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