Intelligent Transportation Systems in Industry 5.0

Aim. Threats of a non-physical nature have a significant effect on the security of intelligent transportation systems (ITS). They may have the form of unlawful interference in the development and implementation of ITS. The creation of a trusted framework for the development and implementation of ITS is examined as in the case of intelligent water transportation systems (IWTS). Problems. By decree of the President of the Russian Federation, ITS, telecommunications and security of information processing are among the priority areas of scientific and technological development. ITS technologies, as well as those involved in the creation of trusted, secure system and application software are among the most important critical high technologies. The operation of ITS involves wide use of computerised systems that implement the latest information and telecommunication technologies, automated and automatic control technologies, artificial intelligence that can pose security threats. ITS are to be developed and operated in a trusted environment. Methods. The paper used the methodology for ensuring the security of IWTS, development of secure hardware and software platforms for secure automated systems, methods of system analysis, dependability theory, information protection, and law. Results. The paper defines the problem of creating a trusted framework for the development and implementation of IWTS, the applicable terminology is developed. The author examined the effect of IWTS on the security of critical information infrastructure (CII) and national security, developed a model of relationships between the IWTS security domains taking into account threats of physical and non-physical origin. Examples of computer incidents within IWTS that caused consequences at the national and international levels are given. The composition of the IWTS facilities attributed to CII is defined, critical processes implemented by standard CII facilities as part of the IWTS are set forth. The author lists conceptual problems of IWTS security, defines the principles of creating a trusted framework for the development and implementation of IWTS. Conclusion. Ensuring the security of IWTS against modern threats requires solving a number of problems associated with the creation of a trusted framework for the development and implementation of IWTS. For the purpose of improving the timeliness and quality of their solution, the paper proposes an intuitive terminology that reflects the subject area and helps finding a common understanding of the security domain by experts from various industries. IWTS facilities have an effect on CII security and national security in general. That is taken into account in the model of relationships between IWTS security domains, demonstrated using cases of computer incidents within IWTS that caused consequences at the national and international level. Given the above, the paper lists IWTS facilities attributed to CII and sets forth examples of critical processes implemented by standard CII facilities as part of the IWTS. The defined list of conceptual IWTS security problems take into account the growing landscape of IWTS security threats that includes insecure software, hardware and software platforms, software and hardware systems and emerging technologies. When developing the principles for the creation of a trusted IWTS development and implementation framework, the author took into account the best practice of implementing the methodology for creating national secure hardware and software platforms of CII facilities that enables the creation of secure automated systems for various applications that are based on domestically-developed solutions. The examined matters are of a systemic nature, which allows using the findings in the development and implementation of ITS in other modes of transportation.

Purpose Developments regarding the use of artificial intelligence (AI) in transportation systems, one of the important stakeholders of tourism, are remarkable. However, no review thus far has provided a comprehensive overview of research on AI in transportation systems.Design/methodology/approach To fill this gap, this study uses the VOSviewer software to present a bibliometric review of the current scientific literature in the field of AI-related tourism research. The theme of AI in transportation systems was explored in the Web of Science database.Findings The original search yielded 642 documents, which were then filtered by parameters. For publications related to AI in transportation systems, the most cited documents, leading authors, productive countries, co-occurrence analysis of keywords and bibliographic matching of documents were examined. This report shows that there has been a recent increase in research on AI in transport systems. However, there is only one study on tourism. The country that contributed the most is China with 298 studies. The most used keyword in the documents was intelligent transportation system.Originality/value The bibliometric analysis of the existing work provided a valuable and seminal reference for researchers and practitioners in AI-related in transportation system.

Machine Learning (ML) has rapidly emerged as a vital component of Artificial Intelligence (AI), transforming various sectors through predictive models, pattern recognition, and data-driven insights. This article examines the foundational theories underlying ML, key practical applications in high-impact industries, preserving ethical and societal considerations, and future trends. The authors explore how classical supervised and unsupervised learning methods have evolved to include sophisticated techniques such as deep learning, transfer learning, and reinforcement learning. Critically, they also evaluate the significant ethical challenges in algorithmic fairness, transparency, and data privacy alongside the growing concerns of ML's environmental impact and the digital divide. Finally, they discuss the trajectory of ML, touching on quantum computing, federated learning, Edge AI, and lifelong learning as promising avenues for future research that aim to create more robust, equitable, and sustainable systems

The estimation of the time needed for a vehicle to reach a specific destination is one of the main focuses of navigation and Intelligent Transport Systems (ITS) as it helps both transit users and transit providers. Travel time estimation helps transportation providers to gain insight into evaluating travel routes, hence enhancing the transportation system reliability of their systems for transport users. In addition, travel time estimation helps in reducing the anxiety and stress for the travelers. Moreover, real time traffic data extremely impacts travel time estimation. Consequently, finding an accurate model for real time travel estimation is very crucial. Machine learning (ML) and its branch deep learning have proven to be efficient techniques to address this problem. Although there exists multiple ML models that estimate travel time, they are mainly offline models and they are fixed in size. Consequently, finding an adaptive online ML model is a vital task for real time travel estimation. This paper focuses comparing two adaptive online ML algorithms that operate in dynamic environment, namely multi-layer perceptron with hedge backpropagation and the greedy layer-wise pretraining. This paper shows that MLP with hedge backpropagation outperforms the greedy layer-wise pretraining algorithm. The mean square error percentages for MLP with hedge backpropagation and greedy layer-wise pretraining algorithm are reported to have values of 4.52% and 6.32%, respectively.

The concept of smart transportation systems is increasingly becoming critical in addressing the challenges posed by an increasing number of mega cities in both developed and developing regions in maintaining safety, smooth traffic flow, and an environmentally friendly and sustainable urban environment. The Government of Zimbabwe deregulated its transport sector in the early 1990s. This development ushered in the informal public transport operators, locally referred to as ‘kombis'. Major cities such as Harare are characterised by a disjointed and chaotic urban public transport system. The major problems are the impacts this has on the quality of the environment. Currently, the urban public transport system is contributing greatly to both air and noise pollution within the confines of the city, especially in the Central Business Districts. The problem is further exacerbated by the massive importation of used vehicles from outside the country. These developments have resulted in high emission rates of major air pollutants resulting in a deterioration of the ambient air quality especially in the major cities such as Harare. Transportation is a major source of air pollutants. Vehicles are probably the largest single source of pollutants such as hydrocarbons, nitrogen dioxide and carbon monoxide. Other harmful emissions include as lead, benzene, arsenic, aldehydes, sulphates, particulate matter and the secondary creation of ozone. In Harare the number of registered vehicles increased from 192 901 in 1994 to 292 862 and by August 1999 showing that the increase in the number of vehicles is mostly in the cities. This chapter seeks to explore how smart transportation system can be adopted in Harare, the capital city of Zimbabwe. Harare was purposefully selected as it is experiencing rapid urbanization and motorization in the country. Using documentary analysis, discourse analysis and textual analysis, the chapter also describes and examines the challenges, constraints and opportunities of adopting smart urban transportation system in Harare. From this chapter the major conclusions are that the main problem associated with this rapid growth in vehicle population in the major cities is increase concentration of line and area emission sources due to traffic congestion at peak times. The stock of vehicles is quite old and they lack emission control equipment. Major constraints and limitations are observed in the current pieces of legislation. For example, the current Atmospheric Pollution Prevention Act (1971) does not require vehicles to be fitted with emission control equipment. Most of the vehicles use leaded fuel resulting in emission of the dangerous lead particulate matter in the urban areas. There is therefore an urgent need to design and implement air pollution control measures in the urban areas of the country. It is also critical to develop smart and eco-friendly transportation infrastructures so as to achieve sustainable urban communities. The integration of transportation, land use and decision making is also critical in the achievement of smart transport.

Artificial Intelligence (AI) and Big Data Analytics (BDA) are increasingly recognized as transformative forces that are reshaping industries, governance, and human society in profound ways. AI, with its foundations in machine learning, deep learning, natural language processing, and predictive modelling, requires vast amounts of data to train algorithms and generate reliable insights. Complementing this, BDA provides the scale, diversity, and velocity of data processing required to uncover hidden patterns, correlations, and trends within complex datasets. Together, AI and BDA form a synergistic paradigm in which Big Data serves as the fuel and AI operates as the engine, driving intelligent, data-driven innovation across multiple domains. Globally, AI-BDA applications are enabling predictive diagnostics, telemedicine platforms, genomic-driven personalized treatments, and advanced medical imaging, thereby enhancing both the accessibility and quality of care. In India, where the healthcare system must serve more than 1.4 billion citizens with limited resources, these technologies can play a pivotal role in addressing systemic challenges such as inadequate rural access, shortages of medical professionals, real-time disease surveillance, and hospital resource optimization. International best practices, including the NHS AI Lab in the United Kingdom, Mayo Clinic initiatives in the United States, and Ping a Good Doctor in China, provide models that India can adapt and scale to its unique demographic and infrastructural context. Despite their vast potential, the adoption of AI and BDA faces several barriers. Data privacy and cybersecurity remain critical concerns, particularly with the emergence of new regulations and the sensitive nature of health-related information. Additionally, algorithmic bias and the uneven availability of digital infrastructure could exacerbate existing inequities if not adequately addressed. Looking forward, advancements in Edge AI, Explainable AI (XAI), and quantum-enhanced analytics are expected to extend the scope and reliability of AI-BDA systems. This paper argues that the integration of AI and BDA is not merely a technological advancement but a paradigm shifts in how societies innovate, make decisions, and deliver essential services. Specifically, it highlights the need for a human-centric, ethical, and affordable approach to healthcare innovation in India in order to unlock the full transformative potential of these technologies. Keywords: Artificial Intelligence, Big Data Analytics, Healthcare Innovation, Predictive Diagnostics, Ethical AI, Edge AI.

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Following the newly adopted European Directive on Intelligent Transport Systems (ITS) 2010/40/EU, officially published on November 30th 2023 under the new code (EU) 2023/2661, opportunities arise for the entire industry. The main aim of the Directive is to promote coordinated deployment of intelligent transport systems in accordance with technological development and user needs. It provides a framework for developing and implementing technologies to improve transportation systems' efficiency, safety, and sustainability within the European Union (EU) and member states. The new European legislation should ensure that ITS applications in the field of road transport enable seamless integration with other modes of transport thus facilitating a shift to those modes whenever possible. This will in time improve the safety, efficiency and accessibility of the European transportation system. In this paper, we are analyzing the main objectives of the EU initiative and changes compared to the initial Directive of 2010 with special regard to changes in Priority III, safety and Priority IV, ITS services for connecting, cooperation and automatization of mobility. Although all key changes are analysed the special focus was on critical services such as road safety-related traffic information services, to be made available across Europe.

In recent years, there has been increased interest among both transportation researchers and practitioners in exploring the feasibility of applying artificial intelligence (AI) paradigms to address some of the transport problems in order to improve the efficiency, safety, and environmental-compatibility of transportation systems. The contribution is to highlight the application of tools and methods in the field of artificial intelligence solutions to traffic problems, where using traditional solutions would be time consuming and more expensive. This article deals with the application of selected artificial intelligence methods in case of transport and intelligent transport systems.

The Intelligent Transportation System (ITS) is the effective and comprehensive application of advanced science and technology (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) in transportation, service control, and vehicle manufacturing to strengthen the connection between vehicles, roads, and users. The result is an integrated transport system that ensures safety, enhances efficiency, improves the environment and saves energy. It is an effective means to solve the current urban traffic problems and an important indicator for the construction of a "smart city". Through the construction of an urban traffic collection system, the system solves the existing traffic problems, further integrates all kinds of road traffic resources, builds the urban "traffic brain", mines the huge amount of traffic data, deconstructs the operation law and characteristics of urban traffic, realizes the accurate urban traffic management, scientific management and normal management, and makes people's traffic travel safer. It makes people's traffic travel safer, more convenient and more efficient. This study introduces innovative computational models and algorithms specifically designed for traffic optimisation, as well as data-driven machine learning techniques to improve the operational efficiency of intelligent transport systems. An extensive analysis of case studies on the integration of AI into traffic management systems provides empirical evidence of their impact, focusing on the performance evaluation of these AI applications as well as potential scalability issues that may arise in different urban environments.

Artificial Intelligence (AI) has emerged as a transformative force across various domains, with its potential to revolutionize urban architecture gaining increasing recognition. This paper offers a detailed examination of AI's application in the construction of public buildings, emphasizing its achievements, challenges, and future outlook. The review spans all facets of civil engineering, including review processes, analysis, design, construction management, geotechnical engineering, transportation planning, and construction oversight. AI methods, such as machine learning and genetic algorithms, are employed in analysis and design to enhance processes, forecast material behavior, and advance healthcare applications. In construction management, AI is utilized for project scheduling, resource distribution, risk evaluation, and safety management. Geotechnical applications of AI provide precise soil property estimation, soil damage assessment, and foundation construction improvements. Advanced technologies aid in transportation planning, traffic prediction, intelligent transportation systems, and infrastructure enhancements. Additionally, AI plays a crucial role in monitoring and maintaining public infrastructure, including bridge inspections, pipeline integrity evaluations, and early defect detection through image processing and data analysis. Despite significant advancements, challenges persist regarding AI's widespread adoption in civil engineering, including data availability, AI model definitions, ethical issues, and the necessity for collaborative efforts. Addressing these challenges will require the joint efforts of researchers, practitioners, and policymakers. Ultimately, AI's integration into civil engineering demonstrates its potential to enhance the efficiency, safety, and sustainability of infrastructure systems. This review summarizes the current knowledge, highlights challenges, and proposes directions for future research to advance AI integration in civil engineering.

Topicality. The scientific article is devoted to the research analysis of the peculiarities of legal regulation and the construction of the architecture of the intellectualization of transport systems. Transport communications unite companies around the world and are a necessary prerequisite for the integrity of the country and its integration into the world economy through foreign economic activity. In this regard, the transport system is directly related to the economy and the social sphere. The transport policy of any state is characterized by national priorities at the micro and macro levels. A competitive transport system must have the following characteristics: be economically, ecologically and socially sustainable; to be able to recover effectively after natural disasters; have a developed and reliable IT infrastructure.The use of information systems in traffic management is associated with the rapid development of modern cities, an increase in the number of rolling stocks on the streets, new routes and other features. As a result, the amount of information needed for analysis and making operational decisions has increased significantly. The information component will become an integral part of city life. And for more effective use of all the possibilities of intelligent transport systems (ITS), legal regulation is necessary, which includes data protection, standardization, rules for the application of ITS achievements and their further development.Aim and tasks. The purpose of the research is to study the legal foundations of the concept of transport and logistics systems sustainable development.Materials and Methods. The research methodology is based on the analysis of legislative and legal methods of ensuring transport and logistics flows, economic and legal analysis using the general legal framework and special legal acts that regulate the activity of transport and logistics systems. The problem of intellectualization of environmental protection activities is considered both through structural changes in the international economy, through modern processes of development of national transport and logistics systems, as well as through the study of the complex network structure of global logistics chains, where both approaches complement each other.Research results. The rapid development of modern cities, the increase of vehicles on the streets, the development of new routes increases the amount of information necessary for analysis and making operational decisions. This, in turn, stimulates the active use of intelligent information technologies. And the creation of legal incentives for the development of the intellectualization of the transport and logistics system is one of the factors of strengthening the economy and improving the welfare of the population.Conclusion. The conducted research made it possible to conduct an analysis of legal regulation and stimulation of the development of intelligent transport systems (ITS). In particular, at the international and regional levels. The main necessary elements of the comprehensive application of intelligent transport systems are standardization at the international level, in order to be able to create a global system for more efficient use of transport and achieving global goals in the safety of movement and reducing the impact of transport on the environment.

Artificial intelligence (AI) is the most looked up technology with a diverse range of applications across all the fields, whether it is intelligent transportation systems (ITS), medicine, healthcare, military operations, or others. One such application is autonomous vehicles (AVs), which comes under the category of AI in ITS. Vehicular Adhoc Networks (VANET) makes communication possible between AVs in the system. The performance of each vehicle depends upon the information exchanged between AVs. False or malicious information can perturb the whole system leading to severe consequences. Hence, the detection of malicious vehicles is of utmost importance. We use machine learning (ML) algorithms to predict the flaw in the data transmitted. Recent papers that used the stacking ML approach gave an accuracy of 98.44%. Decision tree-based random forest is used to solve the problem in this paper. We achieved accuracy and F1 score of 98.43% and 98.5% respectively on the VeRiMi dataset in this paper. Explainable AI (XAI) is the method and technique to make the complex black-box ML and deep learning (DL) models more interpretable and understandable. We use a particular model interface of the evaluation metrics to explain and measure the model’s performance. Applying XAI to these complex AI models can ensure a cautious use of AI for AVs.

114 Road traffic injury prevention: the role of artificial intelligence

Artificial intelligence (AI) is used in tasks that usually require human intelligence. The motivation behind this study is the growing interest in deploying AI in public spaces, particularly in autonomous vehicles such as flying drones, to address challenges in navigation and control. The primary challenge lies in developing a robust, cost-effective system capable of autonomous navigation in real-world environments, handling obstacles, and adapting to dynamic conditions. To tackle this, we propose a novel approach integrating machine learning (ML) algorithms, specifically, reinforcement learning (RL), with a comprehensive simulation and testing framework. Reinforcement learning machine algorithms designed to solve problems requiring optimization of the solution for the highest possible reward were used. It was assumed that the algorithms do not have to be created from scratch, but they need a well-defined training environment that will appropriately reward or punish the actions taken. This study aims to develop and implement a novel approach to autonomous drone navigation using machine learning (ML) algorithms. The primary innovation lies in the comprehensive integration of ML algorithms with a real-world drone control system, encompassing both simulations and real-world testing. A vital component of this approach is creating a multi-stage training environment that accurately replicates actual flight conditions and progressively increases the complexity of scenarios, ensuring a robust evaluation of algorithm performance. This research also introduces a new approach to optimizing system cost and accessibility. It involves using commercially available, cost-effective drones and open-source or free simulation tools, significantly reducing entry barriers for potential users. A critical aspect of this study is to assess whether affordable components can provide sufficient accuracy and stability without compromising system quality. The authors developed a system capable of autonomously determining optimal flight paths and controlling the drone, allowing it to avoid obstacles and respond to dynamic conditions in real time. The performance of the trained algorithms was confirmed through simulations and real-world flights, which allowed for assessing their usefulness in practical drone navigation scenarios.