The Impact of AI
on Transportation and Mobility

Report: The Impact of AI on Transportation and Mobility

Contents
Executive summary

02

Overview031	The convergence of AI and digital infrastructure

2

3

4

5

1.1	Data generation as an enabler for AI

05

1.2

07

Computer power as an enabler for AI

Technology with human focus

09

2.1

Current approaches

10

2.2

Future approaches

122.3	Navigating complexity and evolving current practices15

The main players

16

3.1

Thinkers

16

3.2

Planners

16

3.3

Users

17

3.4

Deployers

17

Building blocks for responsible AI development 

18

4.1

Macro level risk assessments

19

4.2

Technical level risk assessments

20

Outcome-driven tech

21

5.1

Safety22

5.2

Equity23

5.3

Efficiency and sustainability

24

5.4

Multimodal applications25

5.5	Use cases of applications targeting multiple outcomes 

6	The main enabler: implications of regulatory approaches

7

04

26

27

6.1

US approach

27

6.2

EU approach

29

6.3

UK approach30

Conclusion

31

01© Cambridge Consultants & ITS America 2023

Report: The Impact of AI on Transportation and Mobility

Executive summary
This report is a collaboration between the Intelligent Transportation Society ofAmerica (ITS America) and Cambridge Consultants. It explores how artificial
intelligence (AI) can fundamentally change the capabilities of the Americantransportation sector, while outlining key challenges and solutions associatedwith the deployment of transportation AI technology. This report proposes key
principles of responsible AI utilization, identifies key stakeholders involved with AI
implementation, and summarizes the recent regulatory environment for AI within the
United States and among key global partners. Ultimately, it is expected to serve as aroadmap for transportation and technology stakeholders considering deploying AI.
This report also highlights numerous transportation-specificopportunities associated with AI utilization, including waysthat AI can bolster shared transportation goals like safety,
efficiency, sustainability, and equity. It contains significant
discussion on how AI interacts with transportation data,
both in terms of the possibilities associated with AI’s realtime analysis of transportation trends as well as challenges
associated with dataset limitations. It outlines key
considerations for cybersecurity vulnerabilities associatedwith AI deployment, as well as ways that AI can help bolster
longstanding security methods for current transportation
infrastructure. It details how the various categories of
AI stakeholders interact, including those we classify as“thinkers,” “planners,” “deployers,” and “users.” The report
also discusses how those stakeholders can navigate an
incomplete regulatory AI environment, accessing newmarket opportunities and positively impacting society while
making meaningful progress towards innovations that will
help achieve key transportation goals.

At its core, it is meant to articulate a bold, optimistic vision
for a transportation sector empowered by human-centered
AI implementation. We believe that technology cannot be
deployed for technology’s sake, and that transportation
innovations must be designed to make meaningfulimprovements towards real challenges faced by alltransportation users, including those with the fewest mobility
options. We believe that this report offers actionableproposals as to how AI implementation can embrace thatphilosophy, and thereby represent a foundational building
block on which the next generation of transportation
progress will be delivered for all.

02
© Cambridge Consultants & ITS America 2023

Report: The Impact of AI on Transportation and Mobility

Overview
AI is poised to become a critical enabler of many of theshared goals within the transportation sector. Foremost ofthese goals is improving transportation safety, reducing
or eliminating the 43,000 fatalities that occur on American
roads on an annual basis. Additionally, AI can provide
benefits related to efficiency, equity, and sustainability.
Stakeholders have a unique opportunity to help shape thegrowth of AI transportation applications to achieve thesegoals – while creating new markets and business models
within the sector and boosting economic growth.

are not easy to make tangible, but these factors are vitaland must not be ignored. By bolstering our understandingof AI decision making, we can iterate and improve on how
these decisions are made. With transparency, we caneliminate harmful biases and incorrect answers as we forge
an AI-enabled transportation system that provides better
services for all.
Further to these considerations, a key element for therealization of AI transportation solutions is the vast
proliferation of data generated by people and machines.
Fifteen billion devices are connected to the internet in 2023,
a number projected to almost double by 2030. Fully utilizing
the data provided by connected smart infrastructuredevices demands increasingly advanced connectivity, data
management and processing, as well as a shift in thinking
for human operators. AI can process and analyze that data
at a scale and speed that would be impossible without thistechnology, and as such it becomes an essential tool withwhich human operators work and interact.

Going forward, there are three fundamental considerations
to assure the success of AI within transportation:
First, it is vital to recognize the complex, interdependent
moving parts of transportation systems in both the public
and private sectors. These networks are multifaceted –incorporating road, rail, air and maritime travel – and areincreasingly interdependent with energy, communicationand industrial networks. The improvement and developmentof these networks must bear this complexity and mutuality inmind, especially in the context of AI.

Another key capability of AI is how it can transform
unstructured data into relevant, useful insights. Whiletransportation data from an ever-increasing rangeof sources is becoming more readily available, manytransportation agencies lack the ability to process
meaningful conclusions from raw data. AI is poised to serveas that medium between raw data and insights, weaving
together a narrative from various datasets by rapidly
categorizing and collating data trends at the granularlevel. AI can drive new opportunities for both technologicaldevelopment and business innovation while accelerating
digital transformation across the transportation ecosystem.

The connections are everywhere. Improving traffic
management efficiency will impact not just road but alsorail transit; efforts to reduce emissions will have far reaching
implications across the country. An assured, bird’s-eye viewof the entire ecosystem is essential if we are to achieve
substantial network-wide improvements.
Second, the future of AI-enabled transportation must
be viewed through a holistic, human lens. It is imperativethat AI development prioritizes the human user. This is
an opportunity to enhance the safety, efficiency, equity
and sustainability of our transportation system – and a
responsible approach to AI will do just that.

As for challenges, the transportation industry mustgrapple with how to link technologies and insights within
a complex ecosystem. Solving this entails an assessmentof the interoperability of data and technologies, as well asprivacy, cybersecurity, network capacity, storage capacity
and information latency. We must also consider AI risks andmitigation tactics.

Development that reflects this approach will create
services that respond to the full breadth of human needs.
Rather than retrofitting accessibility into establishedtransportation, these services should be carefully conceived
by paying attention to a diversity of voices. This report will
showcase how we can develop AI tools that reflect a variety
of interests.

If the AI transformation is to benefit all travelers, a horizontal
view across public and private sectors is crucial. Holistic
thinking will give an overarching perspective of opportunities
and challenges, while flexibility and cooperation will beneeded to achieve a multimodal, integrated and intelligent
transportation system.

Third, processes and outputs must be transparent so thathumans can understand them and be able to correct errors
and malfunctions. The transparency and ‘explainability’ of AI

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Report: The Impact of AI on Transportation and Mobility

1	The convergence of AI and digital infrastructure
The digital infrastructure transformation is being enabled
by significant advances in sensing, computing andwireless communication technology. To maximize digital
infrastructure’s potential, we must recognize the complex,
interdependent moving parts of transportation systems
in the public and private sectors. Innovation in one area,such as autonomous vehicles, must be accompanied bytimely advances elsewhere, such as in communication
systems, compute power and data processing. Systems level
coordination will accelerate a scaled digital transformation
founded on AI solutions.

AI’s development has been accelerated by the confluence
and availability of both data and more complex algorithms
enabled by greater computational power. The complex
nature of AI algorithms requires significant computational
resources to train and execute tasks effectively. Morepowerful hardware and infrastructure enable AI systems
to handle larger and more complex datasets, leading to
improved performance and scalability. Accessibility ofhigh-quality data combined with ample computational
resources allows AI systems to learn, adapt and perform at
a higher level. Here we describe these enablers and pinpoint
important key challenges that need to be incorporated into
AI solutions.

04© Cambridge Consultants & ITS America 2023

Report: The Impact of AI on Transportation and Mobility

1.1	Data generation as an enabler for AI
The new technologies that harmonize intelligent
transportation and digital infrastructure implementation
generate data at an unprecedented scale. These data
sources are a crucial enabler for the further innovationnecessary to achieve the US Department of Transportation’s
safety, equity and sustainability goals, as well as
commercial and efficiency benefits for end users. Data will

provide insight into virtually all stages of the transportation
value chains and will remain a fundamental building block of
the future landscape.
AI algorithms are data hungry, but at the same timethese data need to be clearly structured, managed andprocessed to be used effectively.

01

Structuring

Data needs to be structured, organized and stored in amanner that enables efficient access and modification.
This in turn enables the establishment of interrelationships
and the application of different operations and functions tothese data. The principle of ‘build once, use many’ is pivotal
for maximizing the value of data.

02

Processing

Data processing techniques also have evolution
requirements. This evolution is already advancing at
the edge and in the cloud, but taking full advantage ofopportunities requires coordination between different
sectors for capture, processing, storage and dataexchanges. Going forward there will be a steady increase in
the embedded systems that comprise sensor data, storage,
compute and advanced AI capabilities in edge devices.

03

Skills

New skills will be necessary to address these challenges
and holistically incorporate the data driven insights
delivered by digital infrastructure. These skills requirements
will include cloud infrastructure expertise, data analytics,
data architecture, software engineering, DevOps engineers
and AI/ML engineers, designers and product managers
among others.

04

Management

Advances in cloud optimization and orchestration areneeded so that data storage and management can be
expanded at less cost, while being more sustainable andenabling faster decision making.

05© Cambridge Consultants & ITS America 2023

Report: The Impact of AI on Transportation and Mobility

Challenges
AI algorithms need large amounts of data for training andtesting. The availability of data created by new sensors
and communication devices has provided a huge numberof datasets that, when properly structured, managed andprocessed, can be used to train and test new algorithms
and create AI solutions.

Efforts to ensure that enough high-quality data is being
utilized by AI algorithms should remain a top priority, as
this variable has a direct impact on the effectiveness of
AI solutions. Also, contractual agreements should takenote of the requirement to keep AI systems up to date by
retraining over their lifetime – or at least include a supplierrequirement to monitor and declare when datasets are
becoming obsolete so they can be safely retired. Throughthis coordination, intelligent transportation systems (ITS) caneffectively take advantage of scalable solutions.

There are, however, key challenges that need to beaddressed so these data can really facilitate AI solutionsat scale.

01

03

Data updates

A serious limitation of extensively trained AI systems is‘dataset shift’ – the fact that real-world conditions changeover time, gradually taking an AI system outside its training
data distribution. In practice this means the decisions of a
deployed AI system are guaranteed to become graduallyworse (less performant, or potentially less safe), until it isretrained with up-to-date data. This known issue presents
a particular risk in public/private sector procurement,
where, without attention, a system might be procured asa ‘once only’ deliverable with no capacity to provide
ongoing monitoring of dataset shift and AI retraining
when necessary.
02

Data representativeness

It is still prohibitively expensive to collect sufficient realworld data to train a transportation AI system across all thepossible inputs that might be encountered. Technologies
are emerging to meet this challenge and cover all possible
situations (different weather conditions, traffic conditions
and emergency situations, for example). Either synthetic
data is required, or extremely good monitoring of whether
an AI system is going ‘out of training distribution’ so thatalternative mitigations can be deployed. While synthetic
data may suffer from its own limitations in modeling the realworld, emerging ‘equivalence’ techniques provide a solution
to measure error, monitor risk, and iteratively improvesynthetic data quality until acceptable.

Data availability and sharing

Advances in cloud optimization and orchestration areneeded so that data storage and management can be
expanded at less cost, while being more sustainable andenabling faster decision making.

06© Cambridge Consultants & ITS America 2023

Report: The Impact of AI on Transportation and Mobility

1.2 Computer power as an enabler for AI
Another critical enabler for AI solutions has been theevolution of communications networks, computer power,
and cloud computing. The largest models in the early yearsof deep learning were devoted to image classification. Here,researchers quickly realized that increasing computing
power reliably led to better performance. After image
recognition systems began to surpass human performance,

cloud services were developed to offer economies of scaleby sharing maintenance personnel, building space, coolingand other operational necessities for data storage solutions
across many projects and sectors.
Here are some of the key computational elements of AI.

01

Compute requirements

Deep learning is computationally expensive by design.
Recent trends have meant that increasing scale of dataand processing needs is key to accuracy and performancein training deep learning models. This has driven an
exponentially growing demand for computing power.

02

Capacity

Usually, large AI models are trained using a cluster of manychips known as an AI supercomputer. Supercomputers are
hosted in data centers, which provide the infrastructure
to keep the hardware running. Data centers as a whole –including the connected chips within them – represent the
infrastructure layer of AI compute.

03

Concentration

Large data centers enable cloud computing, a market thatis well known to exhibit a high degree of concentration.
More than 30% of the world’s data centers are located in the
United States. These U.S. data centers are located in areas
with abundant electricity demands, large amounts of waterfor cooling, affordable real estate, tax incentives and awayfrom regions that are prone to natural disasters. They arealso concentrated in the hands of very few companies.

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Report: The Impact of AI on Transportation and Mobility

Challenges

01

03

Sustainability

An interesting development incompute power is projects that arestarting to measure and calculate
compute expenditure in terms oftime and energy necessary to run AI
algorithms and incorporating theminto carbon emission calculations. This
will likely become best practice. It will
be invaluable to have a clear pictureof the impact that AI training andsystem use will have on the planet.

02

EnergyAI systems perform transactions
between memory and processors,and each of these transactions
requires energy. As AI becomes moreelaborate and data-intensive, there isa need for more memory storage andmore energy. The power required to
run supercomputers in data centersalready accounts for about two
percent of the nation’s electricity
use and huge water quantities are
required for cooling.

Reducing compute

The result of this growth incomputation, along with theavailability of massive data sets
and better algorithms, has yieldedmuch AI progress in very little time.
It is difficult to say if the same paceof computation growth will bemaintained. Large-scale models
require increasingly more compute
power to train, and if computation
does not continue to ramp up,
it could slow progress. For some
applications, AI at the edge canreduce the network traffic, latency andbandwidth consumption necessary
to run algorithms and accelerate
speed of response which can be
extremely powerful in real timetransport applications. AI at the edge
has the benefits of lower computing
and data security by keeping dataprocessing locally. However, the AI
model still needs to be trained with asupercomputer and considerationsof raw data loss or model monitoringneed to be considered. Hybrid models
of cloud and edge will be the key.

Key points

1
2

3

We are witnessing a unique moment in theconvergence of the technologies that willrealize the potential benefits of AI solutions fortransportation systems.

4

Data availably – provided by digitaltransportation infrastructure – compute power
and investment in algorithm development
have made the rapid evolution of new AI
solutions possible.

5

Data needs to be abundant, of high quality,
and able to help develop scalable AI solutions.

08© Cambridge Consultants & ITS America 2023

The continuous training of AI for transportation
applications needs to exploit new techniquessuch as synthetic data that can capture all
situations of real-world operations.
AI development and use must be sustainable tocreate future-proof opportunities.

Report: The Impact of AI on Transportation and Mobility

2 Technology with human focus
The development of AI algorithms over the past decade has predominantly had a technological focus. In general terms,
AI encompasses different ways of ingesting data and producing outcomes depending on how it is trained.

Supervised learning

Unsupervised learning

Reinforcement learning

Symbolic learning

The data used for training
the algorithm are labelled
so it learns these tothen classify or predict
outcomes on unseen data.

The algorithm is trainedwith unlabelled data. The
algorithm groups the datain groups or clusters basedon common features

The algorithm is trainedon “trial and error” cycles
based on rewards andpenalties to learn how to
perform a task

The learning is based onlearning representation
and reasoning, withassociations and rules andan understanding of cause
and effect(i.e., an algorithmm getstrained in thousands ofpictures of cars to learn thepatterns that classify animage as a car)(i.e., the detection of traffic
data trends)(i.e., a self-parking car)(i.e., collision avoidance)

Although the technological advances of AI algorithms are
immense, difficulty arises in applying them to operational
environments when humans try to understand and work withthem. This is mainly because most have been developed
from a technological point of view that doesn’t take humancontext and practical useability into account.

of future intelligent transportation systems. It will ensurethat the needs of individuals, groups and society as whole
are prioritized to create safety, efficiency, equity andsustainability.
In the next section, we explore how we might utilize current
and future trends to ensure human augmentation, alongwith human cognition and agency. We include current
approaches within transportation as well as developments
to consider as AI continues to evolve.

A new era of research is starting to question whether
algorithms can, from the moment of conception, augment,
team with, and assist humans to perform tasks. We believethat it is vital for such human-focused AI to be at the core09

© Cambridge Consultants & ITS America 2023

Report: The Impact of AI on Transportation and Mobility

2.1 Current approaches
AI transportation applications are being implemented in avariety of ways and are driven by advances in particularalgorithms and supervised learning techniques. They include
deep neural networks for prediction tasks and reinforcement
learning techniques for training autonomous vehicles.

Implementation has focused on very particular areas,such as forecasting and planning capabilities, transit andautonomous vehicles. The reason for this is a mixture of
specific, ready-to-use algorithms at scale, integration withlocation and communication systems and investment.

01

Transportation management systems

AI can improve planning, forecasting and managing acrossthe transportation ecosystem. These applications areimportant to mitigate the continuously rising demand withlimited infrastructure supply. This includes the better useof accurate prediction and detection models aiming toforecast traffic volume, traffic conditions, and incidents. For
example, data driven AI in the current form has the ability tosolve problems in real time transportation such as managing
design, operation, time schedule and administration oflogistical systems and freight transportation.

CO2

02

Applications of AI

AI can be applied to improve public transportation withsmart transit solutions. These benefits can be derived
through vehicle operation information transmitted to
end users and control centres. This real time application
is facilitated by communications networks, vehicle toinfrastructure communication and data-driven algorithms
for route and operations by using telematic data for further
optimization of transit performance.

03

Other data-driven applications

Other data driven applications include the identificationof polluting particles and carbon emission hot spots(sustainability), driver behavior monitoring (with a focus on
user safety), control systems (traffic lights for traffic fluidity
and emergency services prioritization) and the predictive
maintenance of infrastructure and vehicles.

04

Connected and autonomous vehicles

Connected vehicles and infrastructure can enhance
efficiency by, for example, routing and rerouting vehicles.
This can reduce the number of traffic jams, facilitatedby vehicle location and route data capture. Additionally,
the capabilities of autonomous vehicles are consistently
advanced by manufacturers employing detect and avoid
improvements using reinforcement learning techniques.

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Report: The Impact of AI on Transportation and Mobility

There are, however, many innovations in AI algorithms research and concept development that surpass many of the above
applications. But the current gap between AI research and implementation is still vast, including for the following reasons:Black box algorithms

Lack of a system level view

Many of the AI algorithms are created within a research anddevelopment technological vacuum, either within academia
or within industry, without considering how a human is going
to be able to understand and use them. They are considereda ‘black box’ that is difficult to understand and explaineven by the developers themselves. The more complex thealgorithms and the more safety critical the application, themore necessary it is to have transparency and explanations
for the outcomes.

It is not clear how these AI solutions fit into an intelligent
transportation system and what interactions, consequences
and trade-offs the implementation will entail if all these
crucial elements are not properly addressed when thetechnology is developed. It is only with a system-wide
approach that sustainability goals can be addressed, andonly with understanding of interactions and cooperation
between the different parts of a system that we can take fulladvantage of AI.

Limited solutions yield limited value

Development to implementation gap

Many applications only deal with some of the users (drivers,
for example), only focusing on certain aspects of situationalawareness and not providing solutions for all. Trying to
implement only part of the solution means that it is moredifficult to realize value and might create further disparities
and inequalities (i.e., some users are provided with lots oftechnological advances while others are not).

Transparency and explainability are a must within theimplementation context, especially in safety criticalsectors such as transportation. However, research anddevelopment of AI does not tend to focus on the actualinterpretation of the AI, risks of implementation, robustness,
resilience, safety, security, and what monitoring needs
to be in place. Potential users are left to assess thesethemselves – which makes the transition from development
to implementation more challenging.

Key points

1
2
3

Bridging the gap between advanced research and implementation
is not easy and explains the slow adoption of certain advances inreal-life operations.

To bridge this gap, emphasis needs to be put on developing
AI solutions with a human-focused approach that includes AI
trustworthiness and assurance, in order to take full advantage of
new developments.
This is essential to move intelligent transportation forward. AI canhelp strategically and with targeted improvements to enhance theevolution of intelligent and complex transportation systems so thathumans benefit in a safe, secure and sustainable way.

11
© Cambridge Consultants & ITS America 2023

Report: The Impact of AI on Transportation and Mobility

2.2 Future approaches
Having learned from the current implementation challenges we’ve outlined, future AI evolution for transportation systems
must address human priorities. Here are some of the potential areas of attention.

Understanding change ofroles for workforce support

Understanding algorithms
for trustworthiness

Human focused AI
for Transportation

Understanding complex
systems for efficiency andsustainability
Understanding
potential threats of newtechnologies for security

Understanding human-AI
teaming and augmentation

Understanding diversity
needs for equity

Understanding algorithms for trustworthiness
While data-driven-based solutions are very effective inmany applications, there are still some open research issues
that limit their deployment in real-world scenarios. One suchchallenge relates to the methods with which AI learns fromexisting data sources. AI applications are typically trainedon existing datasets through a process known as supervised
learning. In supervised learning, AI algorithms are fed large
amounts of labeled data, and they learn to make predictions
or decisions based on that data. This training process
helps AI models recognize patterns, make predictions, andimprove performance over time. However, there are some
limitations to this approach. The quality of existing datasets
can heavily influence the performance and accuracy of AI
models. Biased or incomplete datasets can result in biased
or inaccurate AI predictions, which can have real-worldconsequences. Additionally, there are concerns about thetransparency of what AI is learning from existing datasets.
It is often difficult to interpret how AI models make decisions

or what features they are using to make predictions, which
can make it challenging to identify and rectify any biases
or errors in the training data. This is particularly relevant
when considering transportation data, which, given too
narrow of a dataset, might lead AI analysis to conclude thattransportation solutions specific to one community can beuniversally applied, neglecting to consider the diversity oftransportation challenges between communities.
To address these limitations, researchers are exploring ways
to improve the quality and diversity of training datasets, aswell as developing methods for ensuring the transparency
and interpretability of AI models. This includes techniquessuch as data augmentation, algorithmic fairness, andexplainable AI, which aim to create more reliable, unbiased,
and transparent AI systems. By addressing these limitations,
we can work towards building AI systems that are moreaccurate, reliable, and ethical.

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Report: The Impact of AI on Transportation and Mobility

Understanding complex systems interactions for safety
efficiency and sustainability

Understanding future cybersecurity threats when using AI

Intelligent transportation systems are complex in nature,
formed by different elements (such as digital infrastructure,physical infrastructure, modes of transportation,transportation users) that interact with and that impact
each other. Cooperation between these systems can befacilitated by AI models that discover and deliver insights
about the interaction between the different components,
as well as identify trends and gaps that in turn canbe used to achieve safety and sustainability goals. As
transportation elements continue to become more deeply
connected and interdependent, AI is poised to serve as avital real-time mediator and coordinator between disparatetransportation assets, modes, and operations.

AI-related transportation assets rely on various
interconnected systems, such as sensors, communications
methods, and data processing systems. This greater
degree of connectivity can create additional opportunities
for hostile actors to access critical transportation
infrastructure, threatening to cause significant disruption
to the traveling public.
To begin to address these cybersecurity concerns, it is
crucial to work to further identify potential vulnerabilities
in AI-equipped transportation infrastructure, as well as todevelop additional comprehensive techniques capableof detecting unauthorized incursions. Additionally, we
recommend that policymakers dedicate additional support
for building further professional cybersecurity capacity
within the transportation sector, in order that transportation
agencies have a sufficient workforce capable of preventing
and responding to cybersecurity incidents. Further,cybersecurity considerations should remain a top priority
during the design, development, and deployment of AI
tools. To accomplish this, robust cybersecurity measures
should be incentivized through the procurement process bytransportation practitioners in the market for AI solutions.

For example, AI can a) make use of the data capture andexchanges to understand trends hot spots, b) enable
scenario planning through simulations and real andsynthetic data to understand interactions and trade-offs
and c) orchestrate optimal collaboration and exchanges
between the different components of the complex system tomeet defined goals on safety, sustainability and equity.
As the transportationt sector increasingly utilizes AI
technology, the cybersecurity risks that they face tend to
amplify as well, due to the interconnected and constantly
evolving nature of AI-enabled vehicles and infrastructure.

Finally, while AI can be the source of some cybersecurity
concerns, it is simultaneously poised for use as a tool
to detect and mitigate cyber threats through predictive
analytics of potential vulnerabilities, continuous monitoringof transportation assets, and through the enabling of
automated responses to ongoing hostile activity. Ultimately,while AI introduces additional security variables into
the transportation sector, proper management of thetechnology can simultaneously manage the risks associatedwith AI deployment while maximizing its security benefits.

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Report: The Impact of AI on Transportation and Mobility

Understanding diversity needs for equity

Understanding Human-AI teaming and augmentation

In order for AI to reach its potential as a transformational
force multiplier in the transportation sector, it must be
developed in a way that is representative of the diverse
perspectives and experiences of the people who will be
impacted by these technologies. While AI derived frominsufficiently diverse datasets can threated to reinforce
existing inequalities within the transportation system, diverse
datasets can provide additional insight into innovative ways
to solve specific transportation challenges, broadening
the scope of what the AI-based solutions can accomplish.
The gulf among transportation characteristics for urban,
suburban, or rural communities, as well as wealthy or
underserved communities, should be reflected in thedevelopment of any AI system that will be contributing tothese communities’ transportation planning. Instead ofdesigning for AI-based solutions to focus on outcomes forthe average user, AI systems should be built specificallyto accommodate successful transportation outcomesin a variety of situations and to a variety of users – fromvulnerable road users to the disabled community, tocommunities with limited transportation options.

As we consider the potential trajectory of AI operations within
the transportation sector, it is clear that the greater adoptionof AI will improve the capabilities of transportation operators
to garner insights from various sources of transportation
data, but coordinated effort will be required to ensurethat the workforce is positioned to fully utilize these tools.
Specifically, there is a clear need for a strategy to upskill
the workforce that uses these algorithms. Organizations
cannot rely solely on recruiting, instead they must close thetalent gap by embracing a culture of lifelong learning andcontinuous investment in upskilling. Human-machine teaming
and human augmentation will not happen by default.
From the outset, AI design must consider the aim of tasks
and the type of human interaction involved. It needs toincorporate team structure and team elements that include
AI and human interactions and roles, thereby creating
hybrid intelligent systems.

Key points

1

Any look into the future of transportation andAI solutions needs to be through a holistic
human-focused lens.

2

Humans must understand the logic behindthe outcomes and functioning of algorithms inorder to use and trust them.

3

Humans need to understand the complexityof ITS and Cooperative ITS to make better
decisions – and AI can help them do this.

4
5
6

New cyber risks must be understood andprepared for to protect transportation systems
from attacks.

Only with inclusive design will equity beachieved.

The skills gap needs to be addressed now sothat there is a reskilled and trained workforce
to work with AI systems in the future.

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Report: The Impact of AI on Transportation and Mobility

2.3	Navigating complexity andevolving current practices
As detailed in the “main players” section of this paper,significant progress remains to be completed before afirm, overarching set of standards and regulations will befinalized for AI developers, deployers, and operators toadhere to. This regulatory vacuum represents a challenge
for AI stakeholders, as the uncertainty inherent in the
future regulatory conditions that AI will be operating
under increases the relative complexity associated with
AI deployment in the short-term. To help reduce andnavigate this complexity, we recommend that a maturity
roadmap should be developed to help bridge the gap
between AI technology and AI standards – one that canhelp prospective AI deployers gauge the readiness of both?

the market, the regulatory environment, and their own
particular solution in order to chart an appropriate path to
deployment. This would ideally couple short-term steps anddeterminations with a long-term vision for the particular AI
application in question. This maturity roadmap could bebased on a methodology developed by Carnegie Mellon
University 1 for maturity models, and it would definitely
aid the thinking process behind the deployment of new
solutions. The questions below do not aim to be exhaustive,
but just serve as an example. They would have to be
expanded and developed to reflect the progression andcapabilities within a maturity roadmap.

What is thechallenge tosolve?
What/who areimpacted?

?

Is AI the righttechnology
to solve thischallenge?

yes

?

No

Do you know
where does it
fit within yourAI maturity
model?

Find anothermore suitable
method!

yes?

Would youdevelop the AI
in house?

?

yes

yes

?

Do you haveenough skills todevelop it?

?

yes

?

Do you haveresources
and skills tounderstandand use it?
yes / noDo you haveenough data todevelop it?

yes?

Do youunderstandsustainability
impacts?
yes / noWould you buy
a solution?

?

Do you havegovernance inplace?

?

yes / no?

Do you data,skills, andresource tomaintain it andmonitor it?

Do you haveenough skills
to understandand use it?

?

yes

?

yes / noDo you havegovernance inplace?

Is the AI
solutiontransferable
and suitable?

?

yes

?

yes / noDo youunderstand thedata used fortraining?
yes / noWould you haveto maintain thesolution?

yes?

Do youunderstandsustainability
impacts?
yes / no?

Do you havedata andresources tomaintain it andmonitor it?
yes / no1 Reference: Paulk, M. C., Curtis, B., Chrissis, M. B., and Weber, C. V. 1993. “The Capability Maturity Model for Software,” Software engineering project management (10), pp. 1-26

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3 The main players
If we understand the universe of stakeholders involved in AIenabled transportation systems, we can accommodate theneeds of the most important groups and ensure successful
system deployment that meets USDOT transportation goals.

infrastructure. This mapping (outlining the relationships
between end users, digital infrastructure interfaces, stateand local public entities and enabling organizations) is worth
referencing as a similar high-level baseline for understanding
the main players in the AI transportation space.

In our previous Digital Strategy Infrastructure Report(published September 2023) we outlined a user-centricstakeholder map (Fig. 4, page 24) detailing the groups mostdeeply involved or impacted by transportation digital

Another useful, and simpler, perspective is to consider theneeds of groups as categorized here.

 Philosophers / Social
scientists / Ethicists

 AI experts
 Transport Industry
experts

Thinkers

Planners

 Civil societies
 Research bodies
 Industry adopters Implementers Active users
 Passive users
 Outsiders

 Transport Regulators
 Standards bodies
 Law makers
 Strategists
 DoTs Developers
 Transportation

Technology providers

Users

Deployers Vehicle manufacturers
 Innovators
 Law implementors

3.1 Thinkers

3.2 Planners

Traditionally, AI integration for public services such astransportation infrastructure is considered, discussed,
and tested within research bodies (particularly in academia)
and industry think tanks. AI thought leaders also provide
an anticipatory perspective. The role of these key players
is crucial to ensure that AI integration is considered andprepared for before it happens at scale, driven by awell-informed understanding of the direction, and likely
potential magnitude of AI capabilities.

Planners provide strategic framing and prioritization. Theirmain aim is to ensure fair and non-discriminatory access totransportation for all. Planners include DoTs and MPOs, aswell as regulators, administrations in charge of policy, andstandards bodies. They create the framework that includes
various interactions that underpin the transportation sector,
such as the legal relationships between the transportation
operators and passengers, and relationships between
operators and public administration bodies.

Thinkers provide the necessary deep discussion anddifferent points of view about the vision for the future oftransportation. They consider the challenges people mightface with the introduction of AI, the possible socio-technicalscenarios for the development of a future vision, and thepossible solutions and mitigations to unintended harms.
They are the ones who dissect the possibilities and theresearch to provide the evidence of current challenges andimpacts that can be used to inform planners.

Within AI, planners need to comply with technical andsafety regulations, as well as social and environmental
protection. Planners must be independent from any other
body, but must still consider the views of all the other mainstakeholders to form an impartial viewpoint.
Transportation policy makers at different levels need touse a broad variety of strategies and policy instruments
to tackle challenges and promote opportunities for AI
development. This can generally be a complex task whenit comes to new and disruptive technologies such as AI,
therefore, much of thestrategic development tends tofall behind the speed of technology development by the
deployers – leaving a gap between the plans being adopted
and the technology being deployed.
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3.3 Users

3.4 Deployers

Users are the key to the way the thinkers envision the futureof transportation. Users need to be the focus of planners,
and will be the ones who benefit from the delivery of the
deployers’ solutions. Users, such as goods distributors,
emergency responders, communities, individuals andbusinesses, should clearly represent the primary audienceof transportation advancements, and transportation
advancements powered by AI are no exception.

When it comes to AI in transportation, deployers are currently
operating in a rapidly-evolving regulatory environment– one that affords them a great deal of responsibility forthe outcomes of the solutions they are bringing to market.
Thinkers, planners and users have yet to establish uniform
regulations and standards that deployers need to apply to
AI solutions. In the absense of such guidance, deployers are
responsible for creating services that are sufficiently safe,
efficient, equitable and sustainable.

Given the inherent complexity in AI operations, it isparticularly important that the interfaces of AI systems
be designed to be accessible to the intended user of thatapplication, whether that is an active user intending to utilize
an AI system for a specific job function , or a passive userinteracting with AI as a function of their daily life. For many
AI applications, operators cannot assume that the usersof their AI application will have any great technical literacy
related to the intracacies of AI functionality, and therefore
must ensure that their solutions are accessible to all types
of users. This is particularly important when AI systems arebeing incorporated into public transportation elements,
such as transit, for example, as the AI solution cannot
represent an additional barrier to travelers being able touse transit options. Ultimately, a successful transportation
AI system will be able to deliver on the complexities of theirintended mission while embracing a human-focused design
which establishes an accessible and approachable solution
to the larger traveling public.

The result is that deployers, such as AI technology
innovators, AI technology developers and OEMs, arecurrently creating their own best practices. They arefollowing guidelines and frameworks for the responsible
development of AI solutions, taking into account the inputof thinkers and industry experts. They are keeping updated
on regulations, executive orders, legislation and ways thesepolict aspects might be translated into transportation AI
policy, while considering the diverse needs of users. They
must also anticipate how the standards regulating AI will
ultimately be structured in order to avoid finding theiralready-deployed AI solutions out of compliance with newFederal policy.
Deployers are advancing technological development
and exploring the possibilities that AI can offer within
the transportation sector. They are developing vehicle
automation, solutions for intelligent traffic light
management, applications of mobility as a service (MaaS),
carbon emissions measurement and much more. Ultimately,
given the diverse opportunities within the transportation
sector for AI enhancement, public and private stakeholders
across the larger transportation industry will have to work inclose coordination for AI to achieve advances at system andhuman levels.

Key points

1

The new ecosystem around AI and transportation is still being
formed and developed. Influences, interactions and cooperations
between the main players are in a currently nascent form.

2

Four main groups with clear characteristics and role to play in thisecosystem have been identified to encompass the new ways inwhich the AI within transportation needs to work.

3

These main players will need to work together towards common
goals that guarantee the safety, efficiency, equity and sustainabilityof AI solutions.

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4 Building blocks for responsible AI development
How AI technology is implemented will determine whether
it improves American transportation or creates newchallenges that will need to be solved. There are specificareas of focus for AI stakeholders to consider, ranging fromissues arising from macro-level assessments to technicallevel considerations around AI implementation. Macrolevel risk assessments include aspects related to ethics
and sustainability, regulations and standards, privacy, andtrust, while technical-level considerations could include
robustness and resilience, transparency, safety, andsecurity factors. While specific solutions for each of these
considerations will continue to be developed in coming
years, we will attempt to frame some of the considerations
for each of those variables in the following sections.

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4.1 Macro level risk assessments
A macro-level risk assessment deals with those areas of ethics and regulations that are overarching and applicable to any
technology and aim to protect the user from any harm. While this risk assessment needs to be conducted for all technologies,
it is especially important for AI, which presents greater potential to cause significant challenges due to its scalability and its
capability of amplifying negative impacts throughout the transportation system. These assessments include:

Ethics and Sustainability

Regulations and Standards

PrivacyTrust Agree on a moral compass
and the protection ofpeople’s and planet’s rights

 Assess existing regulations
and standards that might
apply when incorporating AI
within the Transport industry

 Asses the potential to erode
privacy rights through third
party data sharing Create foundational trust by
taking the user needs andconcerns into account fromthe start of the design

 Asses potential impacts
on individuals, groups,
societies, planet of AI withintransport, including job
losses/ job skills evolution

 Understand upcoming
regulations and standards
more general to AI

 Identify potential harmsthat AI could incorporate(impacts on equity,
sustainability, environment) Understand Guidance,
principles, regulatory
ecosystems being formed
and how this affects theincorporation of AI within
Transport Uncover intended andpotential unintended
consequences of an
AI within a transportapplication
 Agree on potential
mitigations and inclusive
human focused andsustainable solution designs
that are beneficial forhumans and the planet Understand related
regulations that an AI
application might have toconsider (privacy)
 Apply best practice/assurance frameworks that
include expert knowledge ofAI and transport Assess the potential
to create new/hidden
discriminatory practices
based on protectedcharacteristics

 Create appropriate tools
for user interpretation andunderstanding of new
techonology

 Understand thecommonalities anddifferences between
privacy laws in different
states and what thatmeans of AI solutions withintransport.

 Promote a balanced
trust on AI so it is used asintended (avoiding overtrust and under trust). User
understanding of limitations
and advantages in different
transport contexts

 Explore the potential of AI
as tool to actually protect
privacy and apply privacylaws Governance processes
are in place to assure
the traceability andtransparency of any AI fromdesign to implementation
to monitoring

Key points

1
2
3

AI solutions for the transportation industry need to look very carefully
at all aspects of ethics, upcoming regulations and standards that
apply to AI. Particular focus should be put on new impacts andharms that AI could bring through unintended consequences.
Privacy laws still apply to any AI development within transportation.
The deployment of any solution must be clear on privacy rights,
sharing of data and evaluate any bias that might arise.

Trust is one of the biggest barriers to the adoption of AI withinhigh safety industries. Trust needs to be built with transparency,
understanding, interpretation and usability of these tools. Otherwise,
users will put the wrong level of trust in them with the consequenceof misuse and safety implications.

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4.2 Technical level risk assessments
In addition to those macro-level assessments related to impacts, there are more specific quantitative risk assessments of thetechnology itself. These are also considered in the White House AI Executive order and EU AI Act, and are mentioned in the UK
AI White Paper. The NIST AI Risk management framework covers many of these risks in detail, the primary of which include:

Robustness and Resilience

Transparency

SafetySecurity

 AI based on historical dataflows should be able toaccomodate unforeseen
operational circumstances,such as data distribution
shifts or unprecedented
levels of information
uncertainty

 Transparency answers thequestion “what happened”
in a system (NIST) AI system should “not under
defined conditions, leadto a state in which human
life, health, property,or the environment isendangered”, (NIST) whichis even more important intransport applications

 Security concerns include
targeted attacks on AI
vulnerabilities such as thosetargeting the data thatis used for training, datapoisoning An AI trained with data
coming from current traffic
conditions may not beeasily transferable to otherroad networks, especiallywhen different patterns andchanges occur Failure of data inputs
or model performanceneeds to be monitored
and fail-safe modes andredundancy systems inplace until they are restored

 Explainability answers thequestion of “how a decision
was made” (NIST) Interpretability answers “whya decision was made” (NIST) Risks assessments on safety
implications of AI solutions
within transport operations
need to be done by design
by developers, productand solution providers
and by any agency thatincorporate them into theirsystem Oversseing andmisunderstanding thesecharacteristics can begrave: a system making
mistakes cannot be
improved if the process bywhich it generates output is
not understood or explained Managing AI safety risk
is a continuous processof improvement, andsimulation, testing andmonitoring to the assure
continuous safety of such
systems Black-box solutions need tobe accompanied by toolsthat help answer the above
questions, especially in highsafety transport solutions
where understanding
the systems are key forsafety, equity, security andsustainability

 Models can also betargeted and manipulatedwith attacks. These, suchas an Oracle Attack, canmanage the exfiltrationof models and themanipulation of outputs
 There can be breahces ofother intellectual property
through AI system endpoints
 Unauthorized access toAI systems can cause
substantial disruption
across any ecosystem,
and for transportation
systems any attack can
be catastrophic. New
cybersecurity defense
methods are crucial

Key points

1

2

AI is a very different type of family ofalgorithms to the ones that has so far been
employed in transportation solutions. As such it
must be created and tested so that robustness
and resilience are assured and validated.

3

Ongoing monitoring of systems needs to bein place because the systems can drift fromoriginal performance resulting in the need tobe re-trained.

4

Transparency of processes and outputs
is essential for human understanding andcorrection of possible malfunctions and errors.

AI introduces new risks that can have serious
implications for safety and security. These are
two key areas that need to be embedded into
the design, development and implementationof solutions.

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5 Outcome-driven tech
Ultimately, while the technological capabilities of AI
are impressive, the goal of transportation-based AI
deployment is not to continue to push the needle on what is
technologically possible; rather, it is to provide meaningful,positive change for transportation users across the board.
Below, we identify examples of use cases across four pillars
that represent some of the main areas of focus for the futureof transportation: safety, efficiency/sustainability, equity
and multimodal applications. These have been identified
from use cases illustrated in the Summary of Potential
Application of AI in Transportation by the US Department ofTransportation Publication Number: FHWA-JPO-20-787.

These use cases represent how AI can be an enabling
technology to achieve improvements in transportation andbe combined with other measures to achieve transportationindustry goals. Some of these are already being trialed
and implemented, while others are more conceptual. Theseuse cases are valuable examples of how AI will impact
transportation. These use cases can be coupled with therecommended approaches outlined earlier in this report tobetter achieve more integrated, holistic and human-focused
AI solutions.

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5.1 Safety
Improving safety across the transportation networkis crucial. With more than 43,000 fatalities on U.S.
roadways each year, there is significant potential for
AI to fundamentally improve safety outcomes, making
meaningful progress towards a world without transportation
fatalities. For the sake of this paper, we have decided tofocus on use cases that can significantly reduce road
fatalities, consistent with the common goals of Vision Zero,which envisions a road network with zero deaths due toimprovements in vehicle and transportation infrastructure
safety. Below are some examples of how AI could help
achieve that vision, including through applications capableof reducing vulnerable road user fatalities and improving
driver performance. These examples include:

Use case

Description

Creating safe intersection
crossings

AI can detect a pedestrian’s intention to cross before they enter the intersection,
automatically switching the crossing signal on and staying on until the pedestrian is safe.
This will assist those with sight and mobility related disabilities, improving safety andequity across the country and making less abled citizens safer.

Recognizing dangerous
driving

AI in vehicles can detect dangerous driving, such as when a driver is distracted or drowsy,
and can predict and analyze information about road incidents before the driver mustreact to them. The implementation of drowsiness detection in vehicles exists already,with many carmakers already using some form of alert, based on driving patterns. Thisis radically improving, where AI is increasingly able to spot distracted or drowsy driving
earlier with more reliability.

Smart headlights

Predict the location and intensity of precipitation and road objects, enabling headlights
to respond dynamically to changing low visibility conditions with a quicker reaction
time than the driver. Also detecting vehicles in the opposite direction and change lights
accordingly and on time so that they do not impede the other driver’s visibility.

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5.2 Equity
Improving equity is a central pillar of the U.S. Departmentof Transportation strategic plan. There are critical ways inwhich AI can play a role in enhancing equity, namely throughimproving accessibility and providing better, more efficientand more cost-effective transportation links to marginalized
and unconnected communities. AI can be leveraged forboth indoor and outdoor wayfinding, as well as to help
sight-, hearing-, and mobility-impaired passengers acrossthe network be able to locate where they need to go and thesafest way to do so. AI can help passengers with language
barriers and disabilities communicate with transit staff
or receive personalized travel alerts on a real time basis.
Additionally, AI can be used by transportation planners tobetter ensure coverage of areas that historically have had
limited transportation options. While this is not an exhaustive
list, these are a few areas in which AI is poised to provide
significant equity benefits to the traveling public. Further
elaboration on some of these themes is included below.

Use case

Description

Automated buses andshuttlesLow speed automated shuttles are usually between 4 - 15 passengers, 10-25 MPH, and SAE
Level 4. There are many demonstrations and pilots, and the technology already exists inprototype form now. These shuttles can provide routes to underserved neighborhoods,
while also providing more options for less affluent citizens.

Personalized informationexchanges

In transportation, AI enables chat-based interfaces for customers to interact with: acustomer can receive details about problems on the roads, road closures, emergency
alerts and alternative routes. This can be progressively personalized to respond to theuser particular needs, using text, audio or images as aid to inform the user according topersonal preferences.

Accessibility andinformation about optimalroutesDigital infrastructure and AI solutions can enhance equitable access in all areas oftransportation according to different user needs. From identifying user needs, patterns,
gaps, forecasting and planning, to integrating accessibility across the transportation
network, including wheelchair access, audio and visual aids and using AI for optimalrouting and available assistance, that uses all that key information, so transportation is
integrated and easy to use by all.

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5.3 Efficiency and sustainability
Another key area of focus for AI in the transportation
sector is achieving greater fuel efficiency and othersustainability goals. Through the utilization of AI’s uniqueabilities to process and interpret large amounts of data,transportation systems can be optimized to reduce traffic
congestion, decrease fuel consumption, and minimize
environmental impacts. This can be accomplished througha variety of ways, including AI-powered route optimizations,traffic signal management, and maintenance monitoringof transportation infrastructure assets to minimize asset
downtime and associated congestion challenges.

These elements are critical in the ongoing net zero strategy,
and the improvement in energy efficiency in USDOT’s 2022
Sustainability Plan.
Further, as new transportation projects move forward, there
is a need to project how land use will change over time, andhow those changes will impact emissions and the climate.
With AI, land use changes can be predicted, documented
and analyzed over time, enabling a better understandingof what measures to take to prevent undue pollution andclimate risk.

Use case

Description

Preparing environmental
impact assessments
for transportation
construction works via
remote Sensing

AI can be used to detect land use change from satellite imagery, LiDAR and predicting
environmental impacts from parking lots and roads. This is achieved through remotesensing and predicting environmental impacts, e.g. through deforestation, hydrology, heatand smog.

Predictive maintenanceand fuel efficiency fortransportation systems

AI can increase the efficiency of tracking and monitoring infrastructure, repair andmaintenance. It can reduce unplanned downtime by monitoring sensor data and alerting
management to abnormal metrics, which can help technicians by providing insights thatare hard to see or track over time. AI can also be used to ease data overload by fusing
and highlighting the most critical areas. Furthermore, intelligent fuel efficiency algorithms
can help reduce emissions and pollution by making small changes in how a vehicle
operates, through the use of engine control and engine diagnosis.

Traffic signal control
systems

AI can be used for Traffic Signal Control (TSC) systems, where counting the number ofvehicles, the vehicle distances and the queue lengths are all important for regulating
traffic flow, improving energy management for vehicles, adaptive energy management fordifferent road conditions, freeway control and using these data for traffic simulations tomeasure carbon zero emissions and create further solutions.

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5.4 Multimodal applications
The transportation ecosystem is incredibly varied, including
freight and passenger travel across numerous modalities
that share many of the same routes and intersect atdifferent points and in different ways. It is therefore very
difficult to manually model how a change in one area may
impact changes in others given the system of systems.

manage numerous infrastructure components at once. AI
can assist with multimodal transportation planning androuting, calculating changes and predicting reroutes moreeasily, all with the goal of improving access and equity to alltransportation end-users.

AI provides considerable value to coordinating multimodaltransportation. Intelligent systems can delineate varying
traffic flows and transportation modes at different times,
using network analysis to increase accessibility andUse case

Description

Multimodal intelligent
traffic signaling system

AI can optimize traffic signaling to service all modes of transportation by predicting
vehicle and pedestrian arrivals, queues and delays. This could be used for emergency
vehicles, freight, pedestrians or cyclists, and can even be expanded to have individualintersections control their own traffic.

Multimodal mobility on
demandAI can improve multimodal mobility on demand for individual users and the transportation
network by analyzing group behaviors and prioritizing efficient transit for groups oftravelers with similar goals and destinations. Further, individual experiences can be
dynamically optimized, creating the best outcomes for the most people – mobility
impaired travelers can be guided to transit with more seating, or travelers with time
constraints can have speed prioritized.

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5.5	Use cases of applications targeting
multiple outcomes
Not all applications are focusing only on one particularoutcome. More often, in fact, they are targeting several of
the key outcomes described above. Examples include:

Key outcome

Sustainability/
efficiency/
safetyUse case

Description

Predictive maintenancePredictive maintenance can be carried out by different approacheswith the aim of sustainability, efficiency and safety as a minimum.
For example, digital twins combine data and models and create
links between the physical world and the digital one. They can beused as training beds for AI in order to build predictive models andtesting beds where AI can be fed historical data about – for example– maintenance problems and create predictive maintenancealgorithms that can then be applied in real operations.
Consistently maintained road safety furniture can considerably
improve safety. Instead of barriers that may already be weakened ordamaged to create hazards, transportation administrators will havea clear view of which elements of the network need attention.

Sustainability/
efficiency/
safetySustainability/
efficiency/
safetyEV charging

AI can assist planning for sustainability and efficiency of EV charging
point availability, management impacts of electric vehicle uptake
and measuring and predicting impacts on the grid and grid’s service
needs. Equity is also improved, as more communities across thecountry have access to charging points that work, which means thatthey are able to travel to work or leisure more easily with more uptakeof EVs and electrification of transportation that contribute towards
carbon zero targets. Different vehicles can use these charging points
for longer, allowing for the same infrastructure to better serve avariety of uses.

Traffic light optimization
at intersections

Intersections are a substantial source of emissions as well as
accidents, with pollution much worse at these points than atother points in a transportation network. The predominant reason
why these emissions are so much worse at intersections is due tothe starting and stopping of vehicles. Early results of the project
claim a 30% reduction in stops and 10% reduction in emissions atintersections. With a variety of data collected at intersections andtraffic light optimization, an AI model can provide recommendations
to civil and traffic engineers who can optimize the system with aclearer understanding of where hot spots for accidents can be foundand prevented by responsive traffic light optimization.

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6	The main enabler: implicationsof regulatory approaches
There are currently many guidance documents, frameworks,
pieces of legislation and regulations seeking to regulate AI indifferent industries and parts of the world. Given continued
public and private sector interest in AI, policymakers are
increasingly considering potential regulatory approaches togovern AI research, development, deployment, and use. Hereis an overview of three of the main approaches from theUnited States, European Union, and United Kingdom

6.1 US approach
Achieving an appropriate regulatory framework fordevelopment and utilization of AI has been a key priorityof the Biden Administration and Federal lawmakers. In
comparison with some of the United States’ global partners,
policymakers in the U.S. have historically adopted a morehands-off approach to AI regulation, often allowing industry
self-regulation while prioritizing innovation and competition.
This section will describe some of the specific ways in which
the United States has approached AI regulation, including
aspects specific to transportation.

to national security, safety, public health, or the economy
will be required to share “test results” and models with theFederal government prior to making the models public. The
order further directed NIST to to set rigorous AI standards
for extensive testing to ensure that AI-powered systems
are safe, secure, and trustworthy before release, as wellas additional provisions related to privacy concerns andworkforce considerations. In the absence of successful
legislation establishing a more prescriptive Federal
legislative framework, NIST’s leadership over standards
and best practices remain the primary way in which AI
development is guided within the United States.

This approach was reflected in the National AI Initiative
Act (NAIIA) of 2020, which established the National AI
Initiative Office (NAIIO) to coordinate key AI activities
across the federal government. The National Instituteof Standards and Technology (NIST), a non-regulatory
agency, was made responsible for developing standards,
guidelines, and other resources to guide AI use throughestablishing best practices, technical guidelines, andtools to help organizations manage the risks and benefits
associated with the development and deployment of AI
systems. The NAIIA specifically directed NIST to develop
an AI risk management framework, a voluntary tool thatorganizations can use to evaluate, assess, and manage
risks that may result from the use of AI. NIST released thefirst iteration of this framework in early 2023 and madeclear that the document was intended to be “voluntary,rights-preserving, non-sector-specific, and use-caseagnostic, providing flexibility to organizations of all sizes
and in all sectors and throughout society to implement theapproaches in the Framework.”

Specific to transportation, the October 2023 Executive
Order directs the Secretary of Transportation to utilize theNontraditional and Emerging Transportation Technology(NETT) Council to “assess the need for information, technicalassistance, and guidance regarding the use of AI intransportation,” as well as to “support existing and future
initiatives to pilot transportation-related applications ofAI, as they align with policy priorities articulated in the
Department of Transportation’s (DOT) Innovation Principles,
including, as appropriate, through technical assistance andconnecting stakeholders.” The Order directs the Secretaryof Transportation to direct appropriate Federal Advisory
Committees of the DOT to provide advice on the safe andresponsible use of AI in transportation, and to direct theAdvanced Research Projects Agency-Infrastructure (ARPA-I)
to explore the transportation-related opportunities andchallenges of AI — including regarding software-defined AI
enhancements impacting autonomous mobility ecosystems.
The Order also specifically encourages the Architecturaland Transportation Barriers Compliance Board to leverage
AI solutions to increase access to transportation options forpeople with disabilities.

In October of 2023, the White House released an Executive
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Legislatively, the NAIIA was the largest major step that
Congressional leaders have taken to regulate AI, and theimpacts that legislation has had on Federal AI policy arebriefly described above. More recently, various policymakers
have released proposals in the Senate that aim to outline
key policy objectives for an AI framework. Most notably, in
June 2023, Senate Majority Leader Chuck Schumer (D-NY)
released a “SAFE Innovation Framework,” which outlined
five policy objectives for AI governance. Those objectives
included safety, accountability, foundations (aligning AI
with societal goals), explain (information sharing), andinnovation. Additionally, in September 2023, Senators Richard
Blumenthal (D-CT) and Josh Hawley (R-MO), Chair andRanking Member of the Senate Judiciary Subcommittee
on Privacy, Technology, and the Law announced abipartisan legislative framework to establish guardrails
for artificial intelligence. The framework lays out specificprinciples for upcoming legislative efforts, including theestablishment of an independent oversight body, ensuring
legal accountability for harms, defending national security,
promoting transparency, and protecting consumers andchildren. While neither of these proposals were specificto transportation, they demonstrate the manner in which
legislators are approaching AI regulation as of late 2023.

practices on issues including data collection and use,
privacy regulations, and workforce regulations. These key
issues represent main focuses of NIST’s efforts in this space,particularly in their efforts to establish performance-based
standards which attempt to ensure safety and reliability
without stifling innovation and economic competitiveness.
NIST is well-positioned to share resources with states tohelp avoid a patchwork approach to AI regulation, but a
key challenge will be continuing to update standards at apace that tracks with AI development, as well as effectively
communicating new AI standards to the public andprivate sectors quickly enough to respond to emerging AI
opportunities and risks.
In the absence of additional legislative support for awider Federal framework governing AI (which would ideally
address security, accessibility, and privacy), Congress
could help NIST address these challenges by appropriating
additional funding for Federally sponsored AI research,which can help NIST and Federal regulators keep pace withemerging AI developments. There is palpable excitement
within the transportation and technology sectors about
the possibilities for AI, and industry continues to pushforward with developing AI applications. It is critical that
Federal leaders move quickly to create a robust Federal
AI framework in order to channel that momentum towards
solutions that benefit the public, such as encouraging the
deployment of AI use cases that promote transportation
safety, efficiency, and sustainability for all travellers.

While some states have created working groups to consider
AI regulations or have limited to use of AI in certain hiring
practices, states are primarily following the lead of NIST
and other Federal partners in terms of adopting AI best28
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6.2 EU approach

Civil Liberties, Justice and Home Affairs) who are reviewing
the Act say that it:

The EU’s approach to AI risk management and regulation
is multifaceted. The new EU AI Act (still in progress ) will be
bridging into already implemented legislation, such as theGeneral Data Protection Regulation (GDPR), and linking
to newly enacted legislation, the Digital Services Act andDigital Markets Act.

 Needs to not overlap with sectoral legislation by imposing
double/conflicting obligations on transportation actors.

The EU AI Act, as it stands, represents a prescriptive
legislative framework. It is based on the classificationof AI applications into prohibited, high-risk and low
risk. Prohibited applications are very clear. For the rest,
depending on whether the application is considered highrisk or low risk, it imposes legislative obligations at all stagesof the lifecycle of an AI system, from training, testing andvalidation to conformity assessments, risk management
systems and post-market monitoring.

 Needs to foster research and innovation to ensure theEU’s transportation sector develops its know-how in theimplementation of AI, while upholding the highest ethicalstandards.

 Needs to promote the development of, and upholding,international standards, that are particularly important
for the transportation sector.

There is a strong emphasis from transportation sectorsthat with the integration of AI systems in the sector, newchallenges could emerge in risk management. But the opinion
is that this new AI regulation should only apply to high-risk
applications in the transportation sector in so far as that they
are not already covered by sectoral legislation and where
they could have a harmful impact on the environment or
health, safety and fundamental rights of persons.

The EU AI Act has a horizontal approach across industries,
the purpose of which is to improve the functioning of theinternal market with a uniform legal framework for thedevelopment, marketing and use of AI. On the other hand,industry verticals such as transportation is governed by
Article 4(2)(g) and Title VI (Articles 90–100) of the Treaty on the
Functioning of the European Union, making transportation
one of the EU’s most strategic common policies.

In this sense, there are still question marks about how
the regulation coming from the AI Act might work sincetransportation and mobility are already highly regulated.
Some EU agencies have already begun to consider how AI
affects their regulatory processes. One leading example
is the EU’s Aviation Safety Agency (EASA), which first set
up an AI taskforce in 2018, published an AI roadmap
oriented towards aviation safety in 2020, and released
comprehensive guidance for AI that assists humans inaviation systems in 2021.

The intersection between the vertical and horizontal
approaches is going to need a very complex exercise of
harmonization of current and proposed policies that affectsafety, security and impact on equity and sustainability.
Representatives of the transportation sector (Committee
on Transport and Tourism for the Committee on the InternalMarket and Consumer Protection and the Committee on29© Cambridge Consultants & ITS America 2023

Report: The Impact of AI on Transportation and Mobility

6.3 UK approachThis approach makes use of regulators’ domain-specificexpertise to tailor the implementation of the principles tothe specific context in which AI is used. The UK Governmentis aware that regulatory co-ordination will be essential.
The risk is that in the absence of a centralized regulator,
UK regulators will diverge in approach. The white paper
discusses measures that the UK government will take tomitigate this risk, including issuing guidance for regulatorsin implementing the principles, central monitoring andevaluation of the framework and principles, and a multiregulator AI sandbox.

The UK is opting for no further legislation at this stage.
Instead, the UK White Paper titled ‘UK regulation: a proinnovation approach’, released in August 2023 outlines fiveprinciples that UK regulators should consider to best facilitate
the safe and innovative use of AI in the industries they
monitor: (1) safety, security and robustness; (2) transparency
and explainability; (3) fairness; (4) accountability andgovernance; and (5) contestability and redress.

The DfT is already in consultation with industry experts
to gather insights into the main issues they need to focus
on when it comes to issuing extra regulation for AI intransportation. The DfT recognizes that it has a criticalrole to play in supporting the responsible growth andadoption of the AI industry. It is already a frontrunner increating world-first regulatory frameworks in areas suchas self-driving vehicles, as well as supporting researchand development initiatives. To scale its use of AI, DfT isinterested in developing a governance framework for
responsible AI trials and an AI Strategy for Transport that isalready in preparation.

The UK government has issued these principles on a nonstatutory basis, but it may introduce a statutory duty on
regulators at a later date. Strengthening the UK approach,especially around AI safety, 28 countries gathered in
November 2023 at an event convened by the UK. Here,the U.S., China, EU, and others agreed to the Bletchley
Declaration on opportunities, risks and the need forinternational action on frontier AI (systems where we face
the most urgent and dangerous risks).
The UK principles will be implemented by existing regulators,
such as the Medicines and Healthcare products Regulatory
Agency (MHRA), the Equality and Human Rights Commission,
the Information Commissioner’s Office (ICO), the Competition
and Markets Authority and transportation regulators(industry control and regulation is exercised principally
through the Department for Transport (DfT), its executive
agencies and the Traffic Commissioners).

Key points

1

2
3

We are experiencing an evolving and complex
time in history where regulations on AI arebeing defined and developed in different
geographies, with the USA, UK and EU being at
the forefront of these developments.

4

All three regulatory approaches recognize
the delicate balance in order to achieve andpromote innovation while assuring safety andresponsible AI development.

5

These AI horizontal and overarchingregulations will need to be harmonized withthe transportation industry regulations
already in place, with an emphasis fromtransportation authorities on avoiding
duplication and complexity.

How regulations are developed (risk-based
approach, guidance-based approach or
more prescriptive-based approach), will
shape the development of AI in transportationin different countries.
Going forward, evolution of regulations andharmonization at global level will be key. Bestpractices and knowledge exchange between
industries and geographical areas will benecessary to advance and update regulations
when needed and in order to anticipate andanswer the needs of the exceptional fastpaced growth of AI.

30© Cambridge Consultants & ITS America 2023

Report: The Impact of AI on Transportation and Mobility

7 Conclusion
The US transportation system is poised for a dramatictransformation based on the deployment and applicationof AI technologies. In the coming decades, AI innovation canhelp propel far-reaching improvements in transportation
safety, efficiency, equity and sustainability. We believe that
AI can be a critical contributor to significant public benefit
within the transportation sector, helping transportation
stakeholders successfully achieve ambitious improvements
to the way that transportation planning, operation and user
experience are managed within the U.S.
However, utilizing AI to achieve these outcomes will requirerobust coordination among Federal leaders, transportation
planners, technology developers, and other related
stakeholders. This paper has outlined what we believe aresome of the key considerations for navigating the next
phase of AI in transportation development and deployment,
including what we see as opportunities as well as challenges
related to successful transportation AI implementation.
Ultimately, a U.S. transportation future powered by AI iswithin our reach. It is our hope that the recommendations
within this paper will help shape that future to be one inwhich AI is positioned to make meaningful strides towards
a safer, smarter and greener transportation system for allroad users.

31© Cambridge Consultants & ITS America 2023

About ITS America
The Intelligent Transportation Society of America wasfounded by Congress to advise the federal government
on digital transformation in transportation and is
the only organization that brings together private
industry, government, researchers, non-profits, andcommunities to advance the deployment of intelligent
transportation technologies to save lives, promotesustainability, and increase access and opportunity for
all communities. Our vision is a better future transformedby transportation technology and innovation that is
Safer, Greener, and Smarter for All. For more information,please visit www.itsa.org.

About Cambridge Consultants
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together, this enables innovative product and services
development and insightful technology consulting. We workwith companies globally to help them manage the business
impact of the changing technology landscape.
We’re not content to deliver business strategy based ontarget specifications, published reports or hype. We pride
ourselves on creating real value for clients by combining
commercial insight with engineering rigor. We work with someof the world’s largest blue-chip companies, governments aswell as innovative start-ups that want to change the statusquo fast.

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