Smarter Cites Challenge 05202016 LG Final

IBM Thought Leadership White Paper
Smarter Cities Challenge
Smarter Transportation
Building better transportation systems
Highlights
Population growth, an increasing number of vehicles on the road, the environmental concerns that
accompany this trend, along with a lack of infrastructure are creating both challenges and
opportunities for transportation professionals worldwide.
The world is an increasingly instrumented, interconnected and intelligent place—smarter cities can
infuse intelligence into the entire transportation system to reduce congestion, improve safety, and
provide greener environment for their citizens. The USDOT Smarter Cities Challenge offers cities
the means to jump start the Smarter Transportation systems.
Meanwhile, technology innovations in recent years have enabled development of next-generation
intelligent transportation systems (ITS). With technologies such as Internet of Things (IoT),
Connected Vehicles, Cloud platform, advanced data analytics, and cognitive computing,
transportation agencies now have an unprecedented opportunity to elevate their existing ITS
systems to a new height.
Introduction
In this paper we will discuss the IBM vision for ITS and how a smarter transportation infrastructure
can support growth, while reducing environmental impacts. Intelligent transportation systems will
transform the way cities look at safety and mobility. Cities can harness predictive analytics and
leverage social media to detect problems. We will weigh the economic costs of congestion—how
can cities improve commuter experiences and quality of life by predicting and improving traffic
congestion and traffic flow? We will consider what cities should look for when evaluating intelligent
transportation system solutions. And finally, what are the experts predicting for the intelligent
connected vehicles?
Thinking and acting in new ways
We live in an increasingly instrumented, interconnected and intelligent world. IBM is helping cities
harness the potential of smarter systems so they can infuse intelligence into the entire
transportation network to effectively address the challenges they are facing. We believe the truly
intelligent transportation system should be:

Instrumented: Smarter transportation systems are able to track traffic from source to
destination, monitor conditions in real time, and instantly identify defects and inefficiencies
across assets and infrastructure using information obtained from installed and mobile
technologies.
Interconnected: Smarter transportation systems enable the integration of all this
information to give transportation professionals and users easy access to continuously
updated information, travel choices and shipment options, with instant notification of any
irregularities in the transport process.
Intelligent: Smarter transportation systems apply advanced analytics to real-time data to
proactively monitor the health of their infrastructure and improve management. This
capability can help cities take measures such as dynamically adjusting conditions,
aligning congestion toll pricing with demand, initiating security measures and making
decisions based on environmental impact as appropriate.
Making transportation infrastructure and processes more instrumented, interconnected and
intelligent will help cities overcome the challenges they face. This approach also recognizes that
data provides one of the greatest opportunities for making the planet smarter, too. Becoming
smarter is leading to new ideas, efficiencies, and equally important, new possibilities for
sustainability of our planet. Once a city has established the three “I” environment, it can progress
to what we refer to as the three “A”s—aware, anticipate and act. With awareness, a city can
leverage real-time visibility across city data sources (transportation and other related agencies). It
can anticipate and proactively identify problems to mitigate impact to services. Then act to
coordinate cross-agency operations to drive better societal impacts, reduce congestion and
increase public transport use.
How can a city create a smarter transportation infrastructure to support growth?
How can a growing city keep up with ever-increasing transportation demands? They need a better
understanding of the overall movement of people in and around the city and the interdependence
between their multiple modes of transportation so they can more effectively balance supply and
demand as well as manage these movements. For instance, a large city in Europe uses an analytics
solution that enables near-real-time collection, aggregation and analysis of huge volumes of
people-movement data. This “city in motion” solution calibrates data against surveys and other
sources, and then converts it into demand models that the city can use to optimize transit systems.
Using aggregated mobile phone location and transit system data, the solution creates a heat map

that depicts the density of people during different time periods such as morning and evening
commutes. It can also drill down to show individual patterns of movement; for example, the city
uses the data to scientifically model from where and when commuters travel to optimize the bus
routes that will connect to a new metro rail line.
Efficient transportation and transit system design requires a detailed understanding of travel
patterns within an urban area. In the past, transportation planners have relied on limited survey
data that includes little information about choice riders or non-users of transit. Transportation and
transit agencies need a technology based data gathering system and route optimization process to
address these challenges. Researchers have designed a system that computes origin destination
models based on multiple sources of data: sampling through a smartphone application; sampling in
transit using smart cards; and aggregating total movement through use of existing mobile phone
data. The aggregation of this data gathering offers the richest and finest spatio-temporal
granularity of information. This data is then analyzed to identify trips based on activity by time of
day. This model offers the largest sample size although at coarser spatio-temporal granularity. The
analysis and the origin destination models are then used to design transit routes to optimize
performance indicators such as average journey time, headways, and wait times. These optimal
routes, when implemented, will substantially help transit agencies meet demand while reducing
operating expenses. This visual overview of how people move around a region has enormous
implications for transportation planning as well as overall city forecasting and directing policy

decisions. Leveraging existing data without the cost and complexity of adding additional devices
and infrastructure to create a smarter city.
Cities can integrate smart sensors that are built into the physical infrastructure, vision-based
systems, vehicles as mobile sensors, and computer-aided decision-making tools that are based on
specific real-world scenarios to get greater insight on these mobility patters. Public transportation
systems can then help alleviate a city’s traffic congestion if its operations are managed efficiently.
Otherwise a strained system can actually contribute to the problem.
Faced with an inefficient public transportation system, a provincial-level city in China uses an
advanced analytics platform to understand ridership levels as well as traffic and usage patterns
across the city’s transportation systems. The powerful solution helps the city transportation
administration officials accurately identify and forecast transportation demands and take proactive
measures to improve and adjust the transportation infrastructure as needed. For instance, if
analysis indicates that during certain times and days of the week, bus line A is more congested
than others, officials can determine the precise number of lines to add, reconfigure routes using
less congested areas and modify schedules to ensure only a certain number of buses are on the
road at various times of the day. By optimizing capacity, routes and schedules, the city can
encourage use of public transportation over private vehicles, reduce the number of vehicles on the
road, and improve traffic flow to help alleviate the city’s traffic congestion.
How to reduce environmental impact of transportation?
Air pollution caused by traffic can cause big problems from both an environmental and economic
standpoint. In October 2013, thick smog blanketed parts of China for two days, blocking road, train
and air traffic, and causing the closing of primary and secondary schools. The visibility in urban
areas was less than 50 meters. Citizens and traffic police were forced to wear masks to escape the
pungent smell and unhealthy effects of the smog.i
According to the World Health Organization, the effects on health of transport-related air pollution
are among the leading concerns about transportation.ii
Pressure is growing to reduce emissions and
the negative environmental impact of transportation.
Smarter traffic management can have a significant impact on reducing emission rates. Cities can
promote highly efficient public transport and make improvements in the flow of traffic to reduce
idling vehicles. Smarter cities can use GPS-based tools that measure road conditions, speeds,
travel times, road closures and road work performance. This can help drivers choose a route that

leaves a minimal environmental footprint and provide environmentally-relevant real-time
transportation data. Congestion charging and low emission zone programs can help alleviate
congestion and vehicle emissions. For example, a metropolitan city set up an automated and
streamlined payment system for their congestion charging zone—a transformation made possible
by fully integrated operations and infrastructure, connecting vehicle detection cameras with
payment interfaces, call center operations and enforcement systems. Drivers can register with a
credit or debit card, authorizing the system to deduct payments automatically when the vehicle
travels within the congestion charging zone.
Wearable environmental sensors (like those available from TZOA, AirCasting, and the Air Quality
Egg) attached or embedded in the vehicles can detect and feed local air quality readings to a
mobile app that formats, graphs and displays the results, including carbon monoxide, nitrogen
oxide, sulfur dioxide and particulate levels—giving riders a way to understand their roadway
environment, even to avoid damaging air quality conditions in route.
Crowd-sourcing and cloud computing provide actionable environmental data Ubiquitous
vehicle/personal environment sensors and mobile devices will not only collect actionable
environmental data, the insights that data yields will create the foundation for healthy personal
choices, sound business decisions, and global/local environmental activism. Real data will make it
possible to study health impacts of air pollution, to identify environmental dangers and to demand
better environmental protections from our governments.
Using connected vehicles and analytics to detect and manage incidents
What if your vehicle could warn traffic management and road authorities about potential or existing
road hazards? Just like a building’s plumbing system, traffic flow is an interconnected system
where individual actions can have a major impact on the system as a whole. Small problems, such
as a single vehicle braking can force vehicles behind it to brake as well, quickly leading to a traffic
jam. Today’s vehicles, and the roads they drive on, may already be equipped with thousands of
sensors that record information. With smarter transportation capabilities, cities can capture and
gain insights from sensor data to help improve traffic conditions and the driving experience. Cities
can pick up on patterns to reduce accidents and related costs, and even predict where events are
most likely to happen. Having visibility into all of the city’s data sources can help them anticipate
and proactively manage problems, and mitigate the impacts.
A regional government in the Netherlands is working with IBM to capture and analyze near-real-
time vehicle and road-sensor information to provide traffic authorities with the information they

need to respond to and alleviate traffic problems more quickly. A sophisticated analytics engine
monitors and analyzes incoming data to flag traffic events and notify authorities. Commuters
equipped with a smart phone application can be alerted to incidents in near-real time, allowing
them to find alternate routes around accidents and traffic jams. Near-real-time analysis and early
warning about almost any road event is expected to help reduce congestion and improve traffic
flow on the region’s roadways.
By connecting vehicles approaching an intersection to the phase of the traffic lights, using V2X
DSRC wireless technology, a smart vehicle will be able to adjust its acceleration and deceleration,
either automatically by the vehicle’s drivetrain or actively by the driver, to achieve optimum vehicle
fuel efficiency therefore reduce fuel consumption. Pilot by such coordinated traffic signal
management, from project like GlidePath, has shown a possible 13% reduction of fuel
consumption. Similar to GlidePath, Europe’s Green Light Optimal Speed Advisory (GLOSA) is being
trailed in a project named Compass4D which has been run in seven European cities: Bordeaux,
Copenhagen, Helmond, Newcastle, Thessaloniki, Verona and Vigo. GLOSA gives the drivers advice
on the best deceleration/acceleration strategy to approach the intersection along with traffic light
signal phase (red/green status) and timing. Preliminary results showed an average savings for
individual vehicles range from 4.7% to more than 11%. IBM participated in the Compass4D project
in the cloud backend.
The continuous deployment of more convenient charge stations will help the on-going adoption of
Electric Vehicles that contribute to zero tail pipe emission. IBM teamed With ESB, a utility provider
in Ireland, to develop an Electric Vehicle Smart Charging IT System in Ireland. The system adds
Flexibility to Smart Grid Operations during Mass-Scale EV Charging across Ireland. A fully
integrated smarter charging IT system helped to manage over 1,000 electric vehicle public charge
points, rolled out across Ireland by ESB ecars. Together the companies will add a layer of
intelligence and convenience to the charging process, allowing EV drivers to access, charge and
pay, using an identification card. Additionally, this project will provide utilities with access to
energy usage data that can help improve smart grid operations, reduce power strain during peak
charging times, and ensure reliable energy distribution to customers, supporting the country to
achieve its audacious goal of generating 40% of the country’s electricity from renewable sources.
Connecting and coordinating the vehicles with cities’ street lights with V2X technology is another
example where energy can be saved. By turning off or dimming off street lights with reduced traffic
or pedestrian presence, electricity used by street lights can be saved

Leveraging social media data
What if a status update on a social media site could be used to help authorities determine whether
to re-route a city bus and avoid a traffic snarl for riders? People connected to Twitter, Facebook
and other social networks often report information about incidents in real time—by tweeting or
posting about accidents, traffic congestion and detour information. Social media and smart phone
apps can gather rich, near-real time insights into the use of public transportation while
simultaneously providing riders with helpful information that makes their travels more efficient.
Drivers can get personalized commute forecasts informed by social media that help them avoid
gridlock before they even get in the vehicle. They can get answers to questions such as “what will
traffic be like a half an hour before I leave” or “will there be a parking space when I get to where
I'm going?”
A U.S. city uses sophisticated analytics of complex data sources gathered anonymously from
carriers’ mobile phone systems, smart phone apps, social media sites, GPS tracking of devices, fare
collection systems, weather data, community calendars, and video cameras located on vehicles and
on public streets. The result is a dynamic, moving picture of the city transit system. When
combined with geospatial intelligence applications, city planners literally see how people and
vehicles move in real time and can make rapid proactive changes, such as rerouting buses to avoid
traffic, and also plan for long term operational goals, such as adding new routes. In exchange for
participating, transit users become essential partners, with valuable information sent directly to
their mobile phones—information they can use to improve their commutes and their quality of life.
Weighing the economic costs of congestion
Congestion costs time and money and is a drain on the economy. On average, travelers incur 50
hours of traffic delays per year.iii
In 2011, U.S. road congestion wasted approximately 2.9 billion
gallons of fuel and cost USD121 billion.iv
The cost of pollution, accidents, and congestion can add
up to more than 10 percent of a country’s GDP.v
While a 10 percent reduction in traffic during
congestion hours and peak demand will almost eliminate all congestion. Eliminating congestion can
lead to a two percent increase in regional GDP. In emerging markets this can even be higher Cities
cannot afford to make mistakes when building out their transportation networks. Every investment
has to create efficiencies and support economic development.
Cities that invest in smarter transportation systems can see a clear return on their investment. If
we compare the economic benefits of investments in road infrastructure to investments in smarter
transportation and ITS solutions we see a large difference. University studies show each dollar

invested in infrastructure provides USD1.2 to 1.8 in return in economic value while investments
made in smarter transportation and ITS return USD6 to 8 on every dollar invested.
Reduced vehicle use in mature and developing countries has many economic benefits including
reduced road and infrastructure costs, reduced pollution, as well as the business and economic
growth development brought by such cost and quality of life benefits. Cities struggling with
congested roads have an incentive to make public transportation more attractive. Schedulers can
place buses where they are most needed to ensure they operate at full capacity without wasting
time, gas and money. This can help ensure commuters consistently arrive for work, ready to be
fully productive. If a city is a tourist destination, schedulers can plan ahead to accommodate
seasonal traffic.
Improving quality of life
The IBM Commuter Pain Index study found that commuters and transport users surveyed were
increasingly frustrated by their daily commute, with many reporting that traffic had negatively
impacted their health and productivity.vi
Traffic congestion, aggressive or rude drivers, low speed
and unreliable journey time all contribute to driver frustration. IBM compiled the results of the
survey into an index (Figure 1) that ranks the emotional and economic toll of commuting in each
city on a scale of one to 100―with 100 being the most onerous. The index reveals a tremendous
disparity in the pain of the daily commute from city to city. Metropolitan-area commuters in many
cities struggle to get to and from work each day, often with negative consequences. For example,
in Nairobi, 35 percent of drivers reported that they have spent three hours or more in traffic, and
in Moscow, over 45 percent. In Beijing and Shenzhen, anger from traffic is by far the highest
among the cities surveyed, while in New Delhi, Shenzen and Beijing, huge numbers of drivers have
simply turned around and gone home rather than deal with the frustration of their intended
journey. Mexico City ranked number one overall when it came to people specifically avoiding trips
altogether due to traffic.

Figure 1: 2011 IBM Commuter Pain Index
Drivers surveyed felt that much of this stress could be reduced by the greater use of technology.
Indeed, cities can improve commuter experiences and quality of life by predicting and improving
traffic congestion and traffic flow. Smarter transportation systems can help:
• Improve lives by giving citizens insights that can help them decide whether to use private or
public transport.
• Improve city services by enabling emergency vehicles to get to their destination faster by
knowing in advance if a road is closed or traffic detoured.
• Lower costs to businesses because they can schedule travel and other logistics during off-
peak times.
• Improve people’s experience at events or tourist sites by providing stadium visitors data
that can help them find the most convenient parking.
Pioneering innovative approaches to smarter transportation
Intelligent transportation systems provide city planners and operators with a comprehensive look
at the state of their city's roadways. But not all systems are created equal. An intelligent, multi-
modal transportation system that uses the latest technology can help cities perform advanced
traffic analysis and optimization for better decision support. It can help cities increase situational
awareness across the entire transportation network and analyze traffic performance to improve
travel experience. It can also serve as a tool to centralize the monitoring of vehicles and estimate
transit and arrival times.
When evaluating an intelligent transportation system, cities should look for a standards-based
integration which makes it possible to aggregate data from a wide variety of traffic and road data

capture systems spanning across multiple device types and vendors. This aggregation helps
provide a unified view of traffic data that can be a valuable tool to gain actionable intelligence.
Centralized access to this wealth of traffic-related data, along with the ability to analyze both
historical traffic patterns and real-time data, provides an opportunity for cities not only to improve
traffic congestion in the short term, but also to address long-range planning goals. Armed with
reports that monitor traffic performance and patterns over time, cities can make significant
progress in cutting congestion, emissions and noise.
Driving the new generation of intelligent connected vehicles
IHS Automotive forecasts there will be 152 million actively connected cars on global roads by
2020.vii
Intelligent connected vehicles have the potential to transform the way cities think about
safety, mobility, traffic flow management and environmental performance. Anticipatory driving will
be enhanced through the development of a next generation 'electronic horizon' platform, which will
ultimately make highly automated driving a reality. Vehicles with embedded sensors will not only
receive data, they will also transmit information such as position, speed or deceleration to the
Cloud where data will be processed, analyzed and acted upon. The result will be a real-time map
that will enable a vehicle to literally 'look around the corner'.
Realization of the fully connected vehicles requires technology expertise across Big Data,
embedded intelligence and the ability to deliver services over a highly scalable cloud platform.
Three strategic innovation forces will advance automotive industry megatrends—on the one hand
vehicle automation, on the other hand reduced emission, fuel-efficient driving or, vehicle
electrification and finally connectivity. The emerging digital world provides powerful stimulus to
each of these megatrends. The vehicle will not just be connected to the Internet, it will become
part of it - Internet of Things. Networked, intelligent mobility opens up enormous potential for
innovation and will enable several new functions for drivers. These include cloud-based voice
recognition, real-time traffic flow data exchange and anticipatory driving based on online and
navigation data—leading to vehicles that will be even safer and more efficient.
Testing predictive analytics for traffic management
Intelligent traffic management based on precise forecasting techniques can help cities anticipate
and avoid traffic congestion and possibly reduce the volume of traffic, resulting in a more
sustainable transportation network. A German city conducted a pilot to predict and manage traffic
flow and road congestion. The pilot demonstrates how the city can anticipate, better manage, and
in many cases, avoid traffic jams and trouble spots across the city using analytics technology. The
city’s traffic engineers were able to predict traffic volume and flow with over 90 percent accuracy

up to 30 minutes in advance. As a result, travelers would be able to better plan ahead and
determine whether they should leave at a different time, plan an alternate route or use a different
mode of transportation.
Improve traffic mobility with congestion-based road user charging
Congestion-based Road User Charging (RUC) is another effective traffic management method for
cities. By adjusting RUC rates based on real-time and predicted traffic congestion levels,
transportation agencies can balance demands in congested roads and divert traffic to alternate
routes to achieve overall network efficiency.
The IBM RUC system provides functions for toll transaction processing, customer service and
financial management. IBM RUC is a state-of-the-art system developed in several advanced RUC
projects.
The congestion charging systems in Stockholm and London are the world’s leading demand
management schemes in city environments. The London system processes over 600 million
transactions annually. In Stockholm, our innovative ALPR solution has made it possible to operate
the system with a very high accuracy even without OBUs.
The free-flow tolling system in Brisbane helped Queensland Motorways migrate to free-flow tolling
and won the International Bridge, Tunnel and Turnpike Association (IBTTA) Toll Excellence Award
in the Technology category. IBM is currently building a shared central system for all Norwegian toll
roads. The new system will offer multi-tenancy functionality and host 40 toll companies with 1.6
million toll transactions per day.
Expedite implementation with open standard repeatable solutions
Increased Urbanization makes traffic jams an increasing threat to economic growth and well being
of citizens (accidents, emissions, health). The IBM TMC solves the problem: Traditional traffic
management solutions just reduced time and money wastage while IBM’s Transportation
Management center (TMC) allow cities to gain insight, influence demand (Incl. pricing) and guide
citizens in real-time. IBM TMC’s provide transportation insight and reduces the carbon footprint;
thus making commuting a hassle-free and enjoyable experience for citizens while generate
economic benefits for City. IBM´s Intelligent Transportation Management Center, is a repeatable
solution that consists of proven IBM modular components that are based on “open standards”, is
upgradable with partner products & adaptable for future developments and is Cloud enabled. IBM’s
Transportation Management Center (TMC) integrates existing technologies into a single information

model for advanced proactive analysis to improve situational awareness and enhance transport and
incident planning and management.
This is accomplished in three stages:
1. Traffic data is captured from disparate source using technology options that include loop
detectors, radars, cell phone data, video analytics (IVA) and data providing partners, and is
transformed and fused as needed, before it is sent to the TMC operation center.
2. The TMC stores, analyses and presents real-time visibility into traffic condition and the
transport network in the City, historical patterns, predictive inferences, and automatic detection
and optimisation recommendations on its dashboard.
3. Standard operating procedures and integrations with system devices are implemented within
the TMC to turn decision support into active traffic management.
Collaborative tools allow for more efficient use of historical traffic and incident data, improved
traffic and incident management, and traffic and incident prediction. These tools will help improve
the efficiency of the transport network, increase illegal vehicle use detection and improve road
safety.
Cities and regions are in need of real time data which can serve as an information source for the
modern TMC. In a lot of cased these sensors can be the existing traffic sensors like loop detectors,
Bluetooth or interactive traffic light systems. These sources are good to use if they are available,
but unfortunately not all these sensors have been made interactive yet, as this requires extra
investments.
As a solution to resolve the problem there are now many new forms of sensors available that do
not require an investment from the city and are already pre integrated in the IBM TMC solution.
These are:
• Social media analytics:
• Traffic camera’s as a sensor
• Weather Analytics and Prediction
• Sensors in cars (taxi’s)
Social media: It is possible to get data from social media about road network availability,
congestion, bus or tram delays and even incidents by just “listening” to what is mentioned about
transport the city, bus line etc. in social media. The IBM TMC solution supports social media as a
sensor. The TMC will be able to use the data from social media and will also allow cities to send
back data about road conditions buy using social media.

Traffic camera as a sensor: The modern intelligent video analytics (IVA), which are a part of the
TMC solution are able to detect incidents and accidents on the road. After fine-tuning the camera to
the IVA interface we are also able to count cars, speed and even do classification of cars. This input
to the TMC will give the city the insight without a massive investment in sensors, as traffic cameras
are already available in most cities on major junctions etc.
Sensors in cars: The connected car is already a reality. In the TMC we have the connected car
sensor interface already implemented. The only thing the city has to do, is get the data from the
taxis or their own fleet of cars. This can be achieved by asking the taxi companies as part of their
license agreement to share the location data and/or connected car data to the city and the TMC.
The investment per taxi or city fleet is relatively low (less than 100€ per vehicle) and will also give
the taxi / fleet owner lots of information about the taxi or fleet itself and gives them also insight on
what is happening with the fleet. For the government and TMC this information is very effective in
managing the traffic using TMC.
IBM’s Traffic Prediction Tool (TPT) has proven to be more accurate than existing traffic prediction
methods and can predict conditions up to 60 minutes in advance. It provides valuable input for
traffic operators in real-time, as well as having the capability of providing input for traveler
information systems to go beyond real-time data to leverage future predictive data in route
planning.

Table 1: Key functions of IBM’s Transportation Management Center
Key Function Overview Detail
Perceiving Creating, and
Feeding a
Geospatial
Graphical User
Interface
The TMC is capable of creating the needed
situational awareness while seamlessly integrating
information of transportation devices. It can show
geospatially-tagged and non-geospatially tagged
information from any source the user may
designate. It can serve to assess the level of
congestion on select road intersections or identify
city incidents that would impact traffic.
Understanding Processing and
Analyzing the
Available
Information
The TMC simplifies the challenges faced by the
Transportation Manager and end-user of making
sense of the available information.
Projecting Providing Decision
Making Simulation
and Automation
Resources
The TMC solution provides simulation and
automation resources that assist the end-user and
the Transportation Manager to make informed
decisions about the actions taken. It takes into
account the possible consequences of initiated
actions with prediction capabilities in order to
improve the likelihood for success of alternative
routes and of remedial or preventive actions.
Sharing Facilitating
Collaboration,
Publication, and
Implementation
The solution enables the effective management of
traffic incidents through field coordination and inter-
agency collaboration. The processes by which the
highest possible level of situational awareness may
be achieved, the most accurate representation that
level of situational awareness is distributed, and
which tasks are involved in implementing a
response to a disruptive event are assigned and
monitored.

The functionality designed into the solution addresses all elements of situational awareness,
assisting the Transportation Manager in;
• Perceiving the status, attributes and dynamics of relevant Transportation elements,
• Understanding the significance of those elements in light of the goals of the Transportation
Manager,
• Predicting the outcome of actions taken in the environment and
• Sharing the decisions, actions and decision-making process with those who need it.
Enhance transportation infrastructure maintenance with predictive analytics
Aging Assets (roads railways bridges and tunnels) are more used, creating a need for better
transportation linear asset management. IBM is solving this problem. If the maintenance team can
predict a failure before it occurs they can get the asset repaired and the operations team can work
to reroute passengers or freight if needed. Early information leads to better service, and will
reduce costs associated with in-service failures. This improved performance will result in lower fuel
costs and carbon emissions and can extend the useful life of the assets.
IBM’s view of Autonomous Vehicle
For decades, mobility industry was very structured and a tight ecosystem with clearly defined
boundaries – Private mode and Public mode. The Authorities regulated the modes in isolation based
on strictly defined boundaries and hence rules – consumers didn’t have much of a voice. But all of
that began to change with the growth of digital technologies. A new term called “Shared” mode is
challenging the traditional definitions of Private and Public modes of transportation. Increasing
need for intermodal integration has started challenging the very basis of this definition. The term
mobility is going beyond just moving people to destination to moving them most efficiently in
shortest possible time. Commuter experience is becoming the most important factor in the way
mobility modes are being planned for.
Based on their digital experiences with other industries, today’s consumers now expect seamless,
omni-channel and customized experiences, and they are increasingly willing to contribute to
product and services innovation. Consumers know how to get information online and circumvent
the standard processes that used to restrict their involvement with industry participants.
As personal mobility expectations grow, non-traditional enterprises are offering technologies to
help consumers with driving, including getting directions, dealing with traffic or parking, and

integrating with other forms of transportation. New business models such as car sharing even
threaten the need to own a vehicle.
Industry ecosystems continue to intersect and overlap. In the future, this disruption will affect
major industry processes as traditional roles change and industry borders fade. Self-driving cars
have been into fantasies for long. Since Google unveiled its driverless-car technology in 2010,
several car manufacturers including Volkswagen, Volvo, BMW and Ford etc have announced plans
to introduce autonomous or semi-autonomous vehicles. As the technology evolves, some countries
are testing the technology or are planning to do so.
Testing on self-driving vehicles have dimensions beyond technology. These cars will perhaps need
a new way of looking at the mobility industry and the regulations driving them. These vehicles also
open up possibilities of leveraging data and analytics to a completely different complexity levels to
improve productivity, safety and reliability of both fixed as well as flexible route transit. The
intermodal fixed route boundaries will diminish in a regime of customized, demand responsive
transit to achieve commuter experience. A network of shared vehicles will not only help address
“first mile, last mile” issues but will also seamlessly integrate with rest of the transport network
through connect vehicle infrastructure.
An Autonomous Mobility system will need to evolve into a Self-enabling vehicle in order to achieve
the above goals and benefits. The vehicle will be sophisticated enough to configure itself to its
occupants. It will be able to learn, heal, drive and socialize with other vehicles and its surrounding
environment.

Self-integrating. Like other smart devices, the vehicle will be an integrated component in the
Internet of Things (IoT).3 It will collect and use information from others concerning traffic,
mobility, weather and other events associated with moving around: details about driving
conditions, as well as sensor-based and location-based information for ancillary industries, such as
insurance and retail.
Self-configuring. Individual mobility personas will contain the necessary digital information about
an individual to provide the desired vehicle experience: for example, personal preferences on
configuring controls and seats, multi-media preferences, financial information for making purchases
from the vehicle or medical information about the driver or its usual occupants.
Vehicles will configure themselves using mobility personas. With permission, vehicles will access
additional personal information as required. For example, a driver with a heart condition could
authorize the monitoring of vital signs. If the vehicle senses a potential heart attack, the driver
would be alerted, the vehicle would automatically slow to park, and additional information about
his or her medical preferences could be released to appropriate health facilities.
Self-learning. Vehicles will have cognitive capabilities to learn the behaviors and choices of its
occupants, the vehicle itself and the surrounding environment to continually optimize and advise.
As the vehicle learns more about the driver and occupants, it will be able to expand its advice to
other mobility services options.

Self-healing. Vehicles will be able to fix and optimize themselves based on certain events or
situations without human intervention. Analytics capabilities will help vehicles identify and locate
issues, schedule fixes and even help other vehicles with similar problems with minimal impact to
the driver.
Self-driving. Vehicles will become highly automated with some areas of limited autonomous
function in controlled environments. The vehicles will be able to drive themselves based on a fixed
or dynamic route based in environmental insights like preference of its occupants, traffic
conditions, transit availability etc
Self-socializing. By 2025, 57 percent of interviewed executives say vehicles will connect with
other vehicles and the infrastructure around them to share information and solutions, and 64
percent of OEMs anticipate it. These vehicle social networks could extend beyond mobility as the
vehicle connects into the greater IoT and socializes with devices from other industries.
Consumer-driven mobility
The vehicle is just one component of the new customized mobility options that are enabled by
technology and demanded by consumers. Mobility includes products and services that enable
different ways for consumers to move from one point to another according to each individual’s
preferences and lifestyle.
Because consumer-driven mobility is not controlled by the auto industry, it offers tremendous
opportunities for new business models, providers, products and services that transcend the
traditional vehicle-centric focus. Sixty-nine percent of the executives cited such new services as a
top way to grow. OEMs control vehicle-centric services that drivers use during vehicle operation,
but other mobility services — including driver convenience and occupant experience — will see
intense competition from non-traditional industry participants.
Conclusion
Transportation has a major impact on the quality of life in a city, its environment and the economy.
Smarter transportation can help to make the city and region it is implemented in a better place to
live, generating more economic prosperity, and providing free flowing public transport which is
clean and easy to use. Citizens and transportation providers can use information from data to get
real time information based on predictions to avoid traffic jams and delays in the transportation
network. Cities come in all shapes and sizes. IBM can help leaders get started in any aspect they
choose, starting with a single service area that has been identified and prioritized, or wherever
their city has the most acute need. From automated tolling and real-time traffic prediction, to
congestion charging and intelligent route planning, IBM works to research, test and deploy new
traffic information management capabilities in cities around the world.

For more information
To learn more about Smarter Transportation, please contact your IBM representative or IBM
Business Partner, or visit: ibm.com/smartercities
Lewis Gaskell Jr – North American Transportation Leader
Lgaskell@us.ibm.com
i
China Daily Mail, "China: Thick smog blocked road, train, air traffic for two days", October 21, 2013,
http://chinadailymail.com/2013/10/21/china-thick-smog-blocked-road-train-air-traffic-for-two-days/
ii
World Health Organization, "Health effects of transport-related air pollution," 2005,
http://www.euro.who.int/__data/assets/pdf_file/0006/74715/E86650.pdf
iii
Smarter Cities: Infographic "Turning Big Data into Insight", http://www-
03.ibm.com/press/uk/en/pressrelease/42081.wss
iv
“2011 Urban Mobility Report.” The Texas Transportation Institute of Texas A&M University, February 5,
2013.
v
Transport: Investing in energy and resource efficiency, United Nations Environment Program,
2011. www.unep.org/greeneconomy/Portals/88/.../ger/GER_10_Transport.pdfCached
vi
IBM 2011 Commuter Pain Survey, http://www-03.ibm.com/press/us/en/presskit/35314.wss
vii
IHS Automotive, Emerging Technologies: Big Data in the Connected Car, Nov.19 2013,
http://press.ihs.com/press-release/country-industry-forecasting/big-data-drivers-seat-connected-car-
technological-advance

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