Big data week l'impact du big data sur l'intelligence urbaine ibm research irelande par olivier verscheure

© 2010 IBM Corporation
IBM Research - Ireland
Taking it to the Streets –
Big and Fast Data in the City
Olivier Verscheure, PhD
Senior Manager, Big Data Analytics & Systems
IBM Research, Ireland

China
WatsonAlmaden
Austin
TokyoHaifa
Zurich
India
Dublin
Melbourne
Rio
IBM Research: 4 new labs established since 2010
Kenya

Isolated Research
Joint Projects
Radical
Collaboration
’50s — ’90s ’90s — ’00s
’00s …
IBM Divisions,
Clients, Universities
The World is Now Our Lab
Hardware + Software & Services
+ Smarter Planet
The Eras of IBM Research
First-of-a-Kind
Research Consulting
Intellectual Property
Collaborative grants

What Do You See?

Dublin City Data Hub

Big Data, Bad Data

How can we help cities achieve their aspirations?
1.  Feel the City’s Pulse
- Assimilate sensor data
- Deal with data diversity, accuracy, sparcity, volume
2.  Analyze the City’s Need
- Understand how people use the city infrastructure
- Model and predict demand
3.  Empower the city’s people, businesses,
universities, and leaders
Optimize planning & operations, in the face of
uncertainty
Organize and open data and knowledge

Feel the City’s Pulse
•  FIFA World Cup ‘06, the final… A perspective from Rome
•  Continuous assimilation of real-time traffic data
Analyze the City’s Need
•  Predicting number of available bikes in bike sharing stations
•  Characterizing urban dynamics from digital traces
Empower the City’s People, Businesses
•  Optimizing public transport from cell phone data
Agenda
TransportandMobility,
WaterManagement,
SustainableEnergy

Feel the City’s Pulse
•  FIFA World Cup ‘06, the final… A perspective from Rome
•  Continuous assimilation of real-time traffic data
Analyze the City’s Need
•  Predicting number of available bikes in bike sharing stations
•  Characterizing urban dynamics from digital traces
Empower the City’s People, Businesses
•  Optimizing public transport from cell phone data
Agenda

FIFA World Cup ‘06, the final… A perspective from Rome

Real-time
assimilation,
mediation
(e.g. de-noising),
aggregation
(e.g. key traffic
metrics)
GPS devices
Induction loops
Axle counters
Traffic lights
Parking meters
Cameras
MCS
Weather stations
Microblogs
• Geomatching
• Geotracking
• Traffic metrics
Our Stockholm Experience (2009)

•  Complex system & analytics challenges
•  Data diversity, heterogeneity
•  Data accuracy, sparsity
•  Data volume
Routes & maps
Bus AVL (GPS)
Parking
capacity
Accessibility
SCATS
Induction loop
Timetables
CCT
V
Ca
r
Bik
e
1,000 buses
3,000 GPS / min
200 CCTV cameras
700 intersections
4,000 loop detectors
20,000 tuples / min
The Dublin Bus Project

Quick Demo

Dublin Bike Sharing Scheme
•  Launched in September 2009
•  More than 1 million journeys per year
•  44 bike stations across the city center
•  Prediction of number of available
bikes and waiting times
•  Immediately applicable to larger bike
sharing schemes (e.g., Bicing in
Barcelona: 1450 stations)

Transparent Analytics
•  Covariates:
•  GAM equation:

Analysis
•  Available bikes (5-min data), 44 bike stations,
February 26th - May 31st, 2012.
•  Weather information (temperature, rain).

Results
•  Comparison with Last Value (LV), Historical Mean (HM), Autoregressive
Moving Average (ARMA)
•  Average and Weighted RMSE (weighted by size of bike station)

Pervasive Technologies Datasets as Digital Footprints
Understand how people use the
city's infrastructure
§ Mobility (transportation mode)
§ Consumption (energy, water, waste)
§ Environmental impact (noise, pollution)

Goal: Modeling and predicting non-routine additive origin-destination ﬂuxes in the city
Proﬁling an event based on the generated travel demand

F. Calabrese, F. Pereira, G. Di Lorenzo, L. Liu, C. Ratti, The geography of taste: analyzing cell-phone mobility and social events. In International Conference
on Pervasive Computing, 2010.

Modeling Urban Mobility during Special Events

Origins of Attendees

Sport

Cinema
Low High
Attendance probability
Circles are centroids of zipcode areas
Event Types and Attendance Origins


D. Quercia, N. Lathia, F. Calabrese, G. Di Lorenzo, J. Crowcroft, Recommending Social Events from Mobile Phone Location Data, ICDM, 2010.

Improving event planning and management
Predicting the effect of an event on the urban
transportation
Adapting public transit (schedules and routes) to
accommodate additional demand

Location based services
Recommending social events
Cold start problem

Applications

AllAboard – Optimizing public transport using cellphone data
AllAboard assists cities in improving their transport network to
better meet demand and reduce travel and wait times.
AllAboard relies on data from Telecom networks and transport
network to evaluate ridership of roads and transit services under
current and optimised network configurations.
AllAboard Demo - Abidjan , Ivory Coast
Input
Cellphone location data from 500,000 users
Existing transit network:17 express and 67 regular bus routes
Output
Optimization model selected 4 new routes
22 routes had increased ridership
Citywide travel time decreased by 10%

Big data week l'impact du big data sur l'intelligence urbaine ibm research irelande par olivier verscheure

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