Smart Traffic Light Research Articles

The Use of Smart Sensors to Enhance Road Safety: An Experimental Study to Explore the Usability of Smart Traffic Light Systems with Emergency Vehicles

The evolving landscape of transportation systems is being reshaped by the integration of sensors and Internet of Things (IoT) technology, particularly in the context of traffic light systems. These innovations aim to transform traditional traffic lights into smart devices capable of being controlled by emergency vehicles (EVs) to ensure safe passage, thereby enhancing road safety, and reducing intersection accidents. However, there are challenges in implementing such technologies and gaining the trust of EV drivers navigating busy intersections. The traditional fixed-time traffic signal plans are contrasted with the dynamic adjustment made possible by smart sensors and applications, which collect real-time traffic data and adapt signal timings to changing conditions, reducing congestion, and enhancing traffic flow. However, security concerns arise, necessitating the strengthening of systems with advanced technologies to address potential security and safety issues. The technological advancements have a significant impact on the transportation industry. Smart sensors in streetlights play a crucial role in solving long-standing transportation issues, and machine learning is employed for data analysis and traffic forecasting, benefiting both regular and EV drivers. Further research is needed to advance technologies like IoT and smart sensors and to integrate GSM sim cards and GPS to support EV drivers in finding the quickest route to their final destinations. EVs are becoming more than vehicles, evolving into smart devices equipped with technical tools allow drivers to control traffic lights, ensuring a clear path and more safety.

Vehicle Detection and Counting for Traffic Congestion Estimation Using YOLOv5 and DeepSORT in Smart Traffic Light Application

The escalating number of vehicles on the roads has exacerbated trafficcongestion, presenting a significant and inevitable challenge for roadusers.Trafficcongestionnotonlyincreasesthetraveltimeofroadusers;it also causes air pollution since more vehicles are stuck on theroad. To address this issue, deep learning algorithms, including YOLOv5and DeepSORT, have been leveraged to enable vehicle detection and estimate the number of vehicles through image processing. This study focuses on training a custom YOLOv5 dataset of vehicles and compares its performance with a pretrained YOLOv5 dataset in terms of feasibility for detecting and estimating the number of vehicles. In the proposed approach, YOLOv5 is employed to detect vehicles in video streams, DeepSORT is used for vehicle tracking and counting as they pass through the detection zone, and the number of vehicles in each lane is estimated with the aid of Supervision. The custom dataset's validation demonstrates promising results, with the precision curve indicating convergence for all classes at 97.4% precision and an 87% accuracy in predicting vehicle classes. Additionally, the precision-recall curve assesses the ability to detect individual vehicle categories, such as bus, car, motorcycle, and truck, with accuracies of 68.2%, 95.2%, 79.9%, and 70.1%, respectively. Furthermore, the overall accuracy for detecting all vehicle classes combined is 78.3%. The F1 curve indicates that the system achieves a confidence level of 30.9% F1 score, ensuring 78% confidence in accurately predicting all classes. Both the pretrained and custom datasets exhibit similar accuracy in counting the total number of vehicles passing through the detection zone as well as estimating the number of vehicles in each lane. However, it is evident that the custom dataset's performance can be further improved by incorporating more extensive and diverse datasets during the training process to enhance the accuracy of vehicle detection and estimation. In conclusion, the integration of deep learning algorithms, specificallyYOLOv5 and DeepSORT, offers a promising solution for addressing traffic congestion byefficiently detecting and estimating vehicle numbers, which allow traffic systems to identify which lane is congested and prioritise ensuring the congested lane has a smooth traffic flow. Continued research with larger datasets can lead to further advancements and refined results in the field of traffic management and optimization.

Intelligent Traffic Signal Management with PLC

This study introduces a Smart Traffic Light Control System (STLCS) that uses advanced technologies to improve traffic regulation. The key components include a camera-based traffic density detection module and a specialized vehicle identification system using Radio Frequency (RF) technology. The system’s core is an Arduino Uno board along with several converters and a Programmable Logic Controller (PLC). Field tests show that STLCS effectively analyses traffic density and prioritizes emergency vehicles, proving its potential for global traffic management improvements

Smart Traffic Light Control System Using Arduino Mega

Abstract: This article presents the "Smart Traffic Light Control System using Arduino Mega" design. These systems manage traffic flow at intersections, aiming for smooth vehicle movement. However, synchronizing multiple traffic lights at adjacent junctions is complex because of a number of variables. Conventional systems struggle with fluctuating traffic flows and lack consideration for emergencies, pedestrian crossings, and accidents. This often leads to congestion and traffic jams. The suggested method resolves these problems by integrating advanced control mechanisms to adjust traffic signals dynamically, enhancing overall traffic management efficiency and safety

Emergency Vehicle Detection and Traffic Prevention Using Open CV

The proposed project focuses on the development of an innovative system for "Emergency Vehicle Detection and Traffic Prevention using OpenCV." The project aims to enhance road safety and expedite emergency response by leveraging computer vision techniques implemented through the OpenCV framework. The primary objective is to design a robust algorithm that can accurately identify emergency vehicles in real-time based on distinct visual features such as color, shape, and motion patterns. OpenCV, a powerful open-source computer vision library, will serve as the foundation for image processing and analysis. Upon successful detection of an emergency vehicle, the system will employ dynamic traffic prevention measures. This includes the activation of intelligent traffic signal control to ensure the smooth and rapid passage of the emergency vehicle through intersections. The system may also interface with connected infrastructure, such as smart traffic lights, to optimize overall traffic flow and minimize delays. The project aims to contribute to public safety and emergency response efficiency by providing a reliable and automated solution for prioritizing emergency vehicles on the road. Through the integration of OpenCV, the system will offer a versatile and scalable approach to emergency vehicle detection and traffic prevention, making it a valuable contribution to the field of intelligent transportation systems. Index terms Emergency Vehicle Detection, Traffic Prevention, OpenCV, Computer Vision Techniques, Real-time Vehicle Identification, Intelligent Traffic Signal Control, Dynamic Traffic Management, Road Safety Enhancement, Emergency Response Efficiency, Public Safety, Automated Traffic Prioritization, Intelligent Transportation Systems, Visual Feature Analysis, Smart Traffic Lights Integration, Automated Traffic Flow Optimization

SIoV Mobility Management using SDVN-enabled Traffic Light Cooperative Framework

Social Internet of Vehicles (SIoV) is an emerging connected vehicular networking framework among specialized social vehicles to share and disseminate important information like traffic updates, weather conditions, parking slots, etc. This study aims to form an SIoV network among emergency vehicles for their frequent communication to improve the throughput, average waiting time, queue length, and speed during vehicular movement while crossing the intersection in the city. To address this, we propose a novel smart traffic light controller-assisted software-defined vehicular networking-enabled SIoV framework for emergency vehicles. Emergency vehicles form an SIoV network by utilizing Software-Defined Vehicular Networking (SDVN) architecture in Vehicle to Vehicle and Vehicle to Infrastructure communication. The SDVN module is used to offer two essential services: 1) SIoV-based road-lane prioritization, and 2) congestion prevention signal generation for the smart traffic light controller. An SDVN-MP algorithm is proposed to generate an effective traffic light control signal with an SDVN controller feedback signal. Furthermore, to improve the SIoV movement in the city, two levels of prioritization: 1) SIoV, and 2) the road lane with SIoV, are done. The first level of prioritization is to assign higher weightage to the social vehicular entities, and the second level is to prioritize the respective road lane based on SIoV quantity. The proposed framework is validated through a realistic simulation study on the Indian city OpenStreetMap utilizing the Simulation of Urban MObility simulator. The experimental findings demonstrate that the SDVN-MP model enhances (state-of-the-art) comparative performance by 22.5% to 55.2%, 1.2% to 82.7%, 1.6% to 38.4%, and 1.8% to 12.4% for average waiting time, average speed, average queue length, and average throughput metrics, respectively.

Anomaly detection in Smart Traffic Light system using blockchain: Securing through proof of stake and machine learning

<p>The Smart Traffic Light system plays a crucial role in smart cities, contributing significantly to enhancing the overall urban living standards, supporting sustainable practices, assuring public safety, and optimizing operational efficiency. Smart lighting systems leverage advanced technology such as the Internet of Things (IoT), data analytics, and networking to construct a complex and flexible lighting infrastructure. Anomalies within the context of a Smart Light system’s data pertain to patterns, events, or behaviors that are unexpected or uncommon, displaying a considerable deviation from the system’s typical operational state. The distributed and tamper-resistant ledger of blockchain technology renders it well-suited for maintaining an open and immutable record of data and events pertaining to smart lighting systems. The integration of blockchain technology and anomaly detection techniques enables the establishment of a resilient and reliable smart lighting system. The primary objective of this study is to investigate the application of the Isolation Forest algorithm for anomaly detection and its enhancement through the implementation of Proof of Stake for enhanced security measures. The Isolation Forest algorithm is utilized for anomaly prediction and evaluation of accuracy and precision. This is achieved by employing synthetic ground truth labels. Subsequently, the non-anomalous data is incorporated into the system as blocks using blockchain technology. The experimentation involves the use of synthetic numerical data derived from a dataset accessible on Kaggle. This process is conducted in two distinct phases. The first phase focuses on the analysis of synthetic numerical data, specifically targeting the identification of anomalies, prior to the implementation of blockchain technology. In the second phase, the same technique is applied to the synthetic numerical data following the integration of blockchain technology. Various machine learning (ML) models were utilized to analyze the data, resulting in improved accuracy from 48% to 94%, as evidenced by the validation of the obtained results. The empirical results indicate that the utilization of blockchain technology in data processing leads to an improvement in accuracy. Furthermore, the random forest algorithm exhibits robust performance when integrated with blockchain technology.</p>

SDN-enabled Quantized LQR for Smart Traffic Light Controller to Optimize Congestion

Existing intersection management systems, in urban cities, lack in meeting the current requirements of self-configuration, lightweight computing, and software-defined control, which are necessarily required for congested road-lane networks. To satisfy these requirements, this work proposes effective, scalable, multi-input and multi-output, and congestion prevention-enabled intersection management system utilizing a software-defined control interface that not only regularly monitors the traffic to prevent congestion for minimizing queue length and waiting time but also offers a computationally efficient solution in real-time. For effective intersection management, a modified linear-quadratic regulator, i.e., Quantized Linear Quadratic Regulator (QLQR), is designed along with Software-defined Networking (SDN)-enabled control interface to maximize throughput and vehicles speed and minimize queue length and waiting time at the intersection. Experimental results prove that the proposed SDN-QLQR improves the comparative performance in the interval of 24.94%–49.07%, 35.78%–68.86%, 36.67%–59.08%, and 29.94%–57.87% for various performance metrics, i.e., average queue length, average waiting time, throughput, and average speed, respectively.

Deep Learning Algorithm for Optimization of Wait Time at a Traffic Signal Controlled Intersection for Smart Traffic Management

Abstract: The urban landscape is constantly shifting and adapting as a result of factors such as population increase, immigration, and changes in the economy, politics, and culture. Concerns like growing road obstruction, transit delays, pollution levels, consumption of gasoline, etc. have resulted from higher traffic volumes in metropolitan areas. Life expectancy, road safety, environmental quality, and commute times are all impacted by traffic congestion. At junctions, traffic lights play a crucial role in regulating vehicular flow. The traditional pre-timed traffic systems have been ineffective due to the dramatic growth in lights become insufficient in managing traffic clearance which leads to heavy traffic congestion at intersection. Another major issue is that the Emergency vehicles like ambulance, police van, fire brigade experience prolonged waiting time to cross the intersection. For these reasons, development of smart intersections with the aid of technologies such as fuzzy logic, neural networks, artificial intelligence, multiprocessor systems etc. can help to provide the solution and improve the urban traffic management problems. Smart cities are increasingly adopting solutions by using adaptive and intelligent approach to develop smart traffic lights to improve the traffic flow at intersections. The current study on smart traffic light system makes contribution in developing intelligent and adaptive systems to resolve traffic congestion problems at single and connected intersections with an objective to improve traffic clearance and prioritize passage of Emergency vehicle at an isolated intersection. Another problem is the one generally faced by pedestrians as they have to wait for a prolonged interval of time for getting way to cross the lane of major intersection specially at peak traffic hours. This problem has also been considered in the study. System performance has been tested for a twenty-four-hour traffic pattern at an isolated intersection. The outcomes of the simulations showed that the suggested design outperformed the standard fixed-time method in terms of automotive clearing, rescue vehicles passing, and pedestrians wait time.

Faults and Solutions for Smart Traffic Lights

Ambulances, fire departments, and police cars are examples of emergency vehicles (EVs) that greatly benefit communities by saving lives and deterring theft and criminal activity. Automatic control systems are a novel kind of advanced system that function as a driving system using hardware controllers and computers. We require automatic adaptive controllers to manage the traffic light system's open and shut functions since an increase in the number of cars while maintaining the same infrastructure led to a significant traffic issue. In addition to the difficulty emergency vehicles have reaching their location due to traffic congestion caused by heavy cars, traffic problems also include red-light traffic violations, which typically result in accidents on the roadways. Traffic conditions worsen as a result of more cars on the road, making it more difficult to make room for emergency vehicles like ambulances, rescue fire engines, and emergence vehicles. An IoT sensor-based system that detects the arrival of an ambulance at the hotspots using the signals the ambulance sends out can be built to prevent problems waiting for traffic signals in an emergency. The system would then maintain the signals appropriately.

The Positive Impacts of Artificial Intelligence in Highway Transport

This Master's thesis titled "The Positive Impacts of Artificial Intelligence in Highway Transport," explores the transformative effects of artificial intelligence in highway transport from the perspective of users and stakeholders. Focusing on key areas such as traffic control/management, smart traffic lights, predictive maintenance, and autonomous vehicles [1,2], the study employs an in-depth survey analysis using questionnaires. Results indicate notable improvements, with a 24.9% increase in traffic safety, 23.6% reduction in congestion, 15% boost in mobility, 14.7% enhancement in sustainability, and a 21.7% increase in efficiency. These findings offer insights into user/stakeholder perceptions of current artificial intelligence applications in highway transport and their potential benefits. The thesis also evaluates limitations to artificial intelligence implementation in highway transport, providing valuable perspectives for policymakers, industry stakeholders, and researchers.

Smart traffic lights with video vision based on a control minicomputer in Kazakhstani megacities

This article proposes innovative solutions in the field of traffic management – smart traffic lights with computer vision, aimed at improving traffic flow in the large cities of Kazakhstan. With a growing number of vehicles and increasing traffic volume, the issues of congestion and delays at intersections have become increasingly relevant. The article reviews the operational principle of such smart traffic lights, which is based on analyzing computer vision data using sensors and cameras. The benefits of applying this technology, including responsiveness, efficiency, and environmental friendliness, are considered. Furthermore, the potential of smart traffic lights in major megacities of Kazakhstan, such as Astana and Almaty, is analyzed in detail. The conclusion supports the assessment of traffic flow, travel time, and an overall evaluation of the traffic situation. Ultimately, this article highlights the improved performance of smart traffic lights with computer vision coverage in modern cities of Kazakhstan, aiming to ensure higher traffic safety and efficiency.

Vehicle Counting System and Smart Traffic Light Control System

Improving traffic flow efficiency has become more important due to the growing number of vehicles in urban areas, and this requires optimizing traffic control systems. In order to reduce traffic, a smart traffic control system using infrared sharp sensors to count vehicles at intersections is proposed in this paper. The system uses sensors to measure distances to a fixed barrier, accurately counting vehicles while ignoring pedestrians. It was originally designed for four-way intersections but is adaptable to various junction types. Each lane’s unique count is controlled by a separate Arduino Nano Board, and the data is saved in microcontrollers. Following the aggregation and processing of these counts, an Arduino Mega Board modifies the traffic light timings in response to actual vehicle flow.

The Role of Road Safety Units in the Prevention of Child Traffic Injuries

This article describes a number of issues devoted to the role of units responsible for ensuring road safety in the prevention of child road traffic injuries. The author examines the issue of the importance and effectiveness of the activities of units responsible for ensuring road safety in the context of preventing child road traffic injuries. The author analyzes in detail the key aspects of the role of these units in carrying out preventive measures aimed at reducing the risk of injury to children on the roads. A wide range of methods and technologies are discussed, including video surveillance systems, smart traffic lights and automated means of recording traffic violations, which are seen as integral tools for improving safety. Automated means of recording violations of traffic rules further strengthen control over compliance with the rules, recording violations with high accuracy. Together, these technologies form a comprehensive approach to security, based on the use of advanced tools and techniques. Such consideration contributes to the creation of a more effective system for preventing child road traffic injuries. The author highlights the importance of collaboration between safety departments and community organizations in the development and implementation of effective strategies to combat childhood road traffic injuries. The result highlights the need for concerted efforts to create a safe road environment and reduce the risk of injury to children in road situations. In conclusion, the author emphasizes that only through an integrated approach and the introduction of advanced technologies can the safety of children on the roads be ensured, emphasizing the importance of cooperation with the public to achieve this important goal.

Proposed Design and Modeling of Smart Traffic Light System using Arduino

The most benefit from using traffic light signals were they reduced the number of accidents on roads and also helped with the movement of traffic securely without collision. In Libya we use traditional traffic lights which causes collisions in some places. Therefore, this paper proposed a smart system to solve the traffic problem by using dynamic traffic light control. In this system if there is no traffic on the other signal then one should not wait for this signal. The system which includes light traffic will control this signal and move to the next signal. The signal timing changes automatically when traffic intensity is sensed at the intersection. The microcontroller used in this project is "ARDUINO", also there are other devices such as ultrasound sensors (transmitter and receiver) that are mounted on both sides of the road on poles, the aim of this research is to solve and develop the problem of the road traffic, by using C++ language, electric wires, resistance, electronic board and ultrasonic sensors. In the end, fortunately the results were very accurate.

Using Smart Traffic Lights to Reduce CO2 Emissions and Improve Traffic Flow at Intersections: Simulation of an Intersection in a Small Portuguese City

Reducing CO2 emissions is currently a key policy in most developed countries. In this article, we evaluate whether smart traffic lights can have a relevant role in reducing CO2 emissions in small cities, considering their specific traffic profiles. The research method is a quantitative modelling approach tested by computational simulation. We propose a novel microscopic traffic simulation framework, designed to simulate realistic vehicle kinematics and driver behaviour, and accurately estimate CO2 emissions. We also propose and evaluate a routing algorithm for smart traffic lights, specially designed to optimize CO2 emissions at intersections. The simulations reveal that deploying smart traffic lights at a single intersection can reduce CO2 emissions by 32% to 40% in the vicinity of the intersection, depending on the traffic density. The simulations show other advantages for drivers: an increase in average speed of 60% to 101% and a reduction in waiting time of 53% to 95%. These findings can be useful for city-level decision makers who wish to adopt smart technologies to improve traffic flows and reduce CO2 emissions. This work also demonstrates that the simulator can play an important role as a tool to study the impact of smart traffic lights and foster the improvement in smart routing algorithms to reduce CO2 emissions.

IMPROVING MOBILITY IN KEDIRI CITY WITH MKJI ANALYSIS 1997 AND TIME SLICE AT SIMPANG EMPAT DHOHO PLAZA

The city of Kediri, as the third largest city in East Java, has cultural heritage, history and natural beauty that attracts visitors from various regions. However, population growth and motorized vehicles cause traffic jams. Signalized intersections, which are a solution to control intersection conflicts, require knowledge of intersection saturation flows to plan efficient traffic management. Analysis methods such as MKJI 1997 and Time Slice with Vehicle Operating Cost (BOK) calculations help design effective solutions. This research shows that adjusting cycle times, increasing effective width, and optimizing signals can improve transportation system performance and reduce congestion in Kediri City. Cycle time analysis taking into account factors such as vehicle type, traffic volume, and vehicle speed resulted in a cycle time of 90.7 seconds. The impact can be seen in the Degree of Saturation (DS) at various intersection arms, with service variations from E to F. Research also shows a decrease in waiting time/queuing for signal lights from 135 seconds to 118 seconds, which results in a decrease in the degree of saturation (DS) and changes in time cycle. The use of Smart Traffic Lights and traffic engineering, such as immediate left turns on the east arm and additional signs, is required. Evaluation of congestion cost efficiency shows that changing the value of side barriers can save Rp. 22,679,183 in the next five years.

Smart control of traffic lights based on traffic density in the multi-intersection network by using Q learning

In urban areas, utilizing traffic lights to prioritize vehicles at the intersection is a solution to control traffic. Among the smart traffic light methods, the methods based on machine learning are particularly important due to their simplicity and performance. In this paper, Q-learning with deep neural network are combined and used in two different intersection models. The first one is an individual intersection, and the second one is two intersections that are connected and shared their actions. By using this method, the traffic light can make an intelligent decision at the intersection to reduce vehicle consumption time by managing the allocation of phases. The proposed smart traffic light is studied and simulated via SUMO. The results illustrated that compared to fixed-time traffic lights, the average queue time of each vehicle in different traffic scenarios has been reduced by 34% in the individual intersection. In the case of two intersections, awareness and communication between agents led to a 24% reduction in the queue time of all cars in the heavy traffic scenario.

Smart Traffic Light Prototype Implementation using LoRa Technology

Smart traffic light systems have the potential to significantly improve traffic flow and safety in both urban and suburban areas. Most smart traffic lights were deployed in a city with adequate infrastructure. However, building a smart traffic light in suburban areas can be challenging due to the limited availability of communication infrastructure. This paper aims to build and implement a smart traffic light system in a suburban area by incorporating LoRa (Long Range) technology for communication. LoRa is a low-power wireless technology that allows long-distance communication between devices and has low data rates and power consumption. It was created especially for Internet of Things (IoT) devices that run on batteries. The peer-to-peer method was used, which utilized three LoRa nodes for enabling the communication between traffic lights. Usability testing was conducted to determine the impact of the smart traffic light system on traffic flow and safety. The result of the testing shows that the system is necessary to improve traffic management in suburban areas, and the respondents were positive about its potential benefits. Received Signal Strength (RSS) was measured in two different environments: paddy field and residential areas in Kampung Alor Ara, Arau, Perlis. Results show a slight difference in RSS indicator versus distance for the placement of traffic lights in three-junction lanes. The optimum distance between any two traffic lights is 100m in the paddy field area. While for residential areas, the optimum distance is only 50m. The successful implementation of this prototype demonstrates the potential of LoRa technology in enhancing traffic management in suburban areas. Further research such as implementing artificial intelligence (AI) and utilizing advanced LoRa devices and antennas is needed to explore the scalability and long-term sustainability of the system.

Automation of fuzzy systems for intelligent traffic lights

Intense vehicle traffic is one of the main disorders in large cities, and in Brazil, where the responsible authorities have not trained the road networks, overcrowding in traffic causes even more obstacles. The applications of computational intelligence techniques in traffic are very broad, with emphasis on smart traffic lights. For the design of intelligent traffic lights, this work proposes the use of Fuzzy Logic, and has as main objective the automatic generation of fuzzy systems using evolutionary fuzzy models for this purpose. To achieve this objective, the traffic simulation software SUMO is used, which allows the elaboration of scenarios of intersections controlled by traffic lights. In these scenarios, the traffic performance is evaluated based on different adjustments in the membership functions and in the set of rules of the fuzzy system that controls the traffic lights, and these adjustments are performed by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). When comparing the traffic performance with traffic lights controlled by fuzzy and fuzzy with optimized hyperparameters, there are important improvements in the analyzed traffic variables, such as waiting time and car queue size/length, in addition to reducing the emission of toxic gases and fuel consumption. Thus, this work highlights the importance of employing evolutionary fuzzy models in hyperparameters optimization.