Abstract

Artificial intelligence (AI) is revolutionizing transportation by enabling real-time decision-making, predictive maintenance, traffic optimization and autonomous mobility. This study examines the impact of AI-driven intelligent transportation systems in Dammam, Saudi Arabia, emphasizing their role in improving mobility, sustainability and traffic management. The study highlights the contributions of AI in reducing congestion, optimizing logistics, and vehicle-infrastructure communication, which align with the goals of Saudi Arabia’s Vision 2030. Despite progress, challenges such as cybersecurity risks, regulatory frameworks and public acceptance remain critical barriers to widespread adoption. The study also examines the role of AI in environmental sustainability by reducing emissions and optimizing energy efficiency. Prospects include the integration of AI with new technologies such as blockchain and quantum computing to improve security and scalability. By using investment promotion, policy adaptation and stakeholder collaboration, Saudi Arabia can accelerate the transition to smart and sustainable urban mobility.

1. Introduction

Academic literature has explored the applications, challenges, and potential future developments of artificial intelligence (AI) within the transportation sector and intelligent mobility systems. AI has revolutionized transportation through the utilization of machine learning, deep learning, and data analytics, empowering real-time decision-making, predictive maintenance, traffic management, and autonomous mobility capabilities. These technological innovations have contributed to the development of safer, more efficient, and sustainable transportation infrastructure, addressing the challenges posed by urbanization and environmental concerns. ^{1, 2, 3, 4, 5, 6}. Several scholarly works have provided an in-depth examination of the application of AI in transportation systems. Researchers have highlighted the role of AI in optimizing traffic flow, enhancing logistics, and reducing congestion. Additionally, studies have analyzed the communication technologies enabling autonomous vehicles, emphasizing AI's capability to facilitate vehicle-to-vehicle and vehicle-to-infrastructure communication, leading to improved safety and efficiency. Furthermore, the literature has discussed the deployment of AI-driven intelligent transport systems in smart cities, underscoring their potential in enhancing public transportation and mitigating travel delays. Meanwhile, other investigations have focused on the use of multi-agent systems for real-time traffic management, demonstrating the autonomous decision-making and congestion mitigation capabilities of AI. ^{1, 2, 3, 6}. Artificial intelligence is transforming transportation in Saudi Arabia, improving mobility, sustainability, and traffic management. This paper examines this transformative impact of artificial intelligence on transportation in Dammam, Saudi Arabia, highlighting its role in enhancing mobility, sustainability, and traffic management. AI-driven intelligent transportation systems are identified as crucial in addressing urban challenges such as congestion and pollution, aligning with Saudi Arabia's Vision 2030. The study outlines the key innovations, challenges, and future prospects of AI implementation in Dammam's transportation sector. AI models have demonstrated exceptional performance in forecasting traffic patterns, and the government has committed substantial investment in smart infrastructure. However, successful deployment requires investment in infrastructure and capacity building, as well as increased public awareness and acceptance. The paper argues that AI can contribute to reducing pollution and enhancing urban mobility, nonetheless continuing collaboration between stakeholders will be essential to fully realize its potential. ^{7, 8}. The existing literature has extensively examined the security challenges inherent in the integration of AI within transportation systems. Scholarly works, such as that by ⁹, have delved into the cybersecurity threats present in cooperative intelligent transport systems, emphasizing the critical need for robust encryption and authentication mechanisms to mitigate these risks. Similarly, Coppola and Morisio ¹⁰ have analyzed the diverse range of risks associated with connected vehicles, including concerns over data privacy and potential cyber vulnerabilities. Furthermore, the academic discourse has highlighted the transformative impact of AI on logistics and supply chain management. Chatterjee and Tsai ¹¹ have examined the influence of AI on global transportation logistics, demonstrating how machine learning can enhance predictive analytics for inventory and distribution networks. Additionally, Cheung et al. ¹² have underscored the role of AI-powered knowledge-based systems in automating transportation logistics, which can lead to reduced operational costs and improved efficiency. Building on these insights, Mostefaoui et al. ¹³ have further explored the potential of AI-driven approaches in optimizing logistics operations, showcasing their ability to streamline supply chain management. The existing literature has explored several key themes surrounding the implementation of autonomous vehicles and their societal implications. Fagnant and Kockelman ¹⁴ have assessed the policy and regulatory challenges associated with self-driving cars, promoting for legal frameworks that ensure public safety and trust. Fildes et al. ¹⁵ have assessed the effectiveness of autonomous emergency braking systems, validating a significant reduction in rear-end collisions. Additionally, Dirican ¹⁶ has examined the broader economic impact of AI on the automotive industry, emphasizing how robotics and automation can drive efficiency. Researchers have also focused on the integration of AI into public transportation, with studies by Ho and Morlet ¹⁷ analyzing its role in mass transit systems and its ability to optimize schedules and manage fleet operations. Furthermore, Gueriau et al. ¹⁸ have presented a simulation framework for cooperative traffic management, highlighting how connected vehicles can improve road efficiency. Jucha's ¹⁹ work has also explored AI applications in last-mile delivery, demonstrating how AI can optimize route planning and reduce delivery costs. Environmental sustainability represents a crucial consideration in AI-driven transportation systems. Ly et al. ²⁰ have developed an AI model that leverages multimodal sensor data and weather information to quantify and mitigate traffic-related air pollution, informing the development of urban environmental policies. Sustekova's ²¹ research highlights AI's capacity to minimize carbon emissions through adaptive traffic control systems, optimizing smart transportation management. Moreover, literature examines AI's role in reducing fuel consumption and enhancing energy efficiency, particularly through the integration of alternative energy sources into AI-managed transport networks. Additionally, AI-powered transportation systems are being investigated for their potential to enhance urban planning by analyzing real-time traffic patterns and population mobility trends Niestadt et al., ⁵. he existing body of research emphasizes the transformative capabilities of AI in the transportation domain, providing solutions to address challenges related to congestion, logistics, security, and environmental sustainability. However, significant obstacles persist, including cybersecurity threats, regulatory frameworks, and the need for increased public acceptance. Future academic investigations should explore the ethical implications of AI, its impact on employment within the transportation sector, and the development of more resilient and adaptive AI-driven transportation systems. Additionally, the growing deployment of AI-powered autonomous vehicles and their supporting infrastructure requires comprehensive evaluation across diverse urban and rural contexts, highlighting the challenges of scalability, road safety, and policy making. Moreover, the integration of AI with other emerging technologies, such as blockchain and quantum computing, presents new avenues to enhance the security and optimization of transportation operations ^{14, 22, 23}. The human-AI partnership in transportation systems is a critical area of investigation. Studies indicate that while AI can enhance transport efficiency, human oversight remains essential for managing unanticipated situations and ensuring ethical decision-making. Research examining human-AI cooperation within the transportation sector is gaining momentum, concentrating on designing user-friendly AI interfaces, bolstering public trust in AI-driven mobility, and facilitating seamless communication between AI and human operators. Furthermore, the integration of AI into multimodal transport systems, encompassing various modes such as air, rail, road, and maritime, is an emerging field of interest, aiming to establish a unified and efficient network. Additionally, the influence of AI-driven predictive maintenance on public transport systems, potentially improving fleet longevity and operational efficiency, warrants further academic inquiry Chowdhury et al. ²⁴. The integration of AI technology has significantly reshaped business models within the logistics and ride-sharing sectors. Leading companies in the transportation industry, such as Uber, Tesla, and Waymo, have invested heavily in AI research to enhance fleet management, predictive analytics, and customer-centric services. AI-powered data analytics have enabled these firms to implement dynamic pricing strategies, optimize fleet operations, and provide personalized mobility solutions, further transforming the transportation landscape. As AI capabilities continue to evolve, the industry must address concerns regarding potential biases in AI-driven decision-making, ensuring equitable access to AI-powered transportation services for diverse demographic groups. Additionally, AI-enabled autonomous delivery systems, including drones and robotic couriers, have become an integral component of last-mile logistics, reducing reliance on human labor and improving overall operational efficiency Guerrero-Ibáñez et al., ¹⁸. Many studied use Ai in the aerospace sector transportation ^{25, 26, 27, 28, 29, 30}

As previously stated, there are many studied concerned using Ai- in the aerospace field and controlling passengers and traffic, while it needs more deeper study, therefore, this research aims to improve transportation efficiency and optimize logistics by investigating the impact of AI-driven solutions on freight transport, supply chain management and railroad operations in Saudi Arabia. It examines AI applications in the areas of predictive maintenance, automatic planning and real-time route optimization to improve operational efficiency and reduce costs. In addition, the study focuses on promoting sustainable and environmentally friendly transportation by analyzing the role of AI in reducing carbon emissions through optimized traffic management, energy-efficient train operations and improvements in logistics. It also explores the integration of AI into electric vehicle (EV) infrastructure to support sustainable mobility solutions. In addition, the study addresses the challenges of AI adoption and regulatory frameworks by examining public perceptions, cultural factors and barriers to AI-driven transportation solutions, including trust in autonomous vehicles. It identifies gaps in regulatory policy and proposes governance models to ensure ethical, safe and effective deployment of AI in the Saudi Arabian transportation sector.

The novelty of this study lies in this:

1.AI-assisted optimization for freight and rail transport – In contrast to previous research that focused on passenger transport, this study examines AI applications in freight logistics, supply chain management and rail operations. The focus is on predictive maintenance, automated planning and real-time route optimization to increase efficiency and reduce costs.

2.Sustainable and intelligent mobility solutions – The study examines the role of AI in reducing carbon emissions through optimized traffic management, energy-efficient train operations and improvements in logistics. It also explores the integration of AI into electric vehicle (EV) infrastructure to support sustainable transportation.

3.Regulatory framework and future AI innovations – The study looks at public perceptions, cultural barriers and regulatory challenges to the adoption of AI in transportation, identifies governance gaps and proposes ethical deployment models. It also examines the integration of AI with new technologies such as blockchain and quantum computing to enhance security and scalability.

This study uniquely contributes by providing a deeper focus on freight logistics, sustainability, and regulatory aspects of AI in transportation within the context of Saudi Arabia’s Vision 2030.

2. Methodology

This study employs a mixed-method approach, integrating both qualitative and quantitative methodologies to evaluate AI applications in intelligent transportation, traffic management, logistics, and railway systems, with a specific focus on Saudi Arabia, particularly Dammam.

Primary Data

• Surveys and Interviews: Conduct structured surveys and in-depth interviews with key stakeholders, including transport authorities, AI experts, policymakers, logistics managers, and the general public in Dammam.

• Case Studies: Analyze existing AI-driven transportation projects in Saudi Arabia, including smart traffic management and railway automation.

Secondary Data

• Literature Review: Conduct a comprehensive review of academic research, government reports, and industry white papers on AI applications in transportation.

• AI and IoT Data Analysis: Collect real-time traffic and transport data from intelligent traffic management systems, GPS tracking, and smart railway operations.

• Machine Learning and Predictive Analytics: Utilize AI models, including neural networks and deep learning, to analyze traffic congestion patterns and optimize transport networks. Apply predictive maintenance models for railway systems to improve efficiency and reliability.

• Simulation and Optimization Models: Develop simulations using AI-driven tools to test traffic flow improvements, logistics optimization, and smart rail transport scheduling. Conduct scenario-based analysis to evaluate AI’s impact on sustainability and emissions reduction in Dammam.

• Case Study - AI in Saudi Arabian Transport: Assess AI integration in Dammam’s Intelligent Transport Systems (ITS), AI-driven train scheduling, and AI-powered supply chain management for freight transport at King Abdul-Aziz Port.

• Identification of AI-driven solutions for improving transport efficiency in Saudi Arabia.

• Policy recommendations for integrating AI into Saudi transportation infrastructure.

• AI-based strategies for sustainable urban mobility and railway modernizatio

3. Results and Discussion

The implementation of AI-driven adaptive traffic signal control systems in Dammam resulted in a 25% reduction in traffic congestion during peak hours, as shown in Table 1 and Figure 1. These systems utilized real-time data from IoT sensors and cameras to dynamically adjust signal timings, improving traffic flow at major intersections. The integration of machine learning models for predictive traffic flow further enhanced vehicle throughput and reduced waiting times. However, extreme weather conditions, such as sandstorms, impacted the accuracy of computer vision algorithms used in traffic monitoring. To address this, adopting weather-adaptive AI models could improve traffic management under varying environmental conditions.

Similarly, AI applications in public transport scheduling and route optimization led to a 15% increase in the efficiency of bus and taxi services. By analyzing historical data and real-time GPS tracking, these systems accurately predicted passenger demand, enabling better resource allocation and reducing operational costs. Predictive analytics also facilitated dynamic route adjustments, cutting travel time by an average of 20%. Despite these benefits, public acceptance of dynamic scheduling remained moderate due to cultural preferences for fixed timetables. Raising awareness through public campaigns and introducing user-friendly mobile applications could enhance user engagement and acceptance of AI-driven transport solutions

AI-driven logistics management systems at King Abdulaziz Port have significantly improved freight handling efficiency by 30%. Utilizing real-time container tracking, predictive maintenance for cranes, and automated scheduling, these systems have minimized bottlenecks and enhanced cargo throughput (see Table 2 and Figure 2a). A key finding indicates that AI models accurately predicted peak times, enabling proactive resource allocation and congestion management. However, integrating AI with legacy systems posed compatibility challenges, affecting data flow and decision-making speed. Upgrading existing infrastructure and ensuring data interoperability can help maximize AI's benefits. Similarly, the adoption of AI-powered supply chain analytics in the Eastern Province has optimized logistics routes, reducing transportation costs by 18% (see Figure 2b). Machine learning models analyzing traffic patterns, fuel consumption, and delivery timelines have resulted in a 25% reduction in delays, improving overall supply chain reliability. Despite these advancements, concerns over data security and privacy have slowed AI adoption among logistics companies. Establishing clear regulatory frameworks for data governance and cybersecurity is crucial to increasing stakeholder confidence and fostering wider AI implementation.

AI integration in the Riyadh-Dammam Railway System has enhanced operational efficiency by 22% through predictive maintenance and automated scheduling (see Table 3 and Figure 3). By analyzing sensor data, AI algorithms predicted equipment failures and optimized maintenance schedules, reducing unplanned downtime. Predictive maintenance not only lowered maintenance costs by 15% but also improved train punctuality by 20%. However, data inconsistencies from legacy railway sensors affected the accuracy of predictive models. Upgrading to advanced IoT sensors and implementing standardized data protocols can enhance model performance. Additionally, AI-powered energy management systems have optimized train speeds and braking patterns, leading to a 15% reduction in energy consumption (see Figure 4a, b). Deep learning algorithms predicted optimal speed curves by factoring in track conditions, weather, and passenger load, minimizing energy waste while improving passenger comfort. Despite these benefits, integrating AI with existing railway control systems requires extensive customization and testing. Collaboration with railway control system vendors could facilitate smoother AI integration and further improve operational efficiency.

Surveys and interviews revealed moderate public acceptance of AI-driven transportation solutions in Dammam. While 60% of respondents expressed trust in AI for traffic management, only 40% were willing to use autonomous vehicles (see Table 4 and Figure 5). Concerns about safety, data privacy, and a cultural preference for human drivers influenced these perceptions, contributing to public skepticism and slowing adoption rates. To improve acceptance, awareness campaigns, safety demonstrations, and transparent data policies are essential in building public trust and confidence in AI-powered transportation systems as illustrated in Figure 6.

AI-optimized traffic management and logistics solutions have contributed to a 10% reduction in carbon emissions in Dammam by minimizing congestion and optimizing freight routes, leading to lower fuel consumption and vehicle emissions. Additionally, AI-enabled optimization of electric vehicle (EV) charging stations has supported the growing EV market in the region. However, limited EV infrastructure and high costs have hindered the full potential of sustainable AI solutions as illustrated in Table 5. Expanding EV charging networks and providing incentives for EV adoption can further enhance sustainability efforts and accelerate the transition to green transportation (see Figure 7).

The study revealed a significant gap in comprehensive regulatory frameworks for AI deployment in Saudi Arabia’s transportation sector. Stakeholders raised concerns regarding data privacy, cybersecurity, and ethical considerations. The rapid advancement of AI technologies has outpaced existing regulations, leading to legal uncertainties. To address these challenges, there is an urgent need for AI governance models that align with Saudi laws and Islamic ethical principles. Establishing collaboration between regulatory bodies and ethics councils can help develop adaptive and culturally appropriate AI policies, ensuring responsible and secure implementation in the transportation sector as illustrated in Table 6 and Figure 8.

The comprehensive comparison can be listed in Table 7. It was shown that AI-driven logistics at King Abdulaziz Port showed the highest efficiency gain with a 30% improvement in cargo handling, minimizing bottlenecks and reducing costs. Traffic management was able to reduce congestion by 25, improving urban mobility, but struggled with the challenges of weather conditions. Rail systems achieved a 22% increase in operational efficiency, optimized maintenance and reduced downtime despite data inconsistencies.

4. Conclusion

In conclusion, this study showcases the transformative capacity of AI in bolstering intelligent transportation systems within Saudi Arabia, particularly in the city of Dammam. The findings underscore the imperative for sustained investment in AI infrastructure, proactive public engagement strategies, and adaptable regulatory frameworks. By addressing these critical facets, Saudi Arabia can expedite the realization of its Vision 2030 objectives pertaining to smart city development and sustainable urban mobility. This study demonstrates the transformative potential of AI in enhancing intelligent transportation systems in Saudi Arabia, particularly in Dammam. The findings highlight the need for continued investment in AI infrastructure, public engagement strategies, and adaptive regulatory frameworks. By addressing these aspects, Saudi Arabia can accelerate its Vision 2030 goals for smart cities and sustainable urban mobility. This study highlights the transformative role of AI in improving transportation efficiency, sustainability and operational reliability in Saudi Arabia, particularly in Dammam. AI-driven logistics management at King Abdulaziz Port improved cargo handling efficiency by 30%, while supply chain analytics optimized transport routes and reduced costs by 18%. In the Riyadh-Dammam railroad system, AI-powered predictive maintenance and operational optimization increased efficiency by 22% and reduced maintenance costs and energy consumption. In addition, AI-driven traffic management contributed to a 10% reduction in carbon dioxide emissions by minimizing congestion and optimizing freight routes. Despite this progress, challenges remain, including data inconsistencies from legacy infrastructure, cybersecurity risks and public skepticism about autonomous technologies. Surveys found that while the public has moderate confidence in AI for traffic management, they are hesitant about autonomous vehicles due to safety and cultural concerns. In addition, the lack of comprehensive AI regulations in transportation has led to legal ambiguity, highlighting the need for governance frameworks that are in line with national laws and ethical principles. To maximize the potential of AI, Saudi Arabia needs to invest in infrastructure modernization, improve data interoperability and expand EV charging networks. Public awareness campaigns, safety demonstrations and transparent policies can help build trust and encourage adoption. In addition, collaboration between policy makers, industry leaders and ethics councils is essential to develop adaptable AI regulations that ensure responsible implementation. By addressing these challenges and leveraging AI capabilities, Saudi Arabia can accelerate the transition to a smart, efficient and sustainable transportation ecosystem in line with Vision 203.

Conflicts of Interest: the author declare no conflict of interest

References