Smart Traffic Management System

This paper explores the advancement of smart traffic management system using the Internet of Things (IoT). It works as middleware on the foundation of the IoT and augments the idea of the smart city through the traffic light control, smart parking, smart emergent assistance, anti-theft security system, and others. IoT provides an effective way of interactions among the web devices with the traffic embedded sensors, services, actuators, and other interconnected networks. Hence, the application of IoT in the smart traffic management system is not only limited to the reduction of the traffic congestion, air quality improvement, and traffic flow optimization but also extended to the continuous monitoring and ensuring the security and safety for the elderly people. Acquiring multiple sources of traffic information for data analysis, IoT monitors the traffic flow, controls the traffic operation and stores the correct decision for the future information presentation. Having a combination of advanced machine learning approach and data-driven technique, there are implementation limitations of this technology. However, this survey provides a good insight into the application of IoT in the smart traffic management system based on the existing research perspective.

Smart Traffic System (STS) is a one of the important aspect for future smart city. STS is more expensive and highly configurable to provide better quality of service for public traffic management. This paper proposes a low cost future STS to provide better service by deploying traffic update instantly. Low cost vehicle detecting sensors are fixed in the middle of road for every 500 meters. Internet of Things (IoT) is being used to attain public traffic data quickly and send it for data processing. The Real time streaming data is sent for Big Data analytics. There are several analytical scriptures to analyze the traffic density and provide solution through predictive analytics.

Internet of Things has given us a new epoch of computing technology. As we know that Internet, a rebellion innovation has transformed everything, Internet of Things has made a hope for excellent future of Internet with Machine-Machine type of communication in addition. Thoughts of creation that correlates a different means of considering the events with invented concepts that avail the possibilities proposed by new approaches in intelligent ways. Framing an intelligent environment depends on smart education system, smart traffic management system, smart vehicle management system, smart waste management system, industrial control, smart agriculture etc. Today, safety on roads, cleanliness in residential areas, security at home has become an essential component in our daily life. So there is a necessity to avail new approach of innovative technologies based on Internet of Things to solve the environmental problems. In this paper we surveyed some of these problems and suggested various executable plans also. The actualization of smart sensing and transferring of data has offered a significant assurance for building the blueprints. This review paper has also shown that it is possible and affordable to construct these smart systems based on Internet of Things (IoT). We have studied various blueprints which can sense unforeseen happenings such as landslides, traffic jam, accidents, etc. using different sensors and demonstrate the facts accumulated on LED display. Observational outcomes have shown good agreement with the hypothetical statements.

The management of supply chains has been profoundly changed by the Internet of Things (IoT), offering new avenues for enhancing real-time visibility and operational efficiency. This study examines how important IoT is to Supply Chain 4.0, focusing on its capabilities to track, monitor, and analyze data within the supply chain ecosystem. IoT technologies make it easier for systems and devices to integrate seamlessly, providing stakeholders with instant access to valuable information, thereby improving procedures for determining decisions and overall performance evaluation of supply chain. The benefits and difficulties of IoT deployment in supply chains are identified in this paper by use of a thorough analysis of the body of current literature. Key advantages include improved transparency, reduced costs, enhanced inventory management, and streamlined operations. IoT-enabled sensors and devices can monitor conditions in real-time, ensuring timely interventions and mitigating risks. Notwithstanding these advantages, the study also identifies a number of drawbacks, including the requirement for a strong digital infrastructure, significant implementation costs, and data security issues. In order to fully utilize IoT in supply chain management, several issues must be resolved. According to the findings, supply chain resilience and efficiency may be greatly increased by implementing a systematic approach to IoT integration that is backed by thorough research and regulatory frameworks. The study ends with suggestions for more study and real-world implementations, highlighting the necessity of stakeholder cooperation to effectively utilize IoT's potential to revolutionize supply chains.

Congestion in traffic is an unavoidable circumstance in many cities of India and other countries. It is an issue of major concern. The steep rise in the number of automobiles on the roads followed by old infrastructure, accidents, pedestrian traffic, and traffic rule violations all add to challenging traffic conditions. Given these poor conditions traffic, there is a critical need for automatically detecting and signaling systems. There are already various technologies that are used for traffic management and signaling systems like video analysis, infrared sensors, and wireless sensors. The main issue with these methods is they are very costly and high maintenance is required. In this paper, we have proposed a three-phase system that can guide emergency vehicles and manage traffic based on the degree of congestion. In the first phase, the system processes the captured images and calculates the Index value which is used to discover the degree of congestion. The Index value of a particular road depends on its width and the length up to which the camera captures images of that road. We have to take input for the parameters (length and width) while setting up the system. In the second phase, the system checks whether there are any emergency vehicles present or not in any lane. In the third phase, the whole processing and decision-making part is performed at the edge server. The proposed model is robust and it takes into consideration adverse weather conditions such as hazy, foggy and windy. It works very efficiently in low light conditions also. The edge server is a strategically placed server that provides us with low latency and better connectivity. Using Edge technology in this traffic management system reduces the strain on cloud servers and the system becomes more reliable in real-time because the latency and bandwidth get reduced due to processing at the intermediate edge server.

This paper describes the development of an optimization model that considers the travel times of buses and traffic patterns to minimize deviations from planned schedules for routes across a network in a European city. Traffic congestion is a significant challenge which negatively affects the on-time performance and reliability of bus services. To address this issue, smart traffic management systems such as SCATS (Sydney Coordinated Adaptive Traffic System) are implemented to enhance traffic flow. While SCATS allows buses to request priority at road junctions controlled by traffic signals, conflicts may arise when different buses have opposing requests. To address this, we use a stochastic optimization approach to determine an optimal time-specific traffic interference solution that network owners can impose, such as signal phasing, to minimize schedule deviations for buses throughout the entire network.

Vehicular Ad-hoc Network is subclass of Mobile Ad-hoc networks and plays a vital role in Intelligent Transportation System implementation for smart cities. In the transportation scenario finding out an optimized path and locating the malicious vehicle is a big challenge. Routing is challenging in this technology because of reasons like high mobility of the node, rapid change in topology, interruption in connectivity, etc. The mobility of nodes affects the link stability and congestion is the effect of limited node resources. It also degrades the Quality-of-Service performance. Unmanned Ariel Vehicle helps to mitigate such conditions with the combination of learning capability. In this paper, the contribution of the Unmanned Ariel Vehicle to a smart transportation system has been studied and the working of different protocols is also analyzed. This paper gives a complete overview of the advancement of the system and implementation concept of different smart traffic management systems in urban areas.

The expansion of technology in metropolitan centers draws people to cities, which causes excessive traffic on the roadways during peak hours. This exacerbated the traffic situation, resulting in a delay, a loss of resources, and a waste of time. Like any other metropolitan metropolis, Riyadh, Saudi Arabia, has everyday traffic congestion during business hours. The current traffic management has made many attempts to ease traffic congestion in cities; despite these measures, the problem has not been solved adequately. To handle this road congestion, there is a need to appropriately store the big data collected by traffic sensors and utilize it for efficient traffic management employing cutting-edge technology. This study provides an architecture for a smart traffic management system that uses modern technologies such as the Internet of Things (IoT), cloud computing, 5G, and big data to aid conventional traffic management systems and efficiently handle the stated problem. The proposed technique has the potential to reduce traffic congestion significantly. Our proposed solution encourages mobility by using roadside messaging agents to offer real-time traffic information on traffic congestion and unexpected traffic incidents. Citizens will save time by getting these early warning messages, particularly during peak hours. As part of the suggested method, each signal dashboard gets traffic information. A case study is used in the research to evaluate alternative solutions to traffic congestion. The case study results show that the proposed strategy outperforms the present options.

The convergence of Supervisory Control and Data Acquisition (SCADA) systems with the Internet of Things (IoT) and Machine Learning (ML) is redefining automation, security, and operational efficiency across industries. Traditional SCADA systems, widely used in critical infrastructure such as energy, water management, and industrial processes, are undergoing a transformative shift with the integration of IoT. IoT-enabled sensors and devices provide real-time data streams from diverse operational environments, enabling SCADA systems to achieve enhanced situational awareness and remote monitoring capabilities. Machine Learning further augments SCADA systems by introducing advanced analytics for predictive maintenance and anomaly detection. ML algorithms analyse vast datasets collected through IoT devices to forecast system failures, optimize resource utilization, and ensure uninterrupted operations. This predictive approach minimizes downtime, reduces maintenance costs, and improves overall efficiency. Cloud-driven security frameworks are pivotal in addressing the growing cybersecurity challenges associated with SCADA and IoT integration. These frameworks provide scalable and resilient solutions for protecting data integrity, preventing unauthorized access, and mitigating cyber threats. The deployment of ML-driven cybersecurity solutions enhances the ability to detect and respond to sophisticated attacks targeting SCADA environments. This paper examines the evolution of SCADA systems in the era of IoT, focusing on the transformative role of ML and cloud-driven security. It explores innovative applications, highlights the challenges of integrating these technologies, and provides insights into future advancements for creating robust and secure automated systems.

This project introduces a Real-Time Vehicle Detection and Lane Tracking System aimed at improving traffic monitoring and road safety through advanced computer vision and deep learning. Using the YOLOv8 model, the system detects multiple vehicle classes in live video streams and assigns unique IDs through multi-object tracking. Lane boundaries are extracted using edge detection and region masking, enabling accurate lane classification for each vehicle. The system also identifies lane-crossing events and automatically logs them into a structured CSV file for further analysis. Additionally, the system estimates vehicle speed, tracks movement patterns, and generates activity logs using pixel displacement and frame-based analysis. A real-time Streamlit dashboard visualizes detections, lane overlays, and tracking information to provide a clear overview of road activity. With its modular and efficient design, the solution supports applications in smart traffic management, violation detection, and intelligent transportation systems, offering a scalable and accurate approach for real-time roadway surveillance.

Smart Traffic Prediction and Management System using Hybrid Deep Learning and Ensemble Methods

As the number of vehicles on the road continues to increase, methods to deal with changes in traffic demand and density keep evolving. The intent is to ensure better management via traffic lights timing, phases, and cycles to reduce the delays that occur in increased demand scenarios. Typically, solutions have been divided into centralized and decentralized, each with their own advantages and disadvantages. Most existing solutions, however incur a high cost to adapt the infrastructure to. In this paper, we present a decentralized method for traffic management via controlling traffic lights that current systems can be adapted to. The proposed solution can be adapted to existing traffic solutions without huge infrastructure costs. Inspired by TCP congestion control, for its operation, the system relies on an RSSI-based vehicle direction detection system. The advantage of such a system is that it can be adapted to existing traffic networks without introducing a high infrastructure cost. The effectiveness of this solution is evaluated through simulations carried out using the Simulation of Urban Mobility (SUMO) tool and is compared to currently deployed solutions. A small-scale traffic intersection physical model is also developed that demonstrates the operation of the vehicle direction detection system.

Hospital-acquired infections (HAIs) remain a major cause of patient morbidity, mortality, and increased healthcare costs, with critical care units and surgical wards particularly vulnerable due to invasive procedures, compromised immunity, and prolonged hospital stays. Evidence-based nursing bundles structured, standardized sets of clinical interventions have consistently demonstrated effectiveness in reducing HAI incidence when applied systematically. This study examines the integration of such bundles with Internet of Things (IoT) devices to enhance compliance monitoring, improve real-time decision-making, and strengthen infection prevention strategies in high-risk hospital environments. The proposed framework incorporates standardized nursing bundles for central line-associated bloodstream infection (CLABSI) prevention, ventilator-associated pneumonia (VAP) protocols, and surgical site infection (SSI) reduction, combined with IoT-enabled sensors, wearable devices, and environmental monitoring systems. Real-time data from hand hygiene dispensers, patient vitals, and cleanliness audits are captured, securely transmitted to cloud-based platforms, and analyzed using machine learning algorithms to identify deviations from established care protocols. Embedding these IoT data streams into nursing workflow dashboards enables clinical teams to receive actionable alerts, track bundle compliance, and initiate timely interventions. Simulated pilot testing suggests that such integration can significantly reduce HAI rates, enable faster detection of protocol breaches, and optimize resource allocation for infection control efforts. This fusion of IoT capabilities with evidence-based nursing bundles not only enhances clinical accountability and supports precision nursing but also provides a foundation for data-driven quality improvement initiatives. Future research should address interoperability standards, cost-effectiveness analysis, and robust privacy safeguards to ensure scalable, sustainable adoption of IoT-assisted infection prevention systems across diverse healthcare settings.

BackgroundOver the last decades, Portugal have made a considerable progress in lowering rates of deaths from road accidents. Between 1975 to 2016 annual road traffic deaths decrease from 2.676 to...

Internet of Things (IoT) technology in Electric Vehicles (EVs) has the potential to enhance performance, connectivity, and the overall user experience. This connection improves EV efficiency, battery life, user interaction, charging infrastructure, and traffic management systems. Dependable communication networks, system compatibility, and data security are all essential. Several concerns must be solved before manufacturers can use the IoT in EVs. Internet of Things-based Accurate Estimation Monitoring Analysis (IoT-AEMA) is presented in this paper as a solution to address these problems. Intending to enhance energy management, safety, and predictive maintenance, the IoT-AEMA has taken the initiative. Electric vehicle (EV) performance can be monitored comprehensively and in real-time with the help of IoT-AEMA, which utilizes IoT technology. This technology makes monitoring metrics like energy use and battery health more accurate. Proactive maintenance is made possible, and communication with smart infrastructure is improved. Improving electric vehicle (EV) connection and efficiency has never been easier than with this scalable solution that prioritizes sustainability. This objective will be accomplished by providing extensive analysis and monitoring of vehicle parameters in real-time. These applications use this technology to enhance data accuracy, the decision-making process for drivers and manufacturers, and the development of intelligent transportation networks. The effectiveness of IoT-AEMA has been demonstrated through simulation studies in various circumstances. By giving accurate insights and encouraging collaboration, this research implies that the electric vehicle industry is on the verge of experiencing a paradigm change. According to the information presented in this article, the IoT and advanced energy management have the potential to make EVs more dependable, efficient, and integrated into the infrastructure of smart cities. The proposed method increases the Energy Management Optimization ratio by 97.6%, Data Accuracy ratio by 90.2%, Predictive Maintenance ratio by 95.7%, System Compatibility ratio by 93.4% and Reliability Analysis ratio by 98.4% compared to other existing methods.