Article citation information:
Kaledinova, E., Boeldak, D. The assessment of the impact of artificial intelligence on transportation sustainability. Scientific Journal of Silesian University of Technology. Series Transport. 2024, 125, 101-113. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2024.125.7.
Elena KALEDINOVA[1],
Dianthy BOELDAK[2]
THE
ASSESSMENT OF THE IMPACT OF ARTIFICIAL INTELLIGENCE ON TRANSPORTATION
SUSTAINABILITY
Summary. Assessing the impact of
artificial intelligence (AI) on transportation sustainability is important for
companies’ internal and external benchmarking and improving technology and
sustainability policies. This article focuses on developing and using the
Transportation Technology Sustainability Index (TTSI) to assess the impact of
AI on transportation sustainability. The authors did literature research to
formulate dimensions and define sustainability indicators (SIs) and conducted
interviews with experts from Dutch road freight transportation companies to
validate the dimensions and SIs that can be used for calculating TTSI. The same
TTSI-based assessment with some necessary adjustments can be applied to other
modern technologies and transportation modes such as air, rail, sea, and
multi-modal. Literature and qualitative research confirmed that AI applications
foster sustainable performance, so more emphasis should be placed on increasing
the use of AI in transportation. The TTSI-based assessment developed by the
authors would allow transportation companies to measure the impact of AI or
other advanced technologies on sustainability.
Keywords: artificial intelligence, sustainability
indicators, transportation sustainability, Transportation Technology
Sustainability Index, company benchmarking, AI skills
1. INTRODUCTION
Technological development has tremendously
accelerated, and fast-paced change can happen within a brief time. Roser [15]
states that this fast-paced change driven by AI could speed up even more, and
AI is already changing our world.
New technologies such as AI, blockchain,
Internet of Things, virtual reality, big data or robotics transform business
activities and working environments and can improve the performance of
companies. These technologies are also reshaping the transportation landscape
and hold the potential for sustainability [19].
The Dutch economy relies heavily on the
transportation and logistics sectors. However, challenges related to emissions,
safety, and increasing demand for public and business properties lie ahead. Numerous
trucks transport goods across various routes in the Netherlands. These
activities are good for the economy, but they are also causing problems like
traffic jams, accidents, and pollution [7].
The Dutch government wants to use artificial
intelligence (AI) technology in transportation to help the country's economy
and remain a major hub for moving goods around.
At the end of 2019, the Netherlands AI Coalition (NL AIC) was
established to catch up to world leaders in AI like China and the USA and provide
an effective platform and favorable business environment for Dutch AI
initiatives [7, 13]. AI in transportation is critical for optimizing processes,
planning, and forecasting and fosters better management, decision-making, and
sustainability.
The assessment of the impact of AI on
transportation sustainability would allow transportation companies to
understand sustainable performance gaps related to AI and to improve companies’
technology and sustainability policies.
The research objective is to develop the
Transportation Technology Sustainability Index (TTSI) which can be calculated
based on sustainability indicators (SIs) measuring the impact of technology on
transportation sustainability. The index can be used for transportation
companies’ benchmarking, and support making decisions in the field of
technology and sustainability. Furthermore, companies can monitor their
changing technology sustainability over time to understand the impact of new
technologies on sustainable performance.
This study considers only AI technology,
however, TTSI can encompass other modern technologies such as blockchain,
Internet of Things, virtual reality, big data, robotics, etc. The index can be
used by transportation companies to assess the impact of AI or other technologies
on their sustainability and to identify possibilities for sustainable
improvement.
The study offers an overview of dimensions and
sustainability indicators (SIs) for calculating TTSI. The dimensions and SIs
were validated through qualitative research based on interviews with experts
from Dutch road freight transportation companies using or going to use AI.
The main research question is: How can the
Transportation Technology Sustainability Index be calculated and applied to
assess the impact of AI on transportation sustainability?
Research sub-questions are: 1) How can AI
enhance transportation sustainability? 2) What dimensions and sustainability
indicators (SIs) can be used for the TTSI-based assessment? 3) How can SIs be
used for calculating the Transportation Technology Sustainability Index?
The outcomes of this study are about defining
dimensions and sustainability indicators for calculating TTSI and giving
recommendations on how to apply this index in transportation companies. These
outcomes can be used by the transportation industry stakeholders such as
policymakers, managers, and employees. The authors also discussed the future
research directions in the ‘Discussion’ section.
2. METHODOLOGY
The authors carried out literature research and
qualitative research based on interviews with experts from Dutch road freight
transportation companies to answer the main research question and research
sub-questions.
The literature research explored current trends
and advancements in AI applications for road freight transport in the
Netherlands, focusing on sustainability performance improvement. This involves
gathering and analyzing existing literature sources to understand the landscape
of AI applications in freight transport and the impact of AI on sustainability.
The literature research aimed to formulate dimensions and define SIs relevant
to the TTSI-based assessment of the impact of AI on transportation
sustainability.
To validate dimensions and SIs the qualitative
research was done through operationalization and thematic analysis of data
collected from interviews with experts from Dutch transportation companies (see
the section ‘Qualitative research’).
The interviews were recorded, transcribed, and
analyzed to understand experts' perceptions of AI and the relevance of
dimensions and SIs for the TTSI-based assessment. The deductive approach of
thematic analysis was applied to analyze qualitative data (interview
transcripts) using the set of themes formulated by authors (see the section
‘Qualitative research’). The overview of dimensions and corresponding SIs in
combination with outcomes of thematic analysis is included in the subsection
‘Expert inputs and analysis of qualitative data’.
The authors formulate the limitations and the
gaps addressing the choice of dimensions and SIs and give recommendations to
stakeholders on how to use the TTSI-based assessment in the section
‘Discussion’.
3. LITERATURE RESEARCH
By applying modern technologies in
transportation, stakeholders can improve decision-making processes, and foster
innovations and sustainability. AI applications for self-driving cars and
trucks, real-time road condition monitoring, driver behavior and fatigue
monitoring, route planning, and optimization can significantly transform
transportation.
Abduljabbar et al. [1] outlined the historical
development of AI, focusing on how AI has advanced over time and its increasing
applicability to real-world problems including those in transportation. AI
advancements include addressing specific transportation challenges (CO2 emissions,
optimal routes for vehicles, energy consumption, etc.), developing self-driving
cars and trucks, and improving their safety and efficiency. According to
Srivastava [18], AI in combination with other emerging technologies like Internet
of Things, machine learning, cloud computing, and big data analytics
empowers interconnectivity between vehicles enhancing efficiency through
collecting and processing data about traffic, driving patterns, and road
conditions. Despite advancements, there remain concerns about the reliability
and effectiveness of AI algorithms. Hong et al. [10] researched whether an AI
system can consistently perform its intended functionality over a specified
period. For example, an autonomous vehicle equipped with AI should reliably
navigate and make decisions throughout its operational lifetime.
Examining various frameworks and models helped
identify the trends in the application of AI in transportation and its impact
on sustainability. In this context, the most notable AI-related frameworks and
methods are the Smart Mobility Framework and Artificial
Neural Networks (ANNs). They provide insights into
sustainability in transportation and the role of AI. According to
Bıyık et al. [4], the Smart Mobility framework is based on several
components including intelligent transport systems, open data, big data
analytics, and citizen engagement. Intelligent transport systems leverage AI to
optimize traffic flow, reduce congestion, and minimize emissions. This
framework fits perfectly freight transportation, fostering fleet management,
routing processes, and sustainable performance. Abduljabbar et al. [1]
highlighted that AI-based models such as Artificial Neural Networks (ANNs) can
be widely used in transportation. ANNs can be applied for traffic planning,
forecasting traffic patterns, scheduling infrastructure maintenance, and
predicting traffic conditions such as weather conditions or road congestion. To
sum up, ANNs can help bridge the gaps in sustainable performance and move
towards more sustainable transportation.
The literature research reveals the fast growth
of AI applications in transportation and their impact on sustainability. It
emphasizes the relevance and importance of developing the TTSI-based assessment
for evaluating the impact of AI on transportation sustainability. The authors of
this study examined articles and reports about transportation sustainability
and AI to formulate dimensions and define corresponding SIs.
Sustainable development fosters the awareness
of the need to optimize business processes. The study by Lyamina et al. [11]
discussed the definition of company sustainability and developed a model for
assessing the company sustainability, including the transport aspect of company
sustainability. It suggests such sustainability metrics as route optimization,
vehicle utilization, number of trips, CO2 and other pollutant
emissions, and fuel consumption. The new technologies, including AI, hugely
influence company processes and sustainable development. Degot et al. [6]
discuss AI’s impact on emissions through optimizing processes and enhancing
energy efficiency. AI-driven process optimization can improve efficiency in
transportation, thereby reducing carbon emissions and cutting costs. Sanghavi
[16] highlights that AI has the potential to reduce traffic congestion and improve
transportation system efficiency. These improvements significantly influence
economic and environmental benefits. Ranyal et al. [14] provide insights into
road condition monitoring systems using AI methodologies. The timely detection
of faults is crucial for riding comfort and safety. According to Abduljabbar et
al. [1], AI can help solve the challenge of increasing travel demand, CO2 emissions,
and safety concerns. Smith [17] emphasizes the ethical challenges of using AI
in transportation, particularly in autonomous vehicles. The main ethical
concerns are risks related to software malfunctions and the need for
safety-enhancing measures. All in all, different areas of sustainability can be
improved by AI. Based on this information, the authors formulated dimensions
and defined SIs for each dimension to calculate TTSI. The dimensions are CO2
emissions, energy efficiency, cost optimization, profit generation (new revenue
streams), social benefits, and ethical principles. Table 1 illustrates the
dimensions and corresponding SIs used for the TTSI-based assessment.
4. QUALITATIVE RESEARCH
Qualitative research involves collecting
and analyzing qualitative data (e.g., text) to gain insights into
complex, abstract topics. It requires operationalization, considered as the
process of turning abstract concepts into measurable evidence. The process
involves defining a concept and its dimensions and indicators to understand the
full scope of the concept and facilitate data collection and analysis.
In this study, the concept is formulated as
follows: assessing the impact of AI on transportation sustainability. The
qualitative research consists of four steps: 1) identifying dimensions and SIs
(operationalization); 2) preparing interview questions based on operationalization;
3) collecting the qualitative data through interviews with experts; and 4) the
thematic analysis of data. This approach validated dimensions and SIs in the
context of assessing the impact of AI on transportation sustainability.
4.1. Dimensions and SIs
The
authors identified six dimensions and ten corresponding SIs based on the
literature research. Table 1 comprises operationalization and explanations of
the relevance of indicators.
Tab. 1
Operationalization (Source: own elaboration)
|
Dimensions |
Sustainability indicators |
Relevance |
|
CO2 emissions |
AI-driven
strategies to reduce CO2 emissions |
Relevant for emission reduction through
better transportation planning and vehicle utilization. Better vehicle utilization practices reduce
the number of partially loaded trucks on the road and thus prevent road
congestion. |
|
Energy efficiency |
Predictive vehicle maintenance |
Relevant for improved energy efficiency and
cost savings. |
|
Optimizing transportation routes |
Relevant for route optimization, fuel consumption
reduction, and cost savings. |
|
|
Cost optimization |
AI analytics to minimize operational costs |
Relevant for cost savings and
cost-effectiveness analysis. |
|
Profit generation |
Exploring new revenue streams through
AI-enabled services |
Relevant for revenue diversification and
business growth. |
|
Social benefits |
Training & Development of employee AI
skills |
Relevant for employee competencies. |
|
Health and well-being of employees |
Better route optimization and workload
management enhance safety conditions, contributing to the health and
well-being of employees. |
|
|
Work-life balance |
Relevant for reducing manual tasks, and
allowing more flexible working schedules. |
|
|
Ethical principles |
Transparent AI algorithms |
Relevant for unbiased decision-making and
ethical and responsible AI implementation. |
|
Data-secure AI algorithms |
Relevant for protecting sensitive data and for ethical and
responsible AI implementation. |
The dimensions and indicators were used to
prepare questions for interviews with experts and collect data focusing on
adopting AI applications in road freight transportation companies, and AI’s
potential impact on sustainability.
The authors applied the deductive thematic
analysis of interviews. According to Hecker and Kalpokas [9], deductive
thematic analysis in qualitative research applies predefined themes
or concepts to qualitative data. The following themes were identified
based on the dimensions and indicators of the study concept (Table 1) and focus
points of the field research: 1) stakeholders' perceptions towards AI
technology; 2) change in management and training; 3) routing planning and
vehicle utilization processes; 4) vehicle maintenance practices; 5) balancing
cost-effectiveness and sustainability within companies operations; 6) health
and well-being of employees; 7) the environmental impact of transportation
activities (CO2 emissions); etc.
4.2. Expert inputs and analysis of qualitative
data
The qualitative research was carried out in
April and May 2024 and was quite challenging and time-consuming because it was
difficult to find interviewees. Moreover, many companies approached did not
know what AI is or do not utilize it in their operations yet. The authors
contacted Dutch transportation companies that met such criteria as the size of
companies (more than 50 employees), utilization of AI applications, or
consideration of implementing AI in their business processes in the future.
Experts from six companies agreed to give interviews. Two companies operate
internationally, and the rest operate locally in the Netherlands. The names of
companies are not mentioned on their request. The operations manager, head of
IT, software engineer, logistics manager, and transport and logistics
specialists participated in interviews. All interviews were recorded via
Microsoft Teams and transcribed.
The qualitative research indicated that across
the companies, there is a shared recognition of the potential benefits in the
field of sustainability that AI can bring to operations and fleet management
practices [5]. The expert inputs support the idea that the assessment of the
impact of AI on transportation sustainability is important for understanding
how to reach sustainable goals.
On the one hand, the experts highlighted the
growing trust in AI capabilities, although it requires time and effort to
overcome technical challenges and ensure effective integration into existing
systems. On the other hand, they described existing barriers such as a lack of
employee awareness and specific examples of AI tools implemented for
sustainability. Moreover, they expressed concerns about technological
limitations such as the range of electric trucks and the reliability of AI algorithms.
They also see challenges associated with adopting and implementing AI-driven
solutions for sustainable fleet management.
An expert at Company 1 emphasized the need for
thorough research and exploration before full-scale implementation. There are also
concerns about security, privacy, and data management which need to be
addressed to ensure smooth integration and effective utilization of AI
technology. He added that employee training and awareness-raising efforts are
ongoing to prepare all employees for the integration of AI into their
workflows.
An expert at Company 2 mentioned the need for
guidelines for employees to adapt to AI integration effectively. Furthermore,
ensuring that drivers and other staff understand the technology and its
implications for their roles is crucial for successful implementation. The lack
of specific rules and regulations for the application of AI creates
difficulties, requiring proactive guidance and collaboration with regulatory
organizations to guarantee the ethical and responsible use of AI.
Despite these barriers, there are notable
contributions of AI to collaboration and continuous improvement. Company 1 and
Company 3 collaborate with external experts or partners in the context of the
potential for knowledge sharing and partnerships to drive innovation. Various
departments at Company 1 and Company 3 are involved in the initial stages of AI
implementation, emphasizing collaboration across business units. Training
programs are being developed to equip employees with the necessary skills to
work effectively with AI tools and systems.
According to an expert from Company 4, there
are AI-related benefits for sustainable fleet management. These include
improvements in operational efficiency, cost-effectiveness, emission reduction,
and increased driver safety. AI technologies offer the potential for optimizing
routes, reducing fuel consumption, and minimizing emissions, contributing to
environmental sustainability and cost savings. Moreover, AI-driven solutions
improve data accuracy and precision, thus enhancing operational efficiency and
decision-making, leading to smoother operations and greater customer
satisfaction.
Company 5 views AI as a key solution for
improving matches between shipments and carriers, leading to more efficient and
environmentally friendly transportation. They integrate AI systems into their
operations using logistics platforms. This indicates a management shift towards
utilizing AI for greater efficiency and sustainability by focusing on full
truck utilization.
Company 6, focusing on safety and health in the
workplace, emphasizes the contribution of AI to enhancing employee well-being
by reducing stress and improving working conditions. Furthermore, the technical
sophistication of AI-based automatic planning systems such as Ortec presents
opportunities for continued innovation and advancement in sustainable fleet
management practices within this company.
The experts' answers to interview questions
confirmed the relevance of dimensions and SIs for assessing the impact of AI on
transportation sustainability. Experts’ perceptions and remarks about possible
barriers are presented in Table 2.
Tab. 2
Expert inputs (Source: [5])
|
Dimensions |
Sustainability indicators |
Expert inputs |
|
CO2 emissions |
AI-driven
strategies to reduce CO2 emissions |
Stakeholders positively perceive the impact
of AI on emission reduction, but lack specific examples; barriers include
awareness gaps (knowledge of AI among employees) and security concerns (data
security). |
|
Energy efficiency |
Predictive vehicle maintenance |
Recognition of AI's potential to improve
efficiency in maintenance planning (predictive maintenance); barriers include
technological limitations and reliability concerns. |
|
Optimizing transportation routes |
Acknowledgment of AI's role in route
optimization; barriers include the need for thorough research and
exploration. |
|
|
Cost optimization |
AI analytics to minimize operational costs |
Growing trust in AI capabilities for cost
optimization; barriers include security and privacy concerns. |
|
Profit generation |
Exploring new revenue streams through
AI-enabled services |
Recognition of AI's potential for revenue
generation; barriers include lack of specific regulations. |
|
Social benefits |
Training & Development of employees AI
skills |
Recognition of a need for comprehensive
training and development programs to ensure employees can effectively
integrate AI into their workflows. This includes raising awareness and
providing guidance on AI technologies to prepare employees for its adoption
and use. |
|
Health and well-being of employees |
Recognition of AI’s potential to improve the
health and well-being of employees. This includes reducing stress through
better route optimization, workload management, and enhancing safety conditions,
contributing to a safer and healthier work environment. |
|
|
Work-life balance |
AI can contribute positively to work-life
balance by optimizing work schedules, reducing manual tasks, and allowing for
more flexible working conditions. |
|
|
Ethical principles |
Transparent AI algorithms |
Emphasis on transparent, unbiased AI
algorithms; barriers include reliability concerns. |
|
Data-secure AI algorithms |
Concern for ethical AI use; barriers include
security and privacy concerns. |
Experts made valuable remarks and expressed
reasonable concerns, for example, about the reliability of AI applications.
These concerns are critical for ensuring the safe and effective deployment of
AI.
Interviews with experts provided useful
information regarding the impact of AI on transportation sustainability. This
information proved the relevance of dimensions and SIs formulated based on the
literature research.
5. FINDINGS
The literature research findings address the
sustainable effects of several frameworks and models used in road freight
transportation and the definition of dimensions and SIs for the TTSI-based
assessment. The existing literature focuses on public transit and has only a
few frameworks on the relationship between new technologies and transportation
sustainability. The Smart Mobility framework and ANNs can significantly assist
in implementing AI (or other advanced technologies) and improving
transportation sustainability. By applying these models, stakeholders can
develop targeted strategies to optimize operations, reduce costs, and minimize
environmental impact in transportation. Considering the growing potential of AI
for improving sustainable performance, the authors developed TTSI to assess the
impact of AI on transportation sustainability and identified dimensions and SIs
(Table 1).
The qualitative research findings include
dimensions and SIs validated by expert inputs (Table 2). They are used for
assessing the impact of AI on transportation, as described below.
5.1. The TTSI-based assessment
The assessment is based on performance scores
assigned by managers or stakeholders. The calculation includes three steps: 1)
defining the performance scores; 2) determining SIs weights in case of the need
for prioritizing; and 3) calculating TTSI.
Performance scores can be assigned using a
scale from 1 to 4 (1 - insufficient, 2 - sufficient, 3 - good, 4 - excellent)
to measure the success of AI integration regarding transportation
sustainability (Table 3). Scores might be assigned with one or two decimals
(e.g., 1.25 or 3.4).
Tab. 3
The TTSI-based assessment (Source: own
elaboration)
|
Dimensions |
Sustainability
indicators |
Performance scores (1 to 4) |
Targets |
|
CO2 emissions |
AI-driven
strategies to reduce CO2 emissions |
|
|
|
Energy efficiency |
Predictive
vehicle maintenance |
|
|
|
Optimizing transportation routes |
|
|
|
|
Cost optimization |
AI analytics to minimize operational costs |
|
|
|
Profit generation |
Exploring new revenue streams through
AI-enabled services |
|
|
|
Social benefits |
Training & Development of employees AI
skills |
|
|
|
Health and well-being of employees |
|
|
|
|
Work-life balance |
|
|
|
|
Ethical principles |
Transparent AI algorithms |
|
|
|
Data-secure AI algorithms |
|
|
|
|
|
TTSI |
|
|
5.2. Calculation of
TTSI
The study employs equal waiting for indicators.
It is possible to assign weights to indicators if policymakers or managers want
to emphasize the priorities of indicators.
The performance scores for each SI are used for
the calculation of TTSI as follows:
(1)
where:
SI
– a score of a sustainability indicator,
n – the number of indicators.
For weighted scores 
, the formula must be adjusted:
(2)
where
is the weight of SI.
The greater the TTSI achieved, the greater the
impact of AI on sustainability enhancement is in a transportation company.
The scores can be compared with targets
assigned by a manager of a transportation company to analyze the gaps and make
decisions on possible improvements. Setting a target for CO2
emissions (measured in kg) depends on the way of emissions calculation.
According to Gazzard et al. [8], different formulas are used considering the
data available (e.g., the total fuel used by all the vehicles, or total
distance travelled). The next step is to assign the target score, for example,
a score of 3 to the target of 3-5% emissions reduction after implementing AI
tools for transportation planning.
TTSI can be calculated for other new
technologies used in a company (for each technology the set of dimensions and
corresponding SIs should be identified as it is done for AI), and then the
overall TTSI can be calculated as an average of all TTSIs.
6. DISCUSSION
In this section, the authors discuss the
answers to the main research question and research sub-questions, the relevance
and implications of the findings for transportation, and address the study's
limitations and future research directions.
The literature research was done to answer the
research sub-question ‘How can AI enhance transportation sustainability?’. This
research examined various frameworks and models and identified the trends in
applying AI in transportation and its impact on sustainability. Various AI
algorithms used in transportation can significantly enhance sustainability. The
Smart Mobility Framework and Artificial
Neural Networks (ANNs) provide insights into the role of AI and
sustainability in transportation.
The literature and qualitative research helped
answer the second research sub-question ‘What dimensions and sustainability
indicators (SIs) can be used for the TTSI-based assessment?’. The authors
formulated dimensions and defined SIs for TTSI assessing the impact of AI on
transportation sustainability. The qualitative research based on interviews
with experts validated these dimensions and SIs. There were similarities in
answers to interview questions but also discrepancies. The discrepancies can be
explained by differences in companies’ business activities, levels of AI
adoption, and various backgrounds of respondents.
El Makhloufi [7] states that the Netherlands and
Western Europe are lagging in using AI compared to global leaders like China
and the USA. The results of the interviews partially confirmed this statement
but also showed significant stakeholders’ interest in AI. Experts also
expressed concerns about the difficulties of implementing AI tools due to the
need for computational resources and employees' competencies.
The literature and qualitative research were
used when answering the research sub-question ‘How can SIs be used for
calculating the Transportation Technology Sustainability Index?’ and the main
research question ‘How can the Transportation Technology Sustainability Index
be calculated and applied to assess the impact of AI on transportation
sustainability?’. The study suggests dimensions and SIs for the TTSI-based
assessment for measuring and monitoring the impact of AI on
sustainability. The authors showed how SIs are used for calculating TTSI
and decision-making in using AI in transportation and enhancing sustainability.
TTSI can also be used for understanding the
impact of other new technologies on a company's sustainable performance, so
further research might be needed to identify dimensions and SIs relevant to the
blockchain, Internet of Things, virtual reality, big data, or robotics.
The TTSI can be applied for internal and
external benchmarking. Large companies could compare departments or
subsidiaries to identify performance and improvement areas. External
benchmarking implies a comparison with other companies to gain insights beyond
the company's boundaries. Regulatory organizations can use the assessment to
understand the current state of the technology performance in transportation
and the impact of technologies on sustainability.
The findings are related to the current state
of the use of AI in transportation and assessing the impact of AI on
transportation sustainability. Since the number of AI applications rapidly
grows, there is a need for further research, like looking at new applications
of AI in transportation and adjusting the dimensions and indicators used in the
TTSI-based assessment.
The study focuses on several dimensions and
corresponding SIs for the TTSI-based assessment however there can also be some
other relevant dimensions (e.g., employee experience in the context of using
AI, or data privacy) and corresponding SIs so it might require additional
literature and field research.
The experts interviewed were from Dutch
companies, and the number of interviews was limited to six. Future research can
encompass interviewing experts from multiple countries to explore other
experiences and opinions concerning the use of AI and its impact on
sustainability.
7. CONCLUSION
The authors suggested TTSI to measure the
success of AI integration regarding transportation sustainability. The
TTSI-based assessment offers valuable insights for sustainable enhancement in
transportation and making informed decisions on AI deployment. Companies can
significantly benefit from AI applications enhancing sustainability. To
maximize AI's benefits, companies should focus on overcoming barriers,
collaborating with regulatory organizations, and investing in training programs
to ensure ethical and effective AI integration.
The literature and qualitative research showed
a lack of AI technology in the Dutch road freight transportation sector. The
study indicates that the implementation of AI notably impacts transportation
sustainability, and the companies should incorporate this technology to improve
sustainable performance and gain competitive advantages. The stakeholders can
use the TTSI-based assessment to monitor technological development and the
impact of AI (or other technologies) on sustainability. This assessment also
implies a comparison of performance scores with targets and supports
decision-making processes in a company. Benchmarking transportation companies
based on TTSI can help understand the current state of technological
development and its influence on sustainable performance. Future research could
provide more information about the use of technologies in transportation and
their impact on sustainability.
Acknowledgments
The
authors are grateful to Jan Jansen for the valuable feedback.
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Received 15.07.2024; accepted in revised form 10.10.2024
Scientific
Journal of Silesian University of Technology. Series Transport is licensed
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