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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