Revolutionizing Urban Mobility: A Systematic Review of AI, IoT, and Predictive Analytics in Adaptive Traffic Control Systems for Road Networks

Increasing urban congestion is a common problem in big cities all over the world. Some Adaptive traffic control (ATC) systems have been proposed to reduce the total travel delay time, which is the main factor of congestion cost. However, previous solutions need a great number of sensors and hardware devices, which are hard to deploy. Fortunately, the advent of advanced Internet of Things (IoT) has made possible more effective and efficient solutions for the congestion issue. Besides, with the availability of the Machine Learning (ML) models, there is hope that the ATC system can be improved with the IoT approach, adopting the ML models. In this paper, we propose a machine learning model for adaptive traffic light control system. First, we assume traffic data is collected from internet-connected IOT sensing devices in vehicles on the roads. Next, the proposed machine learning model receives the data analyzed in cloud, and generates an optimal traffic light period as output. Finally, the optimal traffic light period is transformed to traffic light setting signals to be delivered to the IoT actuating devices in the crossroads. For verifying the proposed model, we build a traffic simulator. For a 24-hour simulated period, the proposed model reduces 55.7% waiting time and 12.76% maximum road occupancy on average, as compared with the Fixed Traffic Light Control System (FTLCS). We also simulate different traffic levels, and our model performs consistently better than the FTLCS in the overall waiting time and maximum road occupancy. The experimental results show that the proposed model is able to alleviate the traffic congestion problem.

India is a developing country and needs to occupy low-cost plus effective measures in controlling traffic. With the increase in Indian urbanization, more people are moving from rural areas to cities leading to excess population, which can be easily seen on roads. Due to increase in number of citizens in particular cities, automobiles can also be seen in enormous number. With the increment of vehicles, traffic system needs to be in order. Traffic system in most of the Indian cities is based on fixed time control mode or manual over-ride mode. But with the modernization with time, we need to implement a new and efficient traffic control system. In this research work, the authors have proposed an adaptive traffic light control system, which is helpful to avoid congestion of vehicles and time consumption. This adaptive traffic control system uses camera as an input sensor, which takes snapshots of roads from different angles and directions to provide input data as form of images. It will provide real-time traffic data. In this work OpenCV framework is used for identification and determination of objects in real-time traffic images captured by camera. For ground detection and background subtraction of images we used GM algorithm (part of OpenCV), which is also used to calculate the number of vehicles present in each line of road. Intersection traffic lights will change according to the traffic condition that will be detected through image and video feeds.KeywordsOpenCVCamera sensorDensity-based traffic control system

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is revolutionizing urban development by enabling smarter, more efficient, and sustainable cities. AI-powered IoT systems integrate real-time data processing, predictive analytics, and automation to enhance various aspects of urban infrastructure, leading to improved resource utilization, reduced operational costs, and enhanced citizen services. This paper explores the transformative role of AI-enabled IoT in key smart city applications, including intelligent transportation, energy management, waste management, and public safety. In intelligent transportation, AI-driven IoT solutions optimize traffic flow, reduce congestion, and improve public transit efficiency through predictive modeling, autonomous vehicle integration, and adaptive traffic management systems. Energy management benefits from AI-enabled IoT networks that analyze consumption patterns, enhance grid stability, and facilitate the seamless integration of renewable energy sources, contributing to sustainable urban power distribution. In waste management, AI-powered IoT applications enable smart waste collection, optimize route planning, and enhance recycling processes through automated monitoring and real-time analytics. Furthermore, AI-driven public safety applications, including intelligent surveillance, emergency response systems, and crime prediction models, enhance urban security and disaster preparedness. This study presents various AI-driven IoT frameworks, highlighting their benefits, technological capabilities, and implementation challenges. Issues such as data privacy, cybersecurity, infrastructure scalability, and interoperability are analyzed in detail to assess the feasibility and long-term sustainability of these smart city solutions. Through an in-depth review of existing case studies and emerging trends, this research provides insights into how AI-enabled IoT can shape the future of urban development. Tables, figures, and bar charts illustrate key technological advancements, deployment strategies, and the overall impact of AI-driven IoT systems on modern urban infrastructure. By addressing existing challenges and leveraging the full potential of AI and IoT integration, cities can enhance operational efficiency, improve quality of life, and foster sustainable development in an increasingly digital world.

The rapid advancements of Internet of Things (IoT) and Artificial Intelligence (AI) have catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) models produce state-of-the-art performance and have great potential for practical applications. In the existing DRL-based ATCS, the controlled signals collect traffic state information from nearby vehicles, and then optimal actions (e.g., switching phases) can be determined based on the collected information. The DRL models fully “trust” that vehicles are sending the true information to the traffic signals, making the ATCS vulnerable to adversarial attacks with falsified information. In view of this, this article first time formulates a novel task in which a group of vehicles can cooperatively send falsified information to “cheat” DRL-based ATCS in order to save their total travel time. To solve the proposed task, we develop CollusionVeh , a generic and effective vehicle-colluding framework composed of a road situation encoder, a vehicle interpreter, and a communication mechanism. We employ our framework to attack established DRL-based ATCS and demonstrate that the total travel time for the colluding vehicles can be significantly reduced with a reasonable number of learning episodes, and the colluding effect will decrease if the number of colluding vehicles increases. Additionally, insights and suggestions for the real-world deployment of DRL-based ATCS are provided. The research outcomes could help improve the reliability and robustness of the ATCS and better protect the smart mobility systems.

The advancement of Artificial Intelligence (AI) and the Internet of Things (IoT) has accelerated the development of Intelligent Transportation Systems (ITS) in smart cities, playing a crucial role in optimizing traffic flow, enhancing road safety, and improving the driving experience. With urban traffic becoming increasingly complex, timely detection and response to congestion and accidents are critical to ensuring safety and situational awareness. This paper presents Passable, an intelligent and adaptive traffic light control system that monitors traffic conditions in real time using deep learning and computer vision. By analyzing images captured from cameras at traffic lights, Passable detects road incidents and dynamically adjusts signal timings based on current vehicle density. It also employs wireless communication to alert drivers and update a centralized dashboard accessible to traffic management authorities. A working prototype integrating both hardware and software components was developed and evaluated. Results demonstrate the feasibility and effectiveness of designing an adaptive traffic signal control system that integrates incident detection, instantaneous communication, and immediate reporting to the relevant authorities. Such a design can enhance traffic efficiency and contribute to road safety. Future work will involve testing the system with real-world vehicular communication technologies on multiple coordinated intersections while integrating pedestrian and emergency vehicle detection.

Traffic congestion and higher average waiting time has been a problem for a very long time. The purpose of this project is to design and implement a traffic system that is adaptive to nature of the traffic in respective lanes. Most of traffic signals are having counters according to which the traffic lights of different lanes get changed one by one. To solve this problem of fixed wait time, counter for any traffic, we proposed this adaptive traffic system which is connected to internet so that different lanes can be monitored constantly. The data obtained from different lanes are examined and controlled by Central Traffic Control Office from one place. Data obtained thus gives value of traffic congestion in particular lane, according to which traffic lights are programmed to work. If the first lane is having less traffic than other lane, then the signal lights will be decided on the basis of less wait time and less pollution. This system also gives idea to drivers to choose the path with less congestion. This system is also useful in emergency and VIP clearance and in traffic survey. This increases the efficiency of traffic clearance. This also reduces pollution and traffic congestion, thus being an Adaptive Traffic Control System using Internet of Things.

The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) technologies is reshaping the landscape of global manufacturing by enabling real-time data-driven decision-making, predictive analytics, and autonomous operations. This research investigates the practical applications, challenges, and strategic implications of integrating AI and IoT in smart manufacturing through an in-depth case study of Emirates Global Aluminium (EGA), one of the world’s leading aluminium producers based in the United Arab Emirates. By conducting semi-structured interviews with 15 domain experts, including plant managers, IT engineers, automation specialists, and AI consultants, the study captures rich qualitative insights into the technological transformation taking place within EGA’s production ecosystem. The findings reveal that AI and IoT have significantly enhanced operational efficiency at EGA through predictive maintenance, energy optimization, process automation, and real-time quality control. Experts emphasized the critical role of machine learning algorithms in forecasting equipment failures and the use of IoT-enabled sensors in tracking environmental and performance metrics. Furthermore, the adoption of AIoT technologies has improved data interoperability across departments, contributing to more agile and responsive manufacturing workflows. This study contributes to the growing body of Industry 4.0 literature by offering a grounded case of AI-IoT synergy within a regional industrial leader, thereby bridging the gap between conceptual frameworks and operational realities. It also proposes a customized smart manufacturing framework aligned with EGA’s strategic goals and technological maturity. The implications of the study are both practical and theoretical. Practically, it provides a roadmap for other industrial players in the Middle East seeking to integrate AIoT solutions for enhanced productivity and sustainability. Theoretically, it enriches our understanding of digital transformation in resource-intensive sectors, offering empirical evidence for models like the Technology-Organization-Environment (TOE) framework and the Dynamic Capabilities Theory. The study also underscores the need for addressing challenges such as cybersecurity vulnerabilities, data governance, talent gaps, and integration costs, which must be strategically managed to ensure long-term competitiveness. This research demonstrates how a forward-thinking industrial enterprise like EGA leverages AI and IoT to remain at the forefront of smart manufacturing, offering valuable lessons for policymakers, practitioners, and academic scholars engaged in digital industrial transformation.

The rapid advancement of Artificial Intelligence (AI), Internet of Things (IoT) technologies, and predictive analytics is revolutionizing pest management strategies in agriculture. This chapter explores the integration of these cutting-edge technologies to develop intelligent, data-driven systems for pest detection, monitoring, and control. AI-powered computer vision systems, combined with IoT sensors and machine learning models, enable real-time monitoring of pest populations and environmental conditions, significantly improving the accuracy and efficiency of pest control interventions. Predictive analytics, leveraging historical and real-time data, further enhances these systems by forecasting pest outbreaks, allowing for proactive and targeted pest management. The chapter examines various real-world applications, including precision pest control in large-scale agriculture, greenhouses, vineyards, and field crops, where these integrated systems have successfully minimized chemical use, reduced environmental impact, and optimized resource allocation. Additionally, the challenges of scaling IoT sensor networks and the complexities of system integration are discussed, alongside potential solutions for widespread adoption. The future of pest management lies in the seamless fusion of AI, IoT, and predictive analytics, offering a sustainable, autonomous, and precision-driven approach to pest control. This chapter provides valuable insights for researchers, practitioners, and policymakers seeking to enhance pest management strategies in modern agriculture.

Adaptive Traffic Signal Control (ATSC) systems represent a critical advancement in urban traffic management, offering significant potential for reducing pedestrian-vehicle conflicts at high-risk crossings. This review paper examines the effectiveness of ATSC systems in enhancing pedestrian safety through real-time signal optimization and intelligent traffic management. We analyze recent developments in adaptive signal technologies, including machine learning-based systems, connected vehicle integration, and multi-modal optimization approaches that prioritize pedestrian safety alongside traffic efficiency. The paper explores various ATSC architectures, from basic actuated systems to sophisticated deep reinforcement learning models, and their performance in reducing conflict points between pedestrians and vehicles. Recent field studies demonstrate that advanced ATSC systems can reduce pedestrian-vehicle conflicts by up to 40% while simultaneously improving overall traffic flow efficiency. However, challenges persist in balancing competing demands between vehicular throughput and pedestrian safety, particularly in high-density urban environments. This review synthesizes current research findings, identifies implementation barriers, and highlights the critical role of real-time pedestrian detection technologies in enabling safer adaptive signal control. Our analysis reveals that while ATSC systems show considerable promise for improving pedestrian safety, their effectiveness varies significantly based on intersection geometry, traffic patterns, and system sophistication. The integration of emerging technologies such as computer vision, artificial intelligence, and vehicle-to-infrastructure communication presents opportunities for next-generation ATSC systems that can more effectively balance safety and efficiency objectives.

Adaptive traffic control systems represent complex, but powerful tool for improvement of traffic flow conditions in locations or zones where applied. Many traffic agencies, especially those that have a large number of signalized intersections with high variability of the traffic demand, choose to apply some of the adaptive traffic control systems. However, those systems are manufactured and offered by multiple vendors (companies) that are competing for the market share. Due to that fact, besides the information available from the vendors themselves, or the information from different studies conducted on different continents, very limited amount of information is available about the details how those systems are operating. The reason for that is the protecting of the intellectual property from plagiarism. The primary goal of this paper is to make a brief analysis of the functionalities, characteristics, abilities and results of the most recognized, but also less known adaptive traffic control systems to the professional public and other persons with interest in this subject.

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is no longer a distant promise; it is actively reshaping the practice of medicine. From continuous glucose monitors that anticipate hypoglycemic events hours in advance to smart hospital systems that predict patient deterioration before vital signs collapse, the integration of real-time sensing and intelligent computation is shifting healthcare from a predominantly reactive discipline to one that is increasingly predictive, personalized, and proactive. This volume, Artificial Intelligence, brings together leading researchers and practitioners to examine the technologies, architectures, applications, and challenges that define this transformation. The chapters that follow explore the full spectrum of the AI–IoT ecosystem in healthcare: from foundational concepts in predictive analytics and wearable biosensors to advanced topics in multimodal data fusion, edge computing, big data architectures, ethical governance, and the emerging paradigms of digital twins and federated learning. Our contributors represent institutions and perspectives from across the globe, yet they share a common conviction: the future of healthcare will be built on systems that do not merely collect data, but understand it, act upon it, and do so equitably and responsibly. The book is therefore as much about rigorous engineering and clinical validation as it is about the ethical, regulatory, and societal questions that must accompany rapid technological change. We are deeply grateful to the authors for their scholarly generosity and intellectual rigor, to the reviewers for their critical insights, and to Deep Science Publishing for their commitment to open-access dissemination of knowledge. It is our hope that this collection serves not only as a comprehensive reference for researchers, engineers, and clinicians already working at this frontier, but also as an invitation to students, policymakers, and healthcare leaders to engage critically with the possibilities and the responsibilities that AI and IoT now place before us. May the ideas presented here contribute to a future in which intelligent systems extend human expertise rather than replace it, in which predictive technologies narrow rather than widen health disparities, and in which medicine moves decisively from the treatment of disease to the preservation of well-being.

The society is witnessing an accelerated large-scale adoption of technology with transformative effects on daily transport operations, with cities now depending on data driven mobility services. Disruptive technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and decentralized technologies for example Distributed Ledger Technologies (DLT) are being deployed in smart cities. However, AI is faced with data security and privacy issues due to its centralized mode of deployment. Conversely, DLT which employs a decentralized architecture can be converged with AI to provide a secure data sharing across various IoT thereby overcoming the existing setbacks faced in deploying AI in smart cities. Evidently, the convergence of AI and IoT as AIoT and DLT have great potential to create novel business models for improved data driven services such as intelligent mobility in smart cities. Although research on the convergence of AI, IoT and DLT exists, our understanding of its integration in achieving intelligent mobility services in smart cities remains fragmented as current research in this area remains scarce. This study bridges the gap between theory and practice by providing researchers and practitioners with insights on the potential benefits of converging AIoT and DLT. Grounded on the Technology Organization Environment (TOE) framework this study presents the technological, organizational, and environmental factors that impacts the convergence of AIoT and DLT in smart cities. Additionally, findings from this study present use cases on the applicability of AIoT and DLT to support intelligent mobility services in smart cities.

The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has transformed the field of sustainable transportation planning, notably in the context of Electric Vehicles (EVs). The Problems in all the Existing models are not having proper coordination of optimizing the elements of EV functioning. This research proposes a novel strategy to improving sustainable transportation planning by utilizing AI and IoT. AI-powered algorithms analyze real-time data from IoT sensors installed in EVs and the surrounding environment. These adaptive control algorithms are designed to solve issues including range anxiety, charging infrastructure optimization and energy efficiency. Predictive analytics, route optimization, energy management, and grid interface are all part of the proposed system. Energy management algorithms alter EV settings dynamically to maximize efficiency while taking into account real-time traffic conditions thereby increasing the range extension is upto 2.5% and the total energy efficiency is improved upto 92%. Furthermore, bidirectional connection allowed by IoT devices facilitates the integration of EVs into the energy grid. EVs may engage intelligently in Vehicle-to-Grid (V2G) interactions, providing grid services such as energy delivery during peak demand periods and grid stability.

Harnessing Artificial Intelligence to Safeguard Food Quality and Safety.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks. Keywords: Machine Learning, Deep Learning, Neural Networks, Regression Models, Reinforcement Learning