.3% 0.0% 0.0% 0.2% 0.3% 0.1% 0.1% 0.0% 0.0% 0.3% 0.1% 0.0% 0.1% 0.0% 0.0% 0.4% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.1% 0.2% 0.1% 0.1% 0.1% 3.1%
South Windsor 0.2% 0.5% 0.5% 0.1% 0.0% 0.1% 0.3% 0.1% 0.1% 0.0% 0.1% 0.5% 0.1% 0.3% 0.4% 0.1% 0.3% 0.3% 0.2% 0.0% 0.2% 0.0% 0.3% 0.1% 0.3% 0.2% 0.2% 0.4% 0.1% 6.1%
St Clair College 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.3%
Tecumseh Mall 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5%
University 0.0% 0.5% 0.1% 0.0% 0.0% 0.1% 0.1% 0.1% 0.1% 0.0% 0.0% 0.2% 0.1% 0.1% 0.4% 0.0% 0.2% 0.3% 0.1% 0.1% 0.1% 0.0% 0.0% 0.3% 0.1% 0.2% 0.1% 0.1% 0.0% 3.4%
University of Windsor 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5%
Walker Farm 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 1.1%
Walkerville 0.1% 1.1% 0.3% 0.1% 0.0% 0.5% 0.3% 0.3% 0.2% 0.0% 0.1% 0.4% 0.2% 0.1% 0.2% 0.1% 0.1% 0.8% 0.5% 0.2% 0.1% 0.1% 0.2% 0.1% 0.2% 0.0% 0.2% 0.2% 0.1% 6.7%
Lasalle 0.3% 0.6% 0.4% 0.1% 0.0% 0.3% 0.2% 0.2% 0.3% 0.4% 0.4% 0.5% 0.1% 0.4% 0.7% 0.1% 0.2% 0.3% 0.3% 0.4% 0.3% 0.1% 0.1% 0.3% 0.4% 0.2% 0.0% 0.6% 0.1% 8.4%
Tecumseh Ind 0.0% 0.1% 0.2% 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 0.1% 1.5%
Tecumseh Res 0.3% 0.6% 0.3% 0.1% 0.5% 0.3% 0.2% 0.5% 0.8% 0.0% 0.1% 0.3% 0.3% 0.0% 0.1% 0.2% 0.2% 0.1% 0.2% 0.1% 0.0% 0.1% 0.0% 0.1% 0.4% 0.2% 0.1% 0.4% 0.0% 6.4%
Grand Total 3.0% 7.6% 5.2% 1.5% 1.4% 4.9% 4.4% 4.6% 4.8% 0.6% 1.5% 5.4% 3.4% 2.0% 4.0% 1.2% 2.3% 4.8% 3.7% 3.9% 1.8% 1.2% 2.7% 2.8% 5.2% 4.6% 3.1% 5.3% 3.2% 100.0%
Weekday AM Peak – OD Matrix

22. Transit Master Plan
Findings: Transit Demand Visualization – Windsor
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 22
StreeLight platform offers:
1. Built-in visualization tools
2. Visualization of O-D Pair
3. Visualization in converted
transit demand trip
intensification

23. Bridge Assessment
Findings: Bridge trip activities – Fenelon Falls
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 23
StreeLight data reveals:
1. Isolate trips that specifically use
the bridge
2. Highlight zones with high
percent of trip Origins and/or
Destinations
3. Review breakdown of trips by
time of day, day of week, and
season

24. Bridge Assessment
Findings: Bridge trip activities – Fenelon Falls
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 24
Streelight data reveals Internal trips within
Kawartha Lakes Region versus external trips:
1. 5% - 10% of trips on the Bridge are
travelling between External Areas
2. 15% - 21% of trips are between
Kawartha Lakes and External Areas
3. 69% - 81% of trips are within the
Kawartha Lakes Region
Average
Summer Day
Average
Summer
Weekday
Average
Summer
Friday
Average
Summer
Friday
PM PEAK
Average
Summer
Weekend Day
Average
Summer
Weekend
Midday PEAK
External to
External
6% 5% 8% 9% 7% 6%
Internal 77% 81% 74% 69% 72% 73%
External to
Internal
8% 7% 11% 14% 9% 10%
Internal to
External
9% 8% 7% 7% 12% 11%

25. Active Transportation by Smart Data
Potential Application: How people travel to Rouge Park
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 25
Streelight data is expected to useful for:
1. What are common routes/entry points
to park
2. True nature of walking and cycling to
park destinations
3. Where and what to improve to reduce
environmentally sensitive lands
4. How to reduce pollution from
transportation modes
5. Making easy access by transit,
walking, cycling and potential new
mobility modes

26. Smart Data insights lead to different planning principles
STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 26
Towards Shared
Multimodal Cities
Section IV

27. >65% Short
Trips in Urban
Areas
Smart
Data
Application of Smart Data: Candidate Trips for Multimodal Mobility
Mobility’s
Missing
Middle
Graphics @Copyright to Dewan Karim, Prohibited to Use Replicate or Redistribute without Permission

28. EcoMobility Hub Elements Context
1.On-Street
2.Bus-stop
3.MobilityPark
6.Private/
Off-street/
Underground5.TransitStation
Application of Smart Data: EcoMobility Hubs
4.Boulevard

29. Innovative Space Design Multimodal Buildings
Application of Smart Data: Redesign and Reinvent Planning Areas

30. Challenges and Future Opportunities for the
City’s Use of Big Data
Opportunities:
• Trip purpose with larger dataset
• Integrated multimodal dataset
• Full application of Big Data for planning
studies
• Integration with operational planning
• Opportunity to use Big Data for predictive
analytics drastically reducing time & resources
• Incorporation of new modes and technologies
• Understanding multimodal & trip-chain issues
Challenges:
• Data processing skill set
• Understanding data contents and calibration
• Integrated multimodal data set
• Demand modeling and database
• Predictive analytics
• Parallel sets of database

31. Question and Answers
info@streetlightdata.com