Technology Management

Assignment

AI in Transportation

Submitted to – Submitted by-

Prof. Vinayak Drave Group 3
Arushi Tewari
Kishlay Ashish
Shivani Khare
Introduction:
Artificial intelligence (AI) is in the spotlight as one of the emerging fields transforming the
transport sector. Since 1950s, AI has undergone a number of ups and downs, where optimistic
expectations were followed by bitter disillusion. In recent years, AI has made a lot of progress,
as machine learning techniques have been combined with technologies used for searching and
analysing the large quantities of data (otherwise known as big data and data mining) produced by
the development of the digital world. Other reasons for its successful growth include the
development of communications networks and the internet of things, as well as progress in
transport devices. Artificial Intelligence (AI) is a technology that fuels machines with human
intelligence – machines that have AI capabilities can automate manual tasks and learn on the go
just like humans. 

Such automation gets repetitive and time-consuming tasks under the AI-powered systems that
learn with time and can eventually carry out critical tasks and make decisions on their own. Such
unique potential drove the transportation businesses to start investing into AI technology to
improve revenue and stay ahead of their competitors. 

The future progress of AI in transport is expected to be even more spectacular, although there is
no agreement on the timing and exact nature of these developments. AI is helping to make all
transport modes safer, cleaner, smarter and more comfortable. AI in transportation can collect
traffic data to reduce congestion and improve the scheduling of public transport, Transport is
affected by traffic flow, AI will allow streamlined traffic patterns,  Smarter traffic light
algorithms & real-time tracking can control higher and lower traffic patterns effectively, This can
be applied to public transport for optimal scheduling & routing.
Revolutionary AI Applications In Transportation:

 Self-Driving Vehicles

 Traffic Detection (and Traffic Signs)

 Pedestrian Detection 

 Traffic Flow Analysis 

 Road Condition Monitoring 

 Automatic Traffic Incident Detection 

 Automated License Plate Recognition 

 Driver Monitoring

Some examples of AI in transportation:

1. Autonomous Vehicles- Autonomous vehicles are already a reality within transportation.

They represent the first step into a new future of autonomous transportation although it seemed
quite a futuristic idea in the past. A.I. uses its processing, control and optimization capabilities to
power these driverless vehicles. With autonomous vehicles, real-time data transmission and
processing is crucial and any confusion within these processes can cause fatal outcome.

2. Passenger Transportation- The safety of passengers, pedestrians and drivers has always
been a number one issue for the transportation industry. The use case of AI was the autopilot
system which is used in almost every commercial airplane today and it is a vital part of any air
travel nowadays.

3. Drone Taxis- Drones have already been used in delivery service systems but soon they
could be used as taxis as well. Unmanned aerial vehicles represent a one-of-the-kind solution in
fight against carbon emissions, traffic congestion or expensive infrastructure. Drones as taxis
will allow people to arrive at their destinations much faster while reducing their commuting time
to a minimum at the same time. Regarding urban areas, drone taxis can be a real deal for solving
the issues like urban planning and urban infrastructure development. 

Willingness is high:

Transportation, the industry that deals with the movement of commodities and passengers from
one place to another, has gone through several studies, researches, trials, and refinements to
reach where it is now. One of the major milestones in the history of transportation was the
steamboat in the year 1787. Prior to this, people relied on animal-drawn carts for their commute.
Thereafter, major breakthroughs that led to the growth of the transportation industry were the
invention of bicycles (early 19th century), motor cars (in the 1890s), trains (19th century), and
aircrafts (1903). Today, the transportation sector has evolved to a level where vehicles can
navigate and move without any human assistance. Technological advancements have helped the
transportation sector progress in its journey of innovation and evolution. One such new-age
technology that has contributed to the sector is AI. Leveraging AI in transportation helps the
sector increase passenger safety, reduce traffic congestion and accidents, lessen carbon
emissions, and also minimize the overall financial expenses.

Driverless, AI-enabled cars may be just around the corner, promising to alter both business
models and customer behavior. Taxi and ride-sharing businesses must evolve to avoid being
marginalized by AI-enabled transportation models; demand for automobile insurance (from
individual customers) and breathalyzers (fewer people will drive, especially after drinking) will
likely diminish, whereas demand for security systems that protect cars from being hacked will
increase. Driverless vehicles could also impact the attractiveness of real estate, because (1)
driverless cars can move at faster speeds, and so commute times will reduce, and (2) commute
times will be more productive for passengers, who can safely work while being driven to their
destination. As such, far flung suburbs may become more attractive, vis-à-vis the case today.

AI can optimize transportation:

Ensure safety for all road users - The safety of passengers, pedestrians, and drivers has always
been the number one concern for the transportation industry. Taking advantage of AI models
does far more than decrease the number of human errors; transportation analytics assists in
minimizing effects of driving hazards in crowded urban areas, while also monitoring safety
regulation compliance and vehicle maintenance reports.

Plan and schedule efficiently - The problems of intermodal logistics are always relevant to
businesses with a sizable fleet, complex infrastructures, and numerous links in a cooperation
chain. State-of-the-art modeling technology can address these problems and improve operational
efficiency: optimal route scheduling with minimum wait times, traffic detection to adjust the
route in real time, on-time regulation compliance, etc. Using data analytics in logistics provides a
data-driven view on routes and driver behavior, upgrades the transportation planning process,
saves resources, and increases safety.

Predict and monitor traffic - Traffic, the prime transportation disruptor, causes delays,
accidents, and wasted fuel. Prediction techniques, however, allow you to perform traffic
condition forecasting using traffic monitoring data, information about sporting events or
construction in the city, and even automatically calculate alternative routes.

Readiness is low:

There will be lot of difficulties to adapt new thing that could be costly. There are still lot of
surveys and debates going on regarding safety, capacity issues, pollution and reliability. AI has
the potential to make traffic more efficient, ease traffic congestion, free driver's
time, make parking easier, and encourage car- and ridesharing. However, lower
transport costs and freeing the driver from driving
tasks could also lead to more people choosing a car as a transport mode (instead of
public transport),
and subsequently increase congestion and air pollution. In addition, cybersecurity
and data privacy are also of particular importance in the development of
AI in automated vehicles. Namely, AI-based automated vehicles require access to a
lot of data that
is often sensitive or protected. If third parties manage to access automated vehicle
data without
control, the safety of the vehicle, its occupants and other road users is endangered.

AI also
creates new risky situations, as accidents with automated vehicles have
demonstrated. In an interim
period, when vehicles are increasingly automated but not yet completely
autonomous, drivers
might be distracted and pay less attention to the road. When a situation arises
where the human
needs to intervene, a distracted driver can be slow to react. It is simply difficult for
humans to
maintain effective visual attention during a longer period of automated driving. At
present,
therefore, more progress is needed to ensure that fully automated vehicles can
safely interact with
other road users, perform well under all weather and road conditions, correctly
recognise obstacles
and understand the environment.

AI also raises various ethical issues. When faced with life-versus-life situations,
the question as to
how an AI algorithm in a fully automated vehicle should decide how the vehicle
reacts divides
opinion. In 2016, a Mercedes-Benz executive said: 'If you know you can save at
least one person, at
least save that one. Save the one in the car'. The respondents to a survey published
in Science the
same year, on the other hand, wanted others to drive vehicles that would sacrifice
their passengers
for the greater good, whilst simultaneously preferring to drive vehicles that protect
their passengers
(especially family members) at all costs themselves. Where only one passenger
would be killed to
save 10 pedestrians, most respondents preferred the vehicle to save the pedestrians.
Use of AI also
raises the question as to who should make such a decision. Should AI algorithms
automate ethical
decision-making independently, or should all vehicles have the same ethical
settings, or could
people who buy a fully automated vehicle could determine such settings
themselves?
In case an accident arises, liability is another challenge that needs to be addressed.
A clear boundary
of liability must be defined for the different levels of automation, so that it is
possible to identify who
is actually responsible for the accident. This might require changes in legislation,
traffic rules and
insurance policies. The current rules are based on the assumption that when the
vehicle is used on
the road, there is a human driver on board, whereas automation technology is
intended to partially
or fully replace the driver, thus shifting the responsibility.

Conclusion:

Artificial Intelligence (AI) has become more than just a visionary idea – it is a part of our daily
lives and we use it each day without even noticing. AI can be found in our mobile apps, in social
media feeds or in the way how Grammarly checks our grammar mistakes.

Transportation industry has already used some AI solutions for a while but it won’t be long until
the increase of AI within transportation and logistics. As A.I. is getting more subtle with time, it
is a matter of time when we will get to see the exciting future driven by AI!