belgian ooh survey 2017- ready for the future - fepe · step 5 step 6 travel data 3 steps traffic...
TRANSCRIPT
What does the market need?
• Reliable OOH-survey
• Covers the whole country
• Survey with up to date data
• High granularity
• Compatible with DOOH
• Integration of visibility insights
• Future-proof
A future-proof study
• Mix survey data with big travel data
• Modular approach allows integration new data sources
• Integrates all available visibility insights
• Allows real contact based NEW currency
• Will support new business models
Why Be-Mobile
- Performance of Out-Of-Home advertising dependshighly on quantity & quality of eyeballs
- Classic approach: surveys- Very limited sample in time and population- Complexity of survey
- Collecting more data seems necessary, but- Sensor networks are very expensive- Existing big data offerings (eg. Telco data)
are not sufficiently detailed
- Solution: Be-Mobile Mobility Data
Be-Mobile
mobility
database
Floating car data
• GPS data
• Smartphone data
• Telco data
Road sensors
• Cameras
• Radars
• Loops
• Parking sensors
Crowd sourced
• Drivers
• Social media
• Police
• Emergency vehicles
Other data
• Public transport
• Car & bike sharing
• Parking, fuel
• Toll
3.000.000.000 data
records/day6.000.000 vehicles
monitored
The most versatile mobility data aggregator
Virtual Population (VP)
Database
Survey
Data
Week travel diaries
for VP
Validation with
passive traffic data
Map trips
on road network
Calibration
of travel data
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Travel dataTravel dataTravel dataTravel data
3 steps3 steps3 steps3 steps
Traffic volumesTraffic volumesTraffic volumesTraffic volumes
3 steps3 steps3 steps3 steps
Step 1 : Survey data
• Integration of 3 sources
• CIM OOH study
• BELDAM (Belgian government)
• OVG (Flemish government)
• Missing data estimated
Alignment of socio-demographics
• Output = 45.000 respondents
Step 2 : Virtual Population dbase
• Create dbase 9,6 mio Belgians (12+)
• Basic SD = federal census data 2011
• Estimate missing SD’s
• Using survey data to extrapolate SD-patterns
on clusters of statistical zones of similar composition
Step 3 : Week travel diaries for VP
Create 180 million trips for 9,6M Belgians, for 1 week
Trip choices
Frequency by motive How many trips does an individual make
in a week to his work/school, to…?
Time-of-week + sequence And on which day and day part?
Destination What is the destination of his trip?
Transport mode What is the transport mode for each trip?
All values predicted following a model based on the survey data(using gender, age, occupation, family size, car ownership, pop. density, trip motive / distance / travel time)
Activity based models validated on survey data
Step 3 : Week travel diaries for VP
E.g. School trips per week for all 12-24 year old in survey and in model
Model Survey
Travel dataTravel dataTravel dataTravel data
3 steps3 steps3 steps3 steps
Virtual Population
Database
Survey
Data
Week travel diaries
for Virtual Population
Validation with
passive traffic data
Map trips
on road network
Calibration
of travel data
Traffic volumesTraffic volumesTraffic volumesTraffic volumes
3 steps3 steps3 steps3 steps
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step 4 : Map trips on roads
All 180 million trips get the most probable route at a specific moment
taking into account duration and distance
Route at 08:00 Route at 17:00
Step 5 : Calibration / Validation with passive travel data
• CAR volumes (total) => FOD Mobility & DIV
• CAR volumes (per segment) => 700 Loop detectors & 3% GPS
• Total pedestrian, bicycle, PT => Surveys
• Public transport by station => Traffic counts stations
Travels
from diaries
Estimated
Traffic Volumes
Trip mapping
Validation
Solution
Yes
No
Ca
libra
tion
• Increase amount of short trips
• Increase/reduce mean trip lengths
• Shift between time-periods
• Shift between modes
Step 6 : Calibration of travel data
• Up to date traffic data based on measured big data
• Real time speed
• Audience measurement will deliver even more granularity in the future with audience measurement for each hour of the day
• Breakthrough for DOOH
• Projected audience measurement by hour• Different VAI for each hour every day
Advantages
• New commercial model for DOOH
• Dynamic but transparent pricing system for DOOH
• Data needed for automated planning/buying
Opportunities
• Hybrid future proof modular survey
• Audience measurement based on real accountable data by independent third parties
• Fundamentals for a new commercial model in DOOH
Conclusion