A presentation by Neil Frost (Chief Executive Officer: iSAHA), at the Transport Forum SIG: "Cost Effective Public Transport Management Systems" on 12 May 2016 hosted by University of Johannesburg. The theme of the presentation was: "Big Data and Public Transport."

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ISO Big Data in Transport
Potsdam Workshop
Neil Frost and Warwick Frost
May 2016

2. 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

3. 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
Human
generated
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
Content
Format
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 Transcription
Speech
Analytics
Translation 3D
Reconstruction
Real Time
Near Real Time
Analysis
Type
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/

4. 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

5. How does this relate to ITS
© iSAHA

6. Storm water
Extent of Data requirements
© iSAHA

7. Real Time Traffic Flow
ETA Predictions
Reference: Google Maps; https://maps.google.com/

8. INRIX
Traffic Information
Reference: INRIX; http://www.inrix.com/products/

9. INRIX
Connected Driver
Reference: INRIX; http://www.inrix.com/products/

10. 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 NAVIGATION
Advise on best transportation
company route based on
today’s forecast
BUS PRIORITY SIGNAL SYSTEM
Prioritize green lights for bus to ensure it
arrives at the station on time
INTEGRATED TRANSFER BETWEEN BUS & TRAIN
Bus operation management ensures bus arrives on
time to catch desired train
INTEGRATED FARE COLLECTION SERVICE
As 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 DETAILS
DESTINATION
CURRENT LOCATION
Title

11. Typical Enterprise Architecture
Enterprise
Discretionary &
Non-Discretionary
Standards/
Requirements
Feedback
Business
Architecture
Information
Architecture
Information
Systems Architecture
Data Architecture
Delivery Systems Architecture
Hardware, Software, Communications
Drives
Prescribes
Indentifies
Supported By
External Discretionary
& Non-discretionary
Standards/
Requirements
Reference: Wikipedia

12. Future State of Transport
Massively
Networked
Integrated
Public and
Private
Collaboration
Dynamically
Priced
User Centred
© iSAHA

13. 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
IntegratedManagementSystem
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 &
Load
Integration Hub Import/
Export Scripts
Message
Queues
File Transfer
(FTP & SFTP)
Integration
Internal/External Data Sources
© iSAHA

14. Future High Level Abstract Architecture
GIS
Traveller
Phone App
Vehicle
Telemetrics
Network
Status
Smart Cards
Accident
Data
Journey
Planner
CRM
Command &
Control
Engineering
Systems
Real Estate
Mgt
Licensing
Performance
Mgt
Revenue Mgt
Enforcement
Real-time
Fare Mgt
Traffic Mgt
Systems
Employee
Apps
Real-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

15. 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

16. Big Data
Case Study
Rio de Janeiro Municipal Operations
Centre
After 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”.

17. 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