2021· International Journal of Modern Research in Science & Engineering· 0 citations
TL;DR
The findings indicate that intelligent systems are very effective in minimizing congestion, travelling time as well as emissions over traditional methods.
Abstract
The high rate of urbanization has caused a high growth in the number of vehicles, which has produced a congestion, wastage on time, and fuel, as well as pollution to the environment. No longer applicable because of the dynamic character of modern urban traffic, the traditional traffic management systems based on the use of the non-informative control mechanisms and low real-time flexibility. The Intelligent Traffic Management Systems (ITMS) have become a very important part of an intelligent city system, as it intends to use the latest technologies that include Artificial Intelligence (AI), Internet of Things (IoT), machine learning, cloud computing and big data analysis to make traffic flow in the city more efficient and safer. In this paper, complete research on Intelligent Traffic Management Systems in smart cities has been made. It dwells upon the development of traffic management, the enabling technologies, system architecture, and methodologies. An elaborate literature review indicates the latest developments and outlines the gaps in research. The proposed approach will combine real-time data collection, predictive analysis, and responsive signal modulation to improve the traffic flow. The mathematical models and performance evaluation measures have been addressed to measure the system effectiveness. The findings indicate that intelligent systems are very effective in minimizing congestion, travelling time as well as emissions over traditional methods. Lastly, issues, constraints, and research prospects are given to facilitate long-term and viable implementation of ITMS in intelligent city setups.
Urban transportation networks are essential infrastructures that support the movement of people and goods in modern cities. However, rapid urbanization, population growth, and the increasing number of vehicles have led to serious challenges such as traffic congestion, road accidents, increased travel time, and environmental pollution. Conventional traffic management systems rely mainly on fixed-time signal control strategies, which are unable to adapt to dynamic and real-time traffic conditions, resulting in inefficient traffic flow and underutilization of road infrastructure. To overcome these limitations, Artificial Intelligence (AI) and the Internet of Things (IoT) have been integrated into Intelligent Transportation Systems (ITS) to enable intelligent and adaptive traffic management. IoT-enabled devices such as smart cameras, inductive loop detectors, GPS modules, and environmental sensors continuously collect real-time traffic parameters including vehicle density, speed, queue length, and traffic flow. These heterogeneous data streams are transmitted through wireless communication networks to cloud or edge computing platforms for real-time data processing and analysis. AI-based algorithms, including machine learning and deep learning models, analyze the collected traffic data to identify spatiotemporal traffic patterns, predict congestion levels, and optimize traffic signal timings. Advanced techniques such as reinforcement learning enable adaptive traffic signal control by dynamically adjusting signal phases based on current traffic conditions, thereby reducing vehicle delays and improving intersection throughput. Furthermore, Vehicle-to-Everything Communication (V2X) facilitates communication between vehicles, infrastructure, and pedestrians, enabling cooperative traffic management and improved road safety. Despite these advancements, challenges such as high deployment costs, data privacy concerns, and interoperability issues remain. Continuous research in AI-driven IoT frameworks and secure communication technologies is essential to achieve efficient, scalable, and sustainable smart transportation systems.
M. Rakshana, Natalia Andria, P. Muthukumar et al.· International Conference on...· 0 citations
The fast pace of urbanization has made smart and sustainable infrastructure management more important than ever. Because of their inherent silos, traditional urban management systems are unable to adapt in real-time to shifting demands in areas such as water distribution, public safety, energy consumption, traffic flow, and energy consumption. This study found that smart cities may use AI and the internet of things to adapt and manage their infrastructure using data. Sensors throughout the city’s infrastructure for transportation, power, buildings, and the environment provide data into Internet of Things devices. Analytics systems powered by AI can automate decision-making, enhance resource allocation, discover anomalies, and forecast demand using massive amounts of data. For predictive maintenance and real-time monitoring, the framework places an emphasis on interoperability, scalability, cybersecurity, and sustainability. By replacing reactive systems with proactive ones, adaptive algorithms and machine learning models can increase dependability, save costs, and revolutionize urban planning. Topics covered in the research include data privacy, infrastructure integration, and data governance. The convergence of AI with the Internet of Things (IoT) creates robust, efficient, citizen-centric urban ecosystems, as shown by comprehensive design and performance evaluation metrics. Smart cities that can adjust to changes in the environment, population, and economy are made possible by these discoveries.
P. Kumaresan, Hayel Khafajeh, R. Latha et al.· International Conference on...· 0 citations
Rapid urbanization has intensified pressure on energy systems, transportation networks, water resources, waste-management infrastructure, public health services, and the urban environment. Conventional city-management models, which often rely on fragmented information and reactive decision-making, are increasingly inadequate to address these interconnected challenges. Artificial Intelligence (AI) and the Internet of Things (IoT) provide a technological foundation for a transition from conventional urban administration toward intelligent, adaptive, and sustainability-oriented city management. IoT infrastructures enable continuous sensing and communication across physical urban environments, whereas AI converts large volumes of heterogeneous sensor data into predictions, classifications, recommendations, and automated decisions. This paper examines the integrated role of AI and IoT in sustainable smart city development through a conceptual and interdisciplinary review of research on smart urban systems, data analytics, edge computing, intelligent transportation, energy management, environmental monitoring, waste management, water conservation, public safety, and urban governance. The paper proposes an AI–IoT Closed-Loop Sustainable Urban Intelligence Framework consisting of sensing, connectivity, edge/cloud processing, artificial intelligence, decision-making and actuation, and sustainability evaluation layers. The analysis demonstrates that the value of AI–IoT integration lies not simply in increasing technological sophistication but in enabling cities to minimize resource consumption, anticipate infrastructure failure, reduce emissions, improve service responsiveness, and make urban systems increasingly adaptive. At the same time, cybersecurity vulnerabilities, privacy risks, algorithmic bias, interoperability problems, digital inequality, high infrastructure costs, and the environmental footprint of computing can weaken sustainability outcomes. The study therefore argues for a human-centered, secure, interoperable, transparent, and sustainability-measured model of smart city development. Future smart cities should be evaluated not by the quantity of connected devices deployed but by measurable improvements in environmental quality, resource efficiency, social inclusion, resilience, and quality of urban life.
P. S., Shaik Rahamtula, S. J. et al.· Stanzaleaf International Jou...· 0 citations
The intensive development of road transportation and the increasing number of vehicles have led to significant challenges related to road safety, traffic congestion, travel time, energy consumption, and negative environmental impacts. In this context, intelligent transport systems (ITS) represent a significant approach to improving the efficiency and sustainability of modern transportation systems. The aim of this paper is to present and systematize the application of modern ITS technologies for improving road safety, reducing emissions, and shortening travel time. Based on an analysis of the relevant literature, the fundamental components and architecture of ITS are presented, including sensor systems, V2X communication, IoT, cloud and edge computing, as well as the application of artificial intelligence in traffic data processing and prediction. The analyzed studies demonstrate that ITS enables dynamic traffic flow management, route optimization, reduction in congestion and emissions, and more efficient responses to emergency situations. Particular attention is devoted to the possibility of simultaneously considering travel time, energy consumption, emissions, noise, and road safety. As a synthesis of the analyzed findings, an integrated algorithm for intelligent traffic management is proposed, operating as a closed feedback loop encompassing data collection, state assessment, prediction, optimization, and control. Future ITS development is expected to focus on the integration of AI, IoT, 6G, and edge computing technologies and their validation using real-world traffic data.
N. Stojanović, I. Grujić, Suzana Petrović Savić et al.· Future Internet· 0 citations
Rapid urbanization has increased the need for intelligent and sustainable smart city infrastructure management. Smart cities generate massive amounts of data through IoT devices, sensors, cloud platforms, and communication networks, making traditional centralized AI systems less effective due to scalability, latency, privacy, and reliability challenges. Distributed Artificial Intelligence (DAI) addresses these issues by distributing intelligence across multiple interconnected nodes, enabling decentralized learning and decision-making. This study examines the role of DAI technologies such as multi-agent systems, edge computing, federated learning, IoT networks, and cloud-edge collaboration in managing urban services. A review of recent applications demonstrates DAI’s effectiveness in traffic management, energy distribution, water systems, predictive maintenance, public safety, and environmental monitoring. The proposed framework enhances real-time processing, resource optimization, fault tolerance, and data privacy. Results indicate that DAI outperforms centralized approaches in response time, scalability, accuracy, and reliability. The study concludes that DAI is a key enabler of future smart cities, with emerging technologies such as Explainable AI (XAI), blockchain, digital twins, and autonomous urban management expected to further improve smart city operations and citizen services.
R. Sharma· International Journal of App...· 0 citations
A sustainable and equity-oriented framework for adaptive traffic signal control, in which data from road sensors, traffic cameras, GPS, and public transport systems are integrated with a short-term traffic flow prediction model and a reinforcement learning algorithm, demonstrates that traffic efficiency, environmental sustainability, and transport equity can be integrated within a unified control logic.
Mohammad Iqbal Khairandish, M. O. Khairandish· 0 citations
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