Download - Big data and public transport
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ISO Big Data in TransportPotsdam Workshop
Neil Frost and Warwick Frost
May 2016
What is Big Data
Big Data conceptualizes how we capture and process
very large complex sets of data.
Big Data has its roots in time series and predictive
analytics.
Traditional data warehousing techniques are no longer
adequate.
Where Big Data differs is in the sophistication of
analytics.
The big difference is that correlations and patterns can
be derived from information which was previously
considered unconnected. The result is a far greater
level of precision in terms of predictive capability.
Reference: PTV Group; White paper; NEW DATA SOURCES FOR TRANSPORT MODELLING, DECEMBER 2014; pg.04
What is Big Data
Big data is an evolving term that describes any voluminous amount of structured,
semi-structured and unstructured data that has the potential to be mined for
information.
Processing Methodology
Data Sources
Web & Social Media
Machine generated
Humangenerated
Internal Data Sources
Transaction Data
Via Data Providers
Via Data Originator
Data Consumers
Human
Business Process
Other Enterprise
Applications
Other Data Repositories
Text
Videos
Documents
Audio
Images
Structured
ContentFormat
Unstructured
Semi-structured
All formats can be
type structured,
unstructured or semi-
structured
Data Type
Meta Data
Master Data
Historical
Transactional
Continuous feeds
Real time feeds
Time series
Data Frequency
On demand feeds
Predictive Analysis
Analytical
Query & Reporting
Miscellaneous
Social Network Analysis
Location base
Analysis
Features recognition
Text Analytics
Statistical Algorithms
TranscriptionSpeech
Analytics
Translation3D
Reconstruction
Real Time
Near Real Time
AnalysisType
The feeds may be available on monthly,
weekly, daily, hourly, per minute or per
second basis
Periodic
Batch
Reference: IBM, Big data classification, http://www.ibm.com/developerworks/library/bd-archpatterns1/
Internet of Transport
The IoTransport can assist in integration of communications,
control, and information processing across various transportation
systems and modes.
Application of the IoTransport extends to all aspects of
transportation systems, i.e. the vehicle, the infrastructure,
and the driver or user.
Dynamic interaction between these components of a transport
system enables inter and intra vehicular communication, smart
traffic control, smart parking, electronic toll collection
systems, logistic and fleet management, vehicle control, and
safety and road assistance.
Reference: Wikipedia
How does this relate to ITS
© iSAHA
Storm water
Extent of Data requirements
© iSAHA
Real Time Traffic Flow
ETA Predictions
Reference: Google Maps; https://maps.google.com/
INRIX
Traffic Information
Reference: INRIX; http://www.inrix.com/products/
INRIX
Connected Driver
Reference: INRIX; http://www.inrix.com/products/
NAVIGATION SERVICE BUS OPERATOR CITY POLICE STATION RAILWAY OPERATOR
ID MANAGEMENT SYSTEM
TOUCH SMARTCARD WHEN BOARDING BUS
CURRENT BUS LOCATION
EV BUS STATE OF CHARGE
BUS OPERATION MANAGEMENT SYSTEM
EV BUS POWER MANAGEMENT SYSTEMINTEGRATED GUIDANCE ON BEST ROUTE
Station
12
The roads will likely be crowded today, so
he decides to take a bus instead
Arrives at station more quickly than if
driven by car
The train comes just as he arrives at the
station
The integrated fare system means
changing from bus to train is economical
Transportation user experience layer
Transportation services layer
NAVIGATION SERVICE BUS OPERATOR CITY POLICE STATION RAILWAY OPERATOR
MULTI-MODAL NAVIGATIONAdvise on best transportation
company route based on today’s forecast
BUS PRIORITY SIGNAL SYSTEMPrioritize green lights for bus to ensure it
arrives at the station on time
INTEGRATED TRANSFER BETWEEN BUS & TRAINBus operation management ensures bus arrives on
time to catch desired train
INTEGRATED FARE COLLECTION SERVICEAs a single fare gets him all the way to his
destination, transfers between transportation companies are economical
Information collection layer
ITS MANAGEMENT SYSTEM
ITS MANAGEMENT
SYSTEM
RAILWAY OPERATION
MANAGEMENT SYSTEM
TRAIN ARRIVAL TIME TABLE
URBAN MANAGEMENT INFRASTRUCTURE
Information management and control layer
Transportation company coordination layer
Integrated analysis and simulation flow of people
Smartcard integrated management
Integrated analysis and simulations of electric power usage
PERSONAL DETAILSDESTINATION
CURRENT LOCATION
Title
Typical Enterprise Architecture
Enterprise Discretionary &
Non-Discretionary Standards/
Requirements
Feedback
Business Architecture
InformationArchitecture
InformationSystems Architecture
Data Architecture
Delivery Systems ArchitectureHardware, Software, Communications
Drives
Prescribes
Indentifies
Supported By
External Discretionary & Non-discretionary
Standards/Requirements
Reference: Wikipedia
Future State of Transport
Massively
Networked
Integrated
Public and
Private
Collaboration
Dynamically
Priced
User Centred
© iSAHA
Concept Analytical
Enterprise Design
Enterprise Service Bus (ESB)
Data Warehouse
User Interface
Portal Browser/Web Page
Print Thick Clients Email Thin Clients Mobile App M2M
Non-user Interface
Inte
grat
ed M
anag
emen
t Sy
stem
DATA WAREHOUSEBusiness Intelligence / Knowledge Reporting
Data Analytics Data Mining Reporting Scorecards Predictive Analytics
In-memory Analytics
? ?
Data Management
Master Data Management
Metadata Management
Data Models Data Mapping
+
Extract, Transform &
LoadIntegration Hub Import/
Export ScriptsMessage Queues
File Transfer (FTP & SFTP)
Integration
Internal/External Data Sources
© iSAHA
Future High Level Abstract Architecture
GIS
Traveller
Phone App
Vehicle
Telemetrics
Network
StatusSmart Cards
Accident
Data
Journey
Planner
CRM
Command &
Control
Engineering
Systems
Real Estate
Mgt
LicensingPerformance
Mgt
Revenue Mgt
Enforcement
Real-time
Fare Mgt
Traffic Mgt
Systems
Employee
AppsReal-time Messaging
?
Publish Data
Back Office
Systems
Event Processing
Data
Traveller
Profile
Integrated
Multi-modal
What if?
Planning & Analysis
Key elements
Integrated
Event Processing
Big Data
Partnerships
Reference: Russ Heasman: HCL: March 2016
News
Big Data & Transport
Source: http://www.information-age.com/it-management/strategy-
and-innovation/123459878/how-tfl-will-use-data-about-you-keep-
london-moving-its-population-soars
Source: http://acceleratecapetown.co.za/digital-cape-town/
Source: http://www.computing.co.uk/ctg/news/2452328/how-big-data-is-driving-
more-intelligent-transport
Source: http://www.iol.co.za/scitech/technology/software/app-a-game-changer-
for-commuters-1768183
Source: http://www.africanbusinessreview.co.za/technology/2192/Big-Data-can-
enable-world-class-transportation-in-South-Africa
Big Data
Case Study
Rio de Janeiro Municipal Operations CentreAfter a series of floods and mudslides claimed the lives of 72 people in April 2010, city officials recognised the need to overhaul city operations more significantly in preparation for the 2014 World Cup and Olympics in 2016. (United States Environmental Protection Agency, 2014) In collaboration with IBM, the City of Rio de Janeiro launched the Rio de Janeiro Operations Centre (ROC) in 2010 with the initial aim of preventing deaths from annual floods. This centre was later expanded to include all emergency response situations in Rio de Janeiro.
In traditional applications of top-down sensor networks, data from each department operates in isolation. However, ROC’s approach to information exchange is based on the understanding that overall communication channels are essential to getting the right data to the right place and can make all the difference in an effective response to an emergency situation. The information-sharing platform they created enables them to tap into various departments and agencies, and look for patterns across diverse data sets to better coordinate resources during a crisis.
The centrally located facility surveys 560 cameras around the city and another 350 from private sector utility concessionaires and public sector authorities (Centro de Operações da Prefeitura do Rio de Janeiro, 2014). The incoming feeds are aggregated on a single server and displayed across a 80-square meter (861 square feet) wall of tiled screens – a smart map comprised of 120 layers of information updated in real-time such as GPS tracking of buses, city officials and local traffic. With over 400 employees working in shifts 24 hours per day,seven days a week ROC performs a variety of functions aimed at improving the efficiency, safety, and effectiveness of relevant government agencies in the city. While much of the attention paid to the centre focuses on emergency monitoring and response, especially related to weather, a significant portion of the work undertaken relates to ensuring the smooth functioning of day-to-day operations like transport.
Source: Photo: http://www.museumofthecity.org/project/rio-de-janeiro-and-ibms-smarter-cities-project/ | Case Study:
International Transport Forum – Big Data and Transport
Side by side feeds for weather and traffic feeds help city officials to respond
effectively to oncoming storms and traffic issues
In a statement on the use of sensor-based systems to correlate situational events with historical data at their Intelligent Operations Centre for Smarter Cities, IBM’s Director of Public Safety explained “The aim is to help cities of all sizes use analytics more effectively to make intelligent decisions based on better quality and timelier information. City managers can access information that crosses boundaries, so they’re not focusing on a problem within a single domain. They can start to think about how one agency’s response to an event affects other agencies”.
Conclusion
The world has become a massive interconnection of smart device that
generate data continuously and this is extremely relevant to Transport
In terms of ITS this is a limitless opportunity that will change the
way we view, plan and manage transport.
Traffic prediction has a major benefit
for determining demand and supply
and simulating alternate options to
resolve issues.
Congestion management options and
impacts can be determined
Disaster response planning can be
simulated and planned in advance.
Numerous other opportunities to many to mention are becoming
possible.
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Thank you