digital mobility patterns: data as the new oil? · 2015. 1. 6. · wouter haerick...
TRANSCRIPT
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Digital mobility patterns:
data as the new oil?
Wouter Haerick, [email protected]
FUTURE INTERNET TECHNOLOGIES
FOR A SMART SOCIETY
AGENDA
Mobility Of The Future
The FUTURE of Connected MobilityUpcoming technologies
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FUTURE MOBILITY?
AIR QUALITY
SLEEP QUALITY
INFRASTRUCTURE – USED CAPACITY
LOW EMISSIONS
LOW NOISE
LOW MOBILITY COST
TODAY
STATIC MAPS
STATIC CALCULATIONS
LOW-DENSITY MEASUREMENTS
EXPENSIVE 1-OFF
MEASUREMENT CAMPAIGNS
LIMITED INSIGHTS
REALTIME, DYNAMIC MAPS
REALTIME CALCULATIONS + PREDICT
HIGH-DENSITY MEASUREMENTS
IS THIS MORE LOW-COST????
VISUALIZE INSIGHT (TIME, PLACE, WHAT-IF)
FUTURE MOBILITY?
LOW COST MOBILITY INSIGHTS
LOW COST SENSORS
LOW COST INSTALLATION
LOW COST COMMUNICATION
HIGH FIDELITY
COST/SENSOR
SENSORS / KM2
€ / MB W / day
Minimal number of sensors Select locations wisely
Maximize useful information Create spill-over knowledge
DATA-DRIVEN
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Taking the pulse of the city
CHEAP SOUND SENSORS TO TAKE THE PULSE OF THE CITY
Monitor park quality
Monitor infrastructure use
Sleep disturbance
Underline cultural and touristic value
Monitor event noise
Safety monitoring
Infrastructure monitoring
Sound Sleep disturbance
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Sound Traffic estimation
Using sound to estimate traffic parameters
Highway Traffic
Parameters
Estimate
Emissions
WHAT DO WE NEED?
air pollution
estimator
indicators for
air pollution
Engine noise indicator
Vehicle speed indicator
topography,
buildings,
weather, …
dis
pers
ion
conte
xt
Gaussian additive model
Background concentration
treated separately
train
ing
trip exposureconcentration
map
SENSOR
SOURCE RECOGNITION CONTEXT DATA
“UPGRADE” MODELS
EXPENSIVE SENSORVALUABLE
INSIGHTS
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Sound black carbon
Cross validation
Bangalore, Indiapopulation: ~10 million
traffic horn obligatory
scooter is popular
Gent, Belgiumpopulation: ~300 000
thousands of bicycles Cross-Validation
Machine Learning
GHENT
BANGALORE
CAN TRAFFIC DATA HELP TO PREDICT UNEXPECTED NON-RECURRENT
CHANGES IN POWER CONSUMPTION
WRONG POWER FORECAST INBALANCES / BLACKOUTS
HIGH ECONOMIC COSTS
FORECASTS
Traffic Data Power Forecast
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Traffic Power Demand
- Power consumption (kW) in a 15min
interval.
- 2011: ~200 houses in region of Flanders
- Aggregated traffic jam length (km) for the main
highways in the region of Flanders.
- Data length: 15 minutes data for 2011
Cumulative traffic jam length in
kilometers over 260 weekdays Load in kW per 15min interval over 260
weekdays
Same time
Same frequency
(e.g. 15 min data)
Same location
LINEAR PROJECT
Traffic Data Power Forecast
;
),()(),(()),(),,(cov()(,
J
ttJtLEttJtLtcorr
L
JL
JL
LJ
| ρ | ≤ 0.1 represents no correlation.
0.1 < | ρ | ≤ 0.3 represents small correlation.
0.3 < | ρ | ≤ 0.6 represents medium correlation.
0.6 < | ρ | ≤ 1.0 represents strong correlation
Pearson’s correlation coefficient:
variabl
edefinition Value/unit
NNumber of samples per
day
All days: 96 (one every
15min)
Afternoon: 37 (from
12:00 to 21:00)
t Sample# 12:00 < t <21:00
D Total number of daysTotal:365 days
Weekdays:260
d Day# 1 ≤ d ≤260
Δt
Shift in traffic data for
accounting for delayed
dependencies
0< Δt ≤8 (2hours)
δtTime in advance
targeted for prediction15min≤δt≤2hours
L(t,d)Power Consumption –
Load profilesunit: kW
J(t,d)Traffic Jam Length
profilesunit: meter
T(t,d) Temperature profiles unit: degree Celsius
R(t,d) Precipitation profiles unit: centimeter
Traffic Data Power Forecast
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The correlation coefficient between traffic and load reaches
its maximum in rainy days with high traffic.
BEST RESULTS FOR
RAINY DAYS
A LOT OF TRAFFIC
PEAK HOURS
Traffic Data Power Forecast
The correlation coefficient between traffic and load reaches
its maximum in rainy days with high traffic.
BEST RESULTS FOR
RAINY DAYS
A LOT OF TRAFFIC
PEAK HOURS
RELATIVE ERROR IMPROVES WITH 25%
Traffic Data Power Forecast
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Correlation between temperature and load for days with different traffic
and precipitation conditions.
FOR
HIGH TRAFFIC RAINY DAYS
TEMP WEAK PARAMETER
TO PREDICT LOAD
Traffic Data Power Forecast
The FUTURE of Connected Mobility
Freigth Mobility
Mobility Infrastructure
Familiy Transport
Personal Transport
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Tomorrow’s connected mobility will/should deliver …
* Driverless vehicles
* Vehicle-less mobility Minimal space to park, maximize flow, portable
* Maximized security
* Multi-modal routes No waiting times, minimal load/unloading times
* Infrastructure-less traffic Minimal cost of infrastructure (virtual)
Accurate localization (V2V/V2I communication)
* No energy consumption
Collision-avoidance?
Minimal energy consumption
* Autonomous Logistics Autonomous load unit “conversion”
The FUTURE of Connected Mobility
WRAP UP
REALTIME MOBILITY DATA
…. PROVES TO BE VALUABLE FOR OTHER SECTORS
…. PROVES TO BE VALUABLE TO STEER MOBILITY POLICIES
… PROVES TO BE VALUABLE FOR MANY PARAMETERS OF
“QUALITY OF LIFE” OF CITIZENS
BE SMART WITH SENSORS
…. USE CHEAP SENSORS FOR “EXPENSIVE” DATA
…. DEPLOY AS LITTLE AS POSSIBLE BUT ENOUGH TO GET ACCURACY
…. DEPLOY IN CARS, BICYCLES, SMART PHONES, ETC.
…. BUILD SMART MODELS / DATA PROCESSORS TO SELL DATA
TO OTHER MARKETS
AND PREPARE FOR TOMORROW’S MOBILITY SOLUTIONS
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WRAP UP
CONTACT INFORMATION:
Wouter Haerick
INTERNET TECHNOLOGIES
Wireless connectivity
Internet-of-Things
Networked Intelligence
Connected Mobility