Smart Traffic Management System Research Articles

Density Based Real-time Smart Traffic Management System along with Emergency Vehicle Detection for Smart Cities

Density Based Real-time Smart Traffic Management System along with Emergency Vehicle Detection for Smart Cities

Enhancing traffic control systems with live video analytics: Issues, challenges, opportunities, and recent problems

With the rapid urbanization and increasing number of vehicles on the roads, it has become imperative to develop innovative solutions that can monitor and manage traffic congestion automatically. Traffic congestion harms the economy, environment, and overall quality of life. To address these challenges, smart traffic management systems employ cutting-edge technologies such as live video analytics and sensor-based adaptive traffic control systems. These systems can predict traffic patterns, locate congestion hotspots, and uncover abnormalities contributing to road accidents in real time. However, adopting these technologies for traffic control systems raises important concerns such as robustness and sustainability across different traffic junctions, data integration from multiple sources, and computational feasibility for real time computation. Therefore, this paper aims to present an overview of the potential benefits and challenges in adapting the latest technologies including the Internet of things and machine learning for sustainable traffic management. Additionally, a case study of a smart city is presented to evaluate an adaptive traffic control system based on live camera feed analytics by leveraging computer vision techniques. The adaptive traffic control system is accurate in vehicle detection and counting. This system is very useful for smart cities where traffic signals need to be automated according to the density of vehicles.

Research on smart city public health detection system and improvement technology based on intelligent multi-objective.

With the increase of urban population density, urban sanitation becomes more severe; urban sanitation has important influence on public health. Therefore, in order to realize the detection of public health in smart cities, the research will use cutting-edge scientific and technological methods to improve urban environmental health, so as to promote the realization of public health achievements. This study introduces public health detection and optimizationtechnologies for smart cities. Firstly, a data detection system for urban public health environment was established using sensors and intelligent multi-objective technology to evaluate the water quality, air quality, and noise level of the city. Then, an intelligent garbage management system based on Tensor-flow was constructed to achieve efficient garbage collection and treatment. Finally, an intelligent traffic management system was developed to monitor and regulate urban traffic flow. The results of the simulation experiment demonstrated that the life data detection system was operationally stable, with a high success rate of 98%. Furthermore, its accuracy in detecting residents' living environment data was above 95%, the maximum relative error was only 0.0465, making it a reliable and efficient tool. The waste recycling system achieved a minimum accuracy of 83.6%, the highest accuracy rate was 95.3%, making it capable of sorting and recycling urban waste effectively. Additionally, the smart traffic management system led to a 20% reduction in traffic congestion rates, 20 tonnes less tailpipe emissions and an improvement in public health and well-being. In summary, the plan proposed in this study aims to create a more comfortable, safe, and healthy urban public health environment, while providing theoretical support for environmental health management in smart cities.

BUDGET-BASED SMART CITY IMPLEMENTATION FOR TIER III CITIES

In the present day's world the Internet of Things (IoT) is gaining popularity as a field of research in various engineering applications. IoT includes various elements like communication technologies, communication protocols, identification technologies, sensing, computation, and actuation. Today, almost every home is equipped with smart IoT home appliances. As cities grow and expand, smart and innovative solutions are crucial for improving productivity, increasing operational efficiencies, reducing management costs, and enhancing the lifestyle of human beings. A smart city consists of a number of applications. In this paper, budget-based implementation of smart cities with specific applications like smart parking, smart dustbins, smart streetlights, and smart traffic monitoring and management systems are done using various IoT boards, various sensors, actuators, and various IoT cloud platforms. In the smart city application, several heterogeneous devices are connected through the internet for communication, and huge amounts of data are collected from various applications and stored in databases. Data are classified, analyzed, and visualized on both web and mobile dashboards.

Edge-Enabled Smart Traffic Management System: An IoT Implementation for Urban Mobility

Effective traffic management has emerged as a critical issue in today's rapidly urbanising areas with a rise in the number of cars. By developing an Edge-Enabled Smart Traffic Management System (EESTMS) run on the Internet of Things (IoT), this paper puts forth a novel approach. EESTMS makes use of edge computing and IoT technology's promise to improve urban transportation. An large network of thoughtfully placed sensors and cameras dispersed around the city forms the system's central structure. These gadgets continuously gather data on the volume, speed, and congestion of moving vehicles. This information offers insightful information about traffic trends. We can lessen latency and lighten the load on centralised systems by processing this data at the edge. In order to analyse the data and identify traffic bottlenecks and congestion hotspots, machine learning techniques are used. Real-time analysis allows for dynamic traffic signal adjustments, which optimise traffic flow and shorten commuter travel times. EESTMS also offers a user-friendly interface with real-time traffic information, alternate routes, and tailored navigation advice that is accessible via mobile applications and web platforms. By making wise decisions, commuters can lessen their stress and carbon footprint. EESTMS plays a critical role in advancing sustainability by reducing fuel consumption and greenhouse gas emissions through effective traffic management, in addition to enhancing urban mobility. By giving emergency vehicles priority routing, this system also improves emergency response times. The application of EESTMS has shown promising outcomes in terms of lessened traffic congestion, improved commuter experiences, and lower environmental impact. Innovative solutions like EESTMS can open the door for smarter, more sustainable urban mobility as cities continue to grow, eventually enhancing citizens' quality of life.

Smart Traffic Management System

With the rapid urbanization and increasing vehicular density in modern cities, the demand for efficient and intelligent traffic management systems has become paramount. This paper presents a comprehensive overview of a Smart Traffic Management System (STMS) designed to optimize traffic flow, enhance safety, and minimize congestion in urban areas. The STMS leverages advanced technologies such as artificial intelligence, sensor networks, and data analytics to create a dynamic and adaptive traffic control infrastructure. The key components of the proposed system include real-time traffic monitoring, predictive analytics, and adaptive signal control. Utilizing a network of sensors, cameras, and other data sources, the STMS continuously gathers information on traffic conditions, identifying patterns and potential issues. This data is then processed through machine learning algorithms to predict traffic trends, enabling proactive management strategies. One of the innovative features of the STMS is its adaptive signal control mechanism. Traditional traffic signal timings are often static and fail to respond promptly to changing conditions. The STMS, however, dynamically adjusts signal timings based on real-time traffic data, optimizing traffic flow and minimizing delays. This adaptability is further enhanced by the integration of vehicle-to-infrastructure (V2I) communication, allowing direct communication between vehicles and the traffic management system. Additionally, the STMS incorporates intelligent decision-making algorithms to prioritize emergency vehicles, public transportation, and other critical services. This ensures a swift and efficient response to emergencies while maintaining overall traffic efficiency. Furthermore, the system includes a user-friendly interface accessible through mobile applications and web platforms. This interface provides real-time traffic updates, alternative route suggestions, and other relevant information to empower commuters with the knowledge needed to make informed travel decisions. The implementation of the Smart Traffic Management System holds the potential to revolutionize urban mobility by significantly reducing travel times, fuel consumption, and environmental impact. This paper discusses the underlying technologies, design considerations, and potential benefits of the STMS, highlighting its role in creating smarter and more sustainable urban transportation networks

A Comprehensive Review on Object Detectors for Urban Mobility on Smart Traffic Management

This comprehensive review explores the landscape of object detectors in the context of urban mobility for smart traffic management. With the increasing complexity of urban environments and the integration of intelligent transportation systems, the demand for accurate and efficient object detection algorithms has surged. This paper provides a thorough examination of state-of-the-art object detectors, evaluating their performance, strengths, and limitations in the specific context of urban mobility. The review encompasses a wide range of detectors, including traditional computer vision methods and modern deep learning approaches, discussing their applicability to real-world urban traffic scenarios. By synthesizing insights from diverse methodologies, this review aims to guide researchers, practitioners, and policymakers in selecting suitable object detectors for enhancing smart traffic management systems in urban settings.

AI Based Smart Traffic Management

A smart city's traffic management system is one of its key components. Road traffic is frequently congested due to the metropolitan cities' rapid population expansion and urban mobility. In this work, a smart traffic management system utilizing the Internet of Things (IoT) is presented to address a variety of challenges for managing traffic on roadways and to aid authorities in efficient planning. Road traffic flow is optimized using a hybrid strategy (a combination of centralized and decentralized), and diverse traffic scenarios are effectively managed using an algorithm. In order to achieve this, the system adjusts traffic signals after receiving input on traffic density from cameras, sensors, and other devices. To reduce traffic congestion, a different artificial intelligence-based method is utilized to forecast future traffic density. Additionally, during a traffic jam, RFIDs are utilized to give priority to emergency vehicles like ambulances and fire brigade vehicles. Smoke sensors are also a component of this system to detect a fire on the road. A prototype is created that not only improves traffic flow but also connects adjacent rescue departments with a centralized server to show the usefulness of the proposed traffic management system. Additionally, it harvests helpful data that is presented in graphical formats, which could aid the government in future road design. Key Words: Traffic control, Traffic light system, Traffic management, Object detection Intelligent transport systems, Smart surveillance, Computer Vision, YOLO, Machine Learning.

Implementation on IoT Based Traffic Management System for Emergency Vehicles

Abstract: In many Indian cities along with those in other nations, congestion caused by traffic is a serious issue. Traffic congestion is caused by signal failure, ineffective law enforcement, and poor traffic administration. The economy, the environment, and general quality of life are all negatively impacted by traffic congestion. Therefore, it is imperative that the traffic congestion issue be managed efficiently. The proposed system's goal is to suggest a smart traffic control and management system that makes use of the Internet of Things, a decentralised strategy and algorithms to handle all traffic circumstances more precisely. The shortcomings of the existing traffic control systems will be fixed by the proposed system. To reduce traffic congestion, a forecast of upcoming traffic density will be made using an algorithm. The proposed method enables an emergency vehicle to travel directly to its location by turning all red lights on its route into green ones, thereby reducing traffic congestion. The system manages traffic signals and reduces wait times during emergencies. This makes it an endeavour that can save a life.

Method for Comprehensive Evaluation of Urban Smart Traffic Management System Based on the 2-tuple Linguistic Neutrosophic Numbers

Method for Comprehensive Evaluation of Urban Smart Traffic Management System Based on the 2-tuple Linguistic Neutrosophic Numbers

Reduction of Ambulance Response Time and Accident Detection using IP and CPM

Abstract: The usage of vehicles is rapidly increasing due to recent technological and economic development, and at the same time, the lack of infrastructure against the demand is leading to an increasing number of accidents and fatality of life. The trivial issues in our life system motivated us to come up with an application to automate this process and save lives. With a review of literature and brainstorming, I proposed the project on a smart traffic management system using image processing. The objective of this project is to develop a detect ambulance using image processing and machine learning techniques. In the first phase, we determined traffic density to minimize the delay caused by traffic congestion and to provide the smooth flow of vehicles. The density of vehicles on each side can be identified by using datasets. If the density is low on a particular side, the period for that side is normal and if the density is high the period will automatically increase compared to normal density. In the second phase, we simulated a crash or accident detection and for the prototype consideration, we have used static accidental image and trained model. In the third phase, analyzed ambulance detection using the dataset, for the prototype consideration for this, used static ambulance image and trained dataset. On detection of an ambulance, the traffic light is automatically changed to green. In each phase, the data updating and monitoring are provided. This scheme is fully automated and identifies the emergency vehicle and controls the traffic lights dynamically. Hence, the traffic management module is done using image processing techniques.

Analysis of Smart Sign Board Design for Road Safety using IoT

Traffic congestion is a major issue, particularly in urban areas where the number of vehicles on the road is continuing to rise considerably, amidst the delayed construction of road infrastructure. Researchers developed a number of traffic management systems to address this intractable issue. Despite the fact that there have been several studies on traffic management, there hasn't been enough done to examine the overall system for managing traffic on urban roads. This study aims to close the knowledge gap by providing fresh information about the current status of research into the complete Traffic Management System. The main goal is to give a comprehensive overview of the current methods suggested in the literature to address the issue of traffic congestion. Moreover, this work provides some ideas by which the road safety management can be done with the help of cloud services. As mobile technology becomes more extensively used, an evaluation approach for such smart transportation innovation strategies is explored with the help of mobile app. Therefore, based on the current research perspective, this survey offers a useful perspective on the deployment of IoT in the smart traffic management system.

A Fuzzy Logic-Based Smart Traffic Management Systems

Traffic congestion is a serious problem in many developing countrieslike Egypt. In the presence of thetraditional traffic light system, it creates health risks, wastes time andfuel, and pollutes the air. Therefore, there is a big need for a smart trafficmanagement system. This study contributes in solving this problem byintroducing an artificial intelligence-based smart traffic light system usingfuzzy logic to ensure the smooth flow of traffic in cities. Research resultsindicate that smart traffic light management is very crucial in smart cities toreduce their carbon footprint to save the world and save energy. Other possibleapplication could be implemented with minor system upgrades such as Detectionand Management of traffic Congestion, Automatic Billing of Toll Charges,Automatic detection of speed limit Violation, Route planning, IntelligentInternet of Vehicles, and Prevention of Road Accidents. Four traffic lightapproaches (fixed-time, smart-time, fixed-time-fuzzy logic, andsmart-time-fuzzy-logic) are developed with different eight traffic scenarios.The four approaches are applied with two tools, the first tool is a realMarquette which consists of 4 roads with only one or two vehicles. The secondis a simulation software which consists of 4 roads with more than 100 vehiclesthrough 10 min. Many sensors such as (IR sensors, rain sensors, and LEDs tocontrol traffic light) are used to collect data and then the fuzzy algorithm isused to ensure the smooth flow of traffic in cities. The traffic data acquiredfrom the vehicles is fed into the proposed model to maximize the duration ofthe green light based on the road state. According to experimental results, theproposed smart fuzzy technique simulation software reduced the average waitingtime of the vehicles from 769 to 289 sec in heavy rain condition. It also reducedthe total time for all vehicles from 2490 to 1154 sec.

Smart Traffic Management System for Metropolitan Cities of Kingdom Using Cutting Edge Technologies

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.

Learning Traffic Network Embeddings for Predicting Congestion Propagation

Traffic congestion has become a global concern due to continuous increase in traffic demand and limited road capacity. The ability to predict traffic congestion propagation, which depicts the spatiotemporal evolution of the congestion scenario, is essential for developing smart traffic management systems and enabling road users to make informed route choices. In this work, we study the behavior of congestion propagation at the road segment level, and leverage this to develop a novel machine learning framework that characterizes and predicts the congestion evolution among different road segments in the traffic network. In particular, our framework can infer the likelihood of congestion propagation between any pair of road segments through single or multiple propagation paths. The proposed framework relies on a network embedding module to learn a representation for each road segment, and a propagation model which calculates the congestion propagation likelihood based on the learned representations. Specifically, an asymmetric embedding of local proximity and global tendency (AE-LPGT) is relied upon for learning low dimension embeddings of the road segments which incorporate various realistic properties of congestion propagations, such as the local proximity property, global propagation tendency, and asymmetric transitivity of congestion propagations. Experimental results with Singapore traffic data show that our method significantly outperforms the state-of-the-art, and the congestion propagation properties in our embeddings have significant impact on the prediction performance.

Dynamic Traffic Control System

A country with a population of 1.3 billion and almost 300 million vehicles, India is one of the biggest contributors to traffic jams, vehicle- specific pollution, and chronic lung diseases. To manage the footfall of this gigantic urban population, and the vehicles, our country has blindly poured in resources towards both active and passive traffic management, investing in measures such as smart traffic management systems and deploying enormous armies of traffic police to handle intersections and exits. The paper aims, towards showcasing a dynamic, fully autonomous model, that uses real-time feeds from existing traffic junctions/ intersection cameras, process them and provide an intensity score based on the density of traffic in each adjoining lane. The system, based upon the intensity scores, provides suitable traffic go time to each lane. The model also scans for emergency vehicles in each lane, to provide a priority pass to such vehicles.

Retraction Note to: Smart traffic management system in metropolitan cities

Retraction Note to: Smart traffic management system in metropolitan cities

Simulation of IoT-based Vehicular Ad Hoc Networks (VANETs) for Smart Traffic Management Systems

Nowadays, traffic congestion and increasing road accidents have become a major concern for both developed and developing countries. To overcome this challenge, an internet of things- (IoT-) based Vehicular Ad Hoc Network (VANET) system is proposed in which vehicles interact with other vehicles and infrastructure. These self-organized ad hoc vehicle networks not only boost traffic safety but also enhances the efficiency of traffic management systems. The VANET systems are beneficial in busy locations, where improved data dissemination protocols are used to categorize a vehicle’s transmission. However, the performance of these VANETs is also hampered by network splits and insufficient connections. Under these situations, the proposed simulation model reduces broadcast storms by minimizing redundancies, which is equally beneficial for both rural and urban settings. The suggested Next Forwarder Vehicle (NFV) protocol is based on three factors: position, distance, and orientation. Moreover, the DDP4V technique is used to analyse each of these features. Results indicate that DDP4V is compatible with 96% of all traffic scenarios and simulation durations. It can transport data packets 60% faster due to its broadcast suppression capabilities. Also, in comparison to AID and DBRS, DDP4V has fewer dispersed packets, which results into reduced retransmissions. For 200 automobiles/km2, all these techniques accomplish 35–40% of the needed cars. However, the coverage also faces some restrictions since data packets only reach vehicles on the same side as the originating vehicle. Similarly, DDP4V delivers 90% coverage for 400 vehicles/km2, which is 30% greater than prior techniques. It is observed that the proposed protocol reduces broadcast storms by employing a waggon wheel to choose the next forwarding vehicle. In high-traffic areas, it outperforms standard techniques. Similarly, it employs a patchwork of vehicles outside of the impacted region to convey data. The industrial IoT-based VANETs provide an effective tool to monitor and control traffic besides reducing the number of traffic accidents.

A Fog Based Smart Traffic Management System

A Fog Based Smart Traffic Management System

Traffic Violation Data Security System

Since the road resources are limited and the number of vehicles on road are increasing at a tremendous rate, traffic monitoring has become a huge challenge and higher management costs have resulted in implementing a manual traffic monitoring system. Many traffic violations are difficult to capture with human observation. It is incontrovertible that a more intelligent and less cost scheme to solve the traffic management problem is necessary. In this paper, we aim to design a smart traffic management system using image processing, which can identify a vehicle’s license plate and add the violations in a blockchain. The computerised traffic monitoring system relies heavily on licence plate detection. The algorithm uses Canny Edge Detection to detect license plate. Tesseract is used as an OCR engine to recognize the characters from the detected license plate. OpenCV module is used with python for image processing and cloud storage is used to store the details of registered license plates. IoT cameras will be placed at every junction and the license plates of traffic violators will be captured. Then the license plate number along with the violation will be added to blockchain and a notification will be sent to violator. If the license plate is not registered, then the anomaly will be reported to Motor Vehicle Department.