2014 Future Cities Conference / Tânia Calçada & Daniel Moura "UrbanSense Platform"
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Transcript of 2014 Future Cities Conference / Tânia Calçada & Daniel Moura "UrbanSense Platform"
UrbanSense PlatformTânia Calçada, Daniel Moura
UrbanSense Goals
• Impact for the city
• Identify critical urban areas
• Detect events in real time and automatically
• Evaluate the impact of urban intervention actions
Understand and get aware of environmental and behaviour phenomena
• Research platform
• Open data
• Wireless networks testbed
• Data analysis and pattern recognition
• Urban planning
Characteristics
• Hundreds of units
• 400 Mobile units in buses
• Static units located down town
• Units with heterogeneous set of sensors
• 550 environmental sensors
• 60 Video cameras
• 500 GPS, accelerometers, and On Board Devices
Sensor network – static and mobile
• Wireless communications
• Real-time transmission
• Opportunistic communications for delay tolerant data
• Local processing capacity
• Count people and vehicles locally
• Adaptive sampling rate
Sensors
Meteorological
• TemperatureRelative Humidity
• 75 sensors (mobile and fixed)
• Pluviometer, Wind VaneAnemometer
• 10 sensors (fixed)
• Luminosity• 75 sensors (mobile and fixed)
• Solar Radiation• 10 sensors (fixed)
UrbanSense includes 600 sensor units. Hererogeneous sets of sensors.
Air Pollution
• VOC• 50 sensors (mobile and fixed)
• Azote Dioxide• 75 sensors (mobile and fixed)
• Ozone (O3)• 75 sensors (mobile and fixed)
• Particles PM10• 50 sensors (mobile and fixed)
• Carbon Dioxide• 50 sensors (mobile and fixed)
• Carbon Monoxide• 50 sensors (mobile and fixed)
Noise
• Stand alone• High precision
• 1 sensor (fixed)
• Embebed• 25 sensors (fixed)
Mobility• GPS and Accelerometer
• 500 sensors (mobile)
• OBD – On Board Device
Video• Cameras
• 60 sensors (mobile and fixed)
Sensors Location
Mobile units at buses roof
Static units at Porto downtown
Perceiving people and vehicles anonymously
Local Processing =
Anonymity &
Light Communication
• No video streaming• No video storage• Images described by statistics (descriptors)• Low bandwidth requirements
At time 15:018 leaved
10 entered
Contours computed with RPi
Perceiving people and vehicles anonymously
Sensing unit protypeWhat can be done?
• Counting people / vehicles• Classifying vehicles• Detecting patterns (e.g. crowding)
Where?
• Streets• Buildings• Public transports
#vehicles / day
>= 47
40 to 46
34 to 39
27 to 33
21 to 26
14 to 20
8 to 13
1 to 7
Buses as City Scanners
Frequency mapBuses have city-wide coverage
• Scanning the city using sensors• Detecting and predicting traffic jams• Characterizing mobility
(Sample: 108 buses, Wed 27/Nov/2013)
AB
Citizens as City Scanners
Time map
PORTO – Living Lab for Future Cities
www.futurecities.up.pt