Smarter Transportation: The power of Big Data and Analytics

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Smarter Transportation:

The power of Big Data and Analytics

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BECAUSE WE WANT IT FOR

THE FUTURE.

How?

The world is becoming

INSTRUMENTED

Smart sensors on road, in cars, Connected cars everywhere

The world is becoming

INTERCONNECTED

Linking information on road, in cars and railways, throughout the

supply chain – “the internet of things”

The world is becoming

INTELLIGENT

Cars talking to each other, sensors talking to each other, we can

predict where traffic jams are, before and while you drive

Fleets / Cars avoiding accidents, Preventive fleet maintenance,

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Gigabytes of real-time traffic data available today. But by the time it is received, it is no longer representative of the actual traffic

Issue: “real-time” is too late

IBM’s Traffic prediction: intelligence combining

sensor data & sophisticated algorithms that

create relevant insights from the raw data

IBM Innovation: forecast the future

Prediction: accurately forecasting future traffic conditions

v

o

lum

e

blue = forecast black = actual

_{red = incident}

time

“

With our Traffic incident & emergency response system, we can react and respond

faster and more efficiently. It’s helping us save lives.”

Eduardo Paes, Mayor, City of Rio de Janiero

0 50 100 150 200 250 200 400 600 800 ID 103011024 0 50 100 150 200 250 0 500 1500 2500 3500 ID 103063556 R E A L[ pl , ] 0 50 100 150 200 250 500 1500 2500 3500 ID 103064570 R E A L[ pl , ] 0 50 100 150 200 250 1000 2000 3000 4000 5000 ID 103064571 0 50 100 150 200 250 1000 2000 3000 4000 ID 103064655 R E A L[ pl , ] _rr r rrr r r rr r r r rr r r r r rr r r rr r r rr r 0 50 100 150 200 250 500 1500 2500 3500 ID 103064789 R E A L[ pl , ] 0 50 100 150 200 250 1000 2000 3000 4000 5000 ID 103067974 0 50 100 150 200 250 0 100 200 300 400 ID 103072372 R E A L[ pl , ] rr rrr rr r r rr_r rrrr r r rr r r r r rr rr r rr r r r r r r r r r r rr rr_r rrr r r rrrrr_rrrr rrrrrrr rr rrrrrrrr r r rrrr_rrrrr rrrr r r r r r rr rr r rr r rrrr r r rr r rrr rr r rr rr r rr rrr r rrr r r r r r rrrrr r r r r r r r r r r r r r r rr r r r r r r r rrr r r r r rrr r r r r r r r r r r r r r rr r r r r rr r rr r 0 50 100 150 200 250 1000 2000 3000 4000 ID 103102451 R E A L[ pl , ]

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Transportation Data Analytics enhances vehicle safety

Analytics generate

Infra 2 Vehicle warning

and triggers

Assisted breaking

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Creating benefits from big data across multiple regions & organizations

• A

wareness

: Leverage real-time visibility across city data sources

• A

nticipate

: Proactively identify problems to mitigate impact to services

• A

ct:

Coordinate cross-agency operations to drive better business outcomes

Benefits

Increased Revenue

Operational efficiencies

Reduced Costs

Economic Vitality

Challenges

Reduced Budgets

Increase Delivery

Expectations

Aging Infrastructure

Increased Threats

ITMC

Transportation

Management

Center

ITMC

Transportation

Management

Center

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Cities around the world have experienced significant improvements after implementing

our Intelligent Traffic Management Systems *

Source: PH Gov Figures; IBM; RITA US Dept of Transportation; Cycling Embassy of Great Britain; USA Today, Leeds City Council,

Potential Benefits

↓

Traffic delays

-10%

↓

Congestion

_-25%

↑

Public transport ridership

↓ Wait at pedestrians crossings

↑

Walking and cycling travel

+10/30%

+80%

-35%

↓

incident response time

+80%

↓

vehicle collisions

_-30%

↓

cyclist injuries

-30%

↓

pedestrian accidents

-65%

* Benefits are illustrative of potential for positive change, based on experience of many cities around the world

↑

Resultant Retail Sales

+6%

↑

Fuel Economy

+15%

↓ Emissions

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Smarter Transportation solutions are generating funds

Gothenburg, London, Stockholm and

Norway

An intelligent road charging system reduces traffic

by 20 percent, increases public transport usage by

40,000 users per day, and dramatically cuts

peak-time road congestion

Wolfsburg, Shanghai

Merge Mobile Real-Time Sensor Data from Vehicles

and Road Traffic Data to enable Officials to Monitor

and Act on Unexpected Road Conditions, lowering

costs and congestion

Lyon, Optimod

Can forecast traffic conditions 60 minutes into the

future to help prevent planning congestion for

goods delivery. The solution generates alternative

routes and automatically advises delivery planners

to enhance the flow of traffic & transit

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Miami City & Stadium: Sun Life stadium

Use Intelligent Operations Center for Smarter Transport to address

the logistical and management challenges facing its staff while

offering visitors a unique fan experience. IBM's IOT provides a

complete interconnected view of stadium activity, from weather

alerts, to real-time security, to traffic flow into the stadium creating

a seamless flow of visitors attending a game, to insights into dining

experience or buy food at concession stands prior to a big game

Nice Côte d’Azur, City and Airport

A Transportation management center and asset management

solution centralizes real-time monitoring and reporting of all the

city and airport's critical assets, allowing operators to predict and

prevent transport and maintenance issues and provide a

consistently high standard of service to travelers, airport tenants,

such as retail stores, car rental agencies and the traffic and fire

department

Smarter Transportation TMC Solutions

Rail transport : SNCF

Use an Linear Transportation asset management solution to

optimize maintenance of its “rolling stock, by integrating

maintenance processes and centralize information, including

real-time data collected by intelligent sensors placed on railway

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Intelligent Transport Management center

Leveraging Multiple Data Sources

Evolution of Data Sources

Connecting across multiple data sources for a single

picture across the city

Video

Sensors

Prediction

Social Media

Analytics

Correlation

Factual

_Mathematical

_{Observational}

Sentiment

Citizen

Collaboration

Mobil Phone Tracking

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Transport Fleet Management

Center based solution with

Apple cooperation

IBM SW: IOC, Cognos, SPSS,

Streams, Social media

Door to door route planning

Multi modal travel with real time

and predictive information

Payment for your Transport

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Increasing Traffic Awareness via Twitter

Nairobi example

Post traffic information periodically for selected 50 roundabouts/road

Conducted a survey to get feedback from users

Image

Analytics

&

Network

Analytics

Image

Analytics

&

Network

Analytics

Areas selected for Traffic Awareness

© OpenStreetMap contributors, CC BY-SA

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Key Process Indicators are monitored and managed to trigger actions

Example: Real time monitoring of a bus environment

Factual

Observational

Sentiment

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LF, UHF

802.11p

_802.11p

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Top Connected Devices We May See in 2020 & Their Estimated Values

Connected Car $600 billion

Clinical Remote Monitoring $350 billion Assisted Living $270 billion Home & Building Security $250 billion

Pay-As-You-Drive Car Insurance $245 billion New Biz. Models for Car Usage $225 billion

Smart Meters $105 billion

Traffic Management $100 billion Electric Vehicle Charging $ 75 billion

Building Automation $ 40 billion

Source: PC Today, April 2012

55% Automotive related

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1974 : Amsterdam the “White car” sharing project (40 Years)

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We Connect Transportation Fleets

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Snow and Ice rain real time – November 25, 12:00

Heavy Snow / Rain

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NL DOT (Rijkswaterstaat) and University of Eindhoven have

concluded that

in-car data

provides detailed information on road surface

conditions.

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Miami Experience (2014) – Prediction of bus bunching

Predictive analytics for Miami Transit Operations division:

• Real-time alerts on where and when bus bunching

will occur in the future

• Allow for anticipative correction and aid in maintain

bus headways

• Provide Miami Transit Schedule & Planning divisions

with data insights and pattern discovery for schedule

optimization and refinement

• Identify system performance characteristics and

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Space – time frame of reference

Time

Distance

along

route

(Offset)

Start stop End stop

Steeper the line – faster the bus

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What is bus bunching?

Buses serving the same customers arriving together

Bad for passengers

• Longer wait time

• Lower service quality

Bad for operators

• Lower capacity utilization

• Revenue implications

Key dimensions

• Variability in traffic conditions

and boarding patterns

• Unstable system (once it

occurs, it is likely to worsen)

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When and where does bunching occur?

75,000 alerts generated during pilot for 9,000 bunching predictions

Majority of events along Route 119

• (serves high-traffic areas of downtown Miami, Miami Beach and commercial areas like Aventura Mall)

Systematic operational patterns uncovered

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When and where does bunching occur? Route 119 Southbound

Hour

of day

Direction of travel

Start

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When and where does bunching occur? Route 119 Northbound

Hour

of day

Direction of travel

Start terminal end terminal

Bunching of

buses early

in the route

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Position updates

Trajectories

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The data

• 2.6 million bus positions

during pilot

• ~20,000 bus runs for the

four routes

• Key dimensions

• On-time performance

• Dispatch performance

• En-route performance

• Dwell times

• Correlation with

ridership

• Correlation with

schedule

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Data for 119 Northbound

• Characterize performance characteristics

7.7 Miles (12.5 km)

33 min 44 min

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Detection of the problems using Big data analytics

Average speed profile (kmh) along 119 Northbound

Lincoln Rd @ James Av Higher variability Higher variability 25mph 50mph

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Schedule

refinement

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Transport BIG data Use Cases for Transportation Management

Connected

Vehicle

Data Cloud

IBM

Emergency

& safety

m-Commerce

Environment

protection

Communicatio

n/collaboratio

n

Traffic &

navigation

e-Intelligent

driving

Industry

solutions

Collaboration Group tracking PAYD insurance Location-based advertising Vehicle usage Public transport

Family & friends

Taxi management Urban management Safety alert Emergency assistance Road warnings Driver status Traffic prediction