Artificial intelligence is disrupting and optimizing the future of transportation by making it safer, smarter, cleaner and more efficient. AI applications are being used in autonomous vehicles, traffic management systems, and predictive maintenance of transportation infrastructure. However, widespread adoption of AI in transportation faces challenges regarding privacy, security, and the ethical implications of autonomous systems. Considerable investment and regulatory frameworks will be needed to address these challenges and maximize the benefits of AI in developing sustainable transportation solutions.

1. ARTIFICIAL INTELLIGENCE (AI) ENABLED
TRANSPORTATION - DISRUPTING AND OPTIMIZING
THE FUTURE MOBILITY
Automotive & Transportation

2. Transport in developing or emerging markets often faces severe
challenges due to growing populations, urbanization, poor
infrastructure, and rising prosperity in some regions, increasing
cargo volumes, vehicle traffic, and pollution. Artificial intelligence
(AI) offers new solutions to these challenges by making market entry
more comfortable and allowing countries/geographies to reach
underserved populations, creating market opportunities and private
sector investment opportunities associated with them.
AI is changing the transport sector completely from helping cars,
trains, ships, and airplanes function autonomously, making traffic
flows smoother is already applied in numerous transport grounds.
Beyond making consumers' lives easier, it can help make all
transport modes safer, smarter, cleaner, and more efficient. AI-led
autonomous transport could reduce the human errors involved in
many traffic accidents and traffic congestions.
However, these opportunities come with real challenges, including
unintended consequences and misuses such as cyber-attacks and
biased transport decisions. There are also consequences for
employment and ethical questions regarding liability for
AI decisions in humans' place.
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3. Considering the geographies, the EU is taking initiation to adapt its regulatory framework to new developments to supports
innovations while at the same time ensuring respect for fundamental rights and values. The measures already taken include
general strategies on artificial intelligence (AI) & machine learning (ML) and rules that keep the technologies enabling its
transportation section application. Besides, the EU provides financial support, in particular for research.
"AI is Making Transport Safer, Cleaner, More Reliable, and Efficient in the Emerging Markets"
AI Enabled Transportation: Market Dynamics
According to the US Transportation Research Board, the developing AI applications in transportation planning are in
behavioral travel models, city infrastructure design and planning, and demand modeling for cargo and public transport. For
instance, AI-enhanced demand and forecast modeling in road freight transportation management aimed to enhance the
domestic public transportation infrastructure.
Although one restraint for adopting AI in transportation planning applications is that connected transportation
infrastructure will also raise safety concerns about individuals' privacy and data safety. Government and legal regulations
regarding these ethical considerations will dictate the pace of innovation and adoption in the global industry over the
predictable period.
Read More:-:https://www.pukkapartners.com/insight/artificial-intelligence-ai-enabled-transportation-disrupting-and-
optimizing-the-future-mobility

4. COVID-19 Impact on Transportation Sector
The COVID-19 pandemic has strongly hit global transportation sector. With ridership significantly down,
operators in developing cities will have to face difficult future viability questions. The appropriate response
must plan for contingencies, but never forget the value of shared transit in the process of economic
development.
Applications of artificial intelligence (AI), intelligent transportation system (ITS), and internet of things (IoT) can
tackle commuter safety issues of the transportation system. It can also reduce human-to-human transmission
and prevent cluster outbreaks in public transportation systems by monitoring adherence to COVID-specific
norms while improving efficiency and ease of monitoring.
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5. AI models instances into the transport sector
1. Artificial Neural Networks (ANNs)
Description: They are enthused by the neural networks found in the human brain. They use previous experience and data points
with varying weights to make decisions. It handles complex and challenging issues as they operate with large amounts of data
and detect non-linear relationships.
Uses: Automotive Global Position Systems (GPS) use ANN to determine the mode of transportation based on the information
collected from a GPS, accelerometer, and magnetometer. This algorithm is derived from several data points and factors.
Moreover, ANN models can also be used in public transportation to predict buses' arrival times at stop areas.
2. Artificial Immune System (AIS)
Description: This algorithm takes its inspiration from human biology; specifically, how human bodies react to disease-causing
agents known as antigens. AIS models feature extraction, pattern recognition, learning, and memory of human immune
systems.
Uses: AIS is useful for pattern recognition, anomaly detection, clustering, optimization, planning, and scheduling. Engineers have
used AIS to create a real-time regulation support system to help public transportation networks to find the most appropriate
solutions when the network experiences a disturbance.
Others include the Fuzzy Logic Model, Ant Colony Optimizer (ACO), and Bee Colony Optimization (BCO).

6. AI Enabled Transportation: Investment Opportunities
E-logistics is one of the most favorable areas for immediate investment opportunities that mainly involve AI applications in the
transportation sector. Poor roads, suboptimal truck utilization, unreliable tracking, routing, and a lack of transparency in cargo
movement hinder the global market's traditional logistics efficiency. Many new startups have been utilizing technology, including
AI, to solve the efficiency and reliability issues that the conventional logistics industry faces, introducing data analytics and
optimization to lower costs.
Known as e-logistics companies, the services these startups specialize in intend to fall into three categories:
• Transportation data enablers, which provide platform-as-a-service offerings to improve transportation/location services
• Long-haul/inter-city transportation, which usually aggregates truckers and shippers to facilitate the movement of goods across
cities through a digital platform
• Last-mile/intercity transportation, which aggregate local delivery drivers and SMEs/e-commerce companies for last-mile
delivery solutions within cities

7. AI Enabled Transportation: Current Applications
There are few instances of current applications of AI in the transportation industry below:
1. Public Passenger Transportation
Leveraging AI in transportation supports the sector by enhancing passenger safety, reducing carbon emissions, reducing traffic
congestion & accidents, and minimizing the overall financial expenses.
Companies - Olli by Local Motors
2. Autonomous Trucks
The transportation sector is primarily witnessing environmental challenges and stricter emission regulations from various
government regulatory bodies. According to the International Transport Forum (ITF) report, it claims that autonomous trucks will
save costs, lower emissions, and improve road safety compared to traditional trucking with human drivers.

8. Companies - Uber Advanced Technologies Group, TuSimple
3. AI Applications in Railway Cargo Transportation
Transportation has made a concerted effort toward applying AI in the transportation segment. AI brought 'intelligent'
locomotives that aimed to improve efficiency (which translates to rail transport solutions' economic benefit).
Each AI-based smart freight locomotives are equipped with sensors, including cameras that capture track, front and back, and
locomotive cab. The sensors' data is fed to machine-learned analytic applications that aggregate the data right there on the edge
gateway, enabling onboard real-time decision-making.
Companies - GE Transportation, Hitachi
Conclusion
AI holds the promise of dramatically increasing productivity and efficiency in several sectors, including transportation. AI is
already helping to make transportation safer, efficient, cleaner, and reliable. Some smart applications include drones for quick
life-saving medical deliveries, smart traffic systems that reduce congestion & emissions, and driverless vehicles. With great
potential to increase efficiency and sustainability, among other benefits, comes a host of socioeconomic, institutional, and
political challenges that must be addressed to ensure that countries and their citizens can all harness AI's power for economic
growth and shared prosperity in the global transportation sector.

9. How We Are :-
Pukka Partners provide customized intelligence solutions to C-
suite executives and functional growth leaders, along with other
key offerings including market & competitive intelligence,
management & strategy consulting, pre-investment advisory,
real-time business intelligence, and data analytics services
across sectors, globally and locally, on new & emerging
markets and disruptive technologies.
We offer advisory and actionable insights around public
policies, investment tracking along with the obstacles faced by
investors, innovation and strategy impact monitoring,
identification of industry potential, and technology mapping
through comprehensive and standardized research
methodology and tools. We deploy our solutions to solve
prioritized and critical business challenges by leveraging our in-
house expertise and continuous engagement with industry
thought leaders in the business ecosystem.
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