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American Journal of  Smart 
Technology and Solutions (AJSTS)

Smart Transportation Systems with Artificial Intelligence: Enhancing Efficiency, Safety, 
and Sustainability 

Abdullah Sheikh1*, Md. Shakil Sheikh2, Tajbiha Mehonaj Rinvee3

Volume 4 Issue 2, Year 2025
ISSN: 2837-0295 (Online)

DOI: https://doi.org/10.54536/ajsts.v4i2.6022
https://journals.e-palli.com/home/index.php/ajsts

Article Information ABSTRACT

Received: August 27, 2025

Accepted: October 03, 2025

Published: November 08, 2025

Artificial intelligence (AI) is transforming transportation, yet most research and applications 
focus on isolated improvements, lacking a unified approach that connects operational gains 
with strategic national goals. This paper addresses this gap by developing a conceptual 
framework that synthesizes how AI enhances transportation systems across three integrated 
pillars: efficiency, safety, and sustainability. Through a synthesis of  recent literature and 
industry case studies, we propose a model that demonstrates the synergistic effects of  
AI applications, such as predictive maintenance and dynamic routing. The framework’s 
primary contribution is to illustrate how these technological advancements collectively 
bolster U.S. competitiveness by building resilient supply chains, reducing emissions, and 
fostering leadership in sustainable innovation. This study provides a structured roadmap for 
policymakers and industry leaders to leverage AI not merely for operational efficiency, but 
as a strategic asset for long-term economic security.

Keywords
Artificial Intelligence, Efficiency, 
Logistics, Safety, Smart 
Transportation, Sustainability, 
U.S. Competitiveness

1 Wright State University, Dayton, Ohio, USA
2  Atish Dipankar University of  Science & Technology, Bangladesh
3  BRAC University, Bangladesh
*Corresponding author’s e-mail: adustabdullah@gmail.com

INTRODUCTION
Transportation is the backbone of  modern economies 
and defines the movement of  goods, services and 
people in regions and markets. In the United States, 
the transportation sector supports millions of  jobs and 
contributes greatly to economic growth. At the same time, 
it is one of  the largest sources of  carbon emissions and 
faces growing challenges of  congestion, safety and rising 
operational costs. These pressures underscore the urgent 
need for intelligent and sustainable transport systems. 
Artificial intelligence (AI) is a central tool for solving 
these challenges. Unlike traditional optimization 
techniques, AI processes large amounts of  real-time 
data, detects complex patterns, and generates adaptive 
solutions. Applications range from predicting traffic 
flows and adapting flight routes, improving fleet fuel 
efficiency, and supporting autonomous vehicle systems. 
With this combination of  efficiency and adaptability, AI 
is a powerful enabler for change in the transport sector. 
The relevance of  smart transportation goes beyond 
the operation. It has a strategic impact on national 
competitiveness and resilience. Countries successfully 
integrating artificial intelligence into their transportation 
systems are better equipped to reduce costs, reduce 
emissions and maintain reliability in the face of  disasters 
such as pandemics, natural disasters or geopolitical 
tensions. For the United States, which is heavily dependent 
on timely transportation of  goods through its logistics 
network, intelligent transportation with AI is not only a 
technological innovation, but also a matter of  long-term 
economic security.
This paper focuses on how AI can be applied to improve 
three basic outcomes in transport: efficiency, safety and 
sustainability. Efficiency means reducing delays, fuel 

consumption, and waste through predictive analytics 
and real-time optimization. Safety is accompanied by 
computer vision systems, driver monitoring and predictive 
maintenance with AI. Sustainable development is achieved 
through reduction in carbon emissions, optimization 
of  energy use and support for the transition to greener 
transport modes. These three dimensions together form 
a framework for intelligent transportation driven by AI, 
supporting the competitiveness and leadership of  the 
United States in sustainable innovation. 
The remainder of  the paper is as follows. In the second 
section, the literature on AI applications in intelligent 
transport is reviewed, and both progress and current 
gaps are highlighted. Section 3 introduces the conceptual 
framework and methodology. Section 4 presents case 
studies by industrial leaders. Chapter 5 deals with the 
impact on the competitiveness of  the United States. 
Finally, section 6 concludes with a summary of  the 
findings and recommendations of  future research.

LITERATURE REVIEW
Evolution of  Smart Transportation & ITS
Smart transportation systems (STSs) and intelligent 
transportation systems (ITS) have evolved over the past 
two decades. ITS integrates perception, communication, 
calculation and control to improve transport network 
mobility, safety and environmental performance. 
Zemmouchi-Ghomari et al. (2025) Review how AI is 
integrated into ITS to support traffic flows, safety and 
sustainability in urban areas.
Recent research has also highlighted the influence of  
generational AI within ITS. For example, Rong and others. 
(2025) Examine applications such as data generation, 
prediction and decision-making in ITS subsystems.



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In recent research, sustainability and efficiency have 
become increasingly closely linked. Son et al. (2025) 
conducts a systematic review to demonstrate how AI, 
IoT, digital twins and optimization methods are used in 
intelligent transportation planning, highlighting important 
improvements in traffic flows and emission reductions.

AI Methods Applied in Transportation
AI methods used in transportation are diverse. They 
include:

• Machine Learning (ML) and Deep Learning: For 
traffic prediction, demand forecasting, incident detection, 
and routing decisions.

• Reinforcement Learning (RL): It is used to adapt 
control strategies dynamically (e.g. traffic signal control, 
dynamic routing). Li et al.’s bibliographic review. (2022) 
shows that RL is increasingly attractive in transportation 
applications.

• Generative Models/Generative AI: Emerging for 
tasks such as creating synthetic data, simulation of  
scenarios, or improving prediction under data-sparity 
conditions (for example, Yan and Li, 2023).

• Hybrid Approaches: Combining AI with IoT sensors, 
blockchain and optimization. Idrissi et al. (2024) Examine 
how IoT, IoT, and Blockchain interplay in logistics and 
transport, and improve the availability, resilience, and 
support for decision-making. 

Key Application Areas & Findings
AI in smart transportation is applied to many problem 
domains. Here are several with findings:

• Traffic prediction & routing: Many studies show 
that AI is more powerful than traditional models (such 
as ARIMA and linear regression), especially in nonlinear 
and volatile traffic environments.

• Safety & accident prediction: Computer vision and 
sensor-based ML detect risky driving behaviors or predict 
likely crash spots.

• Predictive maintenance: Using sensor data and ML 
to anticipate vehicle or infrastructure failures before they 
happen.

• Energy optimization & emissions: AI optimizes route 
planning, load balancing, and speed profiles to reduce 
fuel use and emissions.

• Mobility-as-a-Service (MaaS): Rouky et al. (2025) 
Examine how AI supports integrated mobility 
systems (routes, payments, user behaviors) and link AI 
technologies to integrated levels.

Challenges, Gaps, and Research Needs
Despite progress, several gaps and challenges persist:

• Data quality and heterogeneity: Many models struggle 
when input data are missing, noisy, or inconsistent.

• Real-time requirements: AI models must be fast and 
efficient to operate in real-time constraints.

• Scalability: Many prior works focus on small regions 
or simulated settings; scaling to national networks is 
harder.

• Integration across systems: Too often, applications 
are isolated (only traffic or routing). Few studies integrate 
safety + emissions + efficiency within a single framework.

• Ethical, privacy, and security concerns: Use of  AI in 
transportation brings risk of  surveillance, data breaches, 
fairness issues.

• Lack of  longitudinal studies: Few works track AI 
performance over long time or in real operations (versus 
simulations).

How This Paper Contributes
Our work addresses these gaps. We propose a unified 
framework for efficiency, safety, and sustainability in 
intelligent transportation. Unlike many previous studies, 
we emphasize the coherence of  the real world over the 
application domain rather than isolated models. It is 
also intended to highlight the strategic implications for 
US competitiveness, which are under-emphasized in the 
current literature.

MATERIALS AND METHODS
This paper uses conceptual and exploratory methods 
rather than empirical methods. The objective is to develop 
a framework to link artificial intelligence (AI) to intelligent 
transportation, efficiency, safety and sustainability, while 
focusing on national competitiveness.

Research Approach
The methodology is based on comparative synthesis of  
recent studies, reports and industry practices. Instead 
of  analyzing a single dataset, we reviewed academic 
and industrial literature published in the fields of  
transportation, logistics and AI between 2018 and 2025. 
The aim was to identify repeated applications, evaluate 
their results and organize them into coherent models of  
smart transportation.

Data Sources
The source of  this study is: 

• Articles reviewed by peer-reviewed professionals on 
AI in transportation, logistics and sustainability. Industry 
reports from major transport companies such as Amazon, 
UPS, Uber Freight and Tesla.

• Policy documents from American and international 
organizations relating to intelligent cities, clean 
transportation and digital infrastructure.
Using various sources ensures that the framework is 
based on both academic theory and practical evidence.

Framework Development
The framework was developed in three steps:
1. Mapping Applications: Key applications of  AI in 
transportation, such as traffic forecasting, routing, 
predictive maintenance, safety, energy optimization, and 
emissions monitoring, were identified and categorized.
2. Identifying Outcomes: For each application, the dual 
outcomes were noted: efficiency gains (reduced costs and 
faster operations) and sustainability benefits (reduced 



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emissions and improved safety).
3. Linking to Competitiveness: Finally, we linked these 
outcomes to national competitiveness by arguing that 
countries with stronger AI-driven transportation systems 
gain resilience and leadership in the global economy.

Scope and Limitations
This is a conceptual study, not an empirical one. While it 
provides a broad synthesis, it does not include primary 
data collection or quantitative testing. However, the paper 
offers a roadmap for both researchers and policymakers 
by organizing existing knowledge into a unified model.

The Proposed Framework for AI in Smart 
Transportation
This section presents the framework for demonstrating 
how artificial intelligence supports the efficiency, safety 
and sustainability of  smart transportation systems. The 
framework is based on three pillars: operational efficiency, 
road safety and environmental sustainability. These pillars 
together strengthen the long-term competitiveness of  the 
country.

Figure 1: Conceptual Framework “AI in Smart 
Transportation”

This figure illustrates how AI applications strengthen 
efficiency, safety, and sustainability, which together 
reinforce U.S. competitiveness.

Efficiency through AI
AI reduces inefficiency across transport networks. 
Analyzing real-time traffic data, AI systems adjust signal 
timing, redirect vehicles, and predict traffic congestion 
before it occurs. In the logistics sector, platforms such as 
Uber Freight and Amazon’s routing system allocate trucks 
more effectively, reducing empty miles and reducing 
costs. Predictive maintenance also supports efficiency. 
Machine learning models monitor vehicle health and 
forecast component failures, allowing rapid repairs and 
reducing shutdown times.

Safety Improvements
Safety is the second pillar of  the framework. AI-enabled 
systems, such as driver assist technology, Lanekeeper 

system, and automatic brake, reduce the risk of  accidents. 
AI can also process real-time road, weather and driver 
data and emit warnings before the situation escalates. 
For example, predictive analytics can identify dangerous 
driving patterns and trigger alerts or training interventions. 
These applications not only save lives but also reduce the 
costs of  accidents financially and socially.

Sustainability and Emission Reduction
Artificial intelligence directly contributes to sustainable 
development by reducing energy consumption and 
emissions. Dynamic routing reduces fuel consumption by 
selecting the most efficient path. Electric vehicle fleets 
can be managed by AI to forecast charging requirements 
and align them with renewable energy supply. Companies 
like Walmart and UPS already use artificial intelligence to 
reduce energy consumption in warehouses and optimize 
fleet management to reduce emissions. At the city level, 
intelligent traffic lights based on AI reduce idle time and 
cause measurable carbon production reductions.

Linking to Competitiveness
Combining the three pillars of  efficiency, safety, and 
sustainability creates a competitive advantage. The nation 
and company adopting AI in transport gains resilience 
to disruption, better compliance with environmental 
standards and more reliable supply chains. For the United 
States, this not only means reducing costs, but also enables 
sustainable innovation, develops exportable technologies 
and strengthens its position in the global market.

RESULTS AND DISCUSSION
The proposed framework emphasizes that artificial 
intelligence is not a set of  digital tools. It is a strategic 
driver of  efficient, safe and sustainable transport. By 
combining these pillars, artificial intelligence offers a path 
to strengthening national competitiveness. 
From the business point of  view, the framework shows 
that artificial intelligence investments can create double 
value. Companies benefit financially by reducing costs 
and delays, while improving social results such as accident 
reduction and emission control. This dual impact makes 
it easier to justify AI adoption to stakeholders.
From a policy point of  view, the framework provides a 
roadmap for governments to develop regulations and 
incentives to support them. For example, subsidies to 
electric vehicles equipped with artificial intelligence can 
reduce emissions, while data sharing policies can improve 
traffic management throughout cities. Policy makers 
can also encourage public and private partnerships to 
accelerate the deployment of  AI solutions.
From a research point of  view, the framework identifies 
gaps in future research. It is necessary to measure the 
long-term impact of  the adoption of  artificial intelligence, 
particularly how efficiency is balanced with sustainable 
outcomes. More empirical research is also needed on the 
social effects of  AI-based safety systems, especially in 
areas with high incidence of  accidents. 



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Overall, the implications of  this framework show that 
leading companies and countries in the field of  AI-
enabled transportation will not only reduce costs and 
risks, but also become leaders in sustainable innovation. 
For the United States, this offers the opportunity to 
strengthen resilience, maintain technological leadership 
and enhance global competitiveness. 
Over the next decade, the most important research 
needs will be the testing of  AI models not only for their 
efficiency, but also for their impact on fairness and long-
term sustainability, particularly in the transportation 
networks of  the United States.

CONCLUSION
This paper developed a conceptual framework establishing 
AI as a catalyst for integrated gains in transportation 
efficiency, safety, and sustainability. The key insight is that 
the synergistic effect of  these pillars is crucial for building 
a resilient and competitive national transportation system. 
For U.S. competitiveness, this means policymakers should 
prioritize funding for integrated AI projects that cross 
environmental and operational agencies. Industry leaders 
must move beyond point solutions and adopt platforms 
that unify these three objectives. For researchers, the 
critical path is to generate empirical evidence on the 
framework’s synergies in real-world settings and to 
establish standards for ethical AI deployment. Ultimately, 
strategic adoption of  this unified AI framework is not 
optional but essential for the U.S. to secure economic 
strength and global leadership in the future of  mobility.
 
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