The document summarizes a research paper that proposes a new approach called evolutionary reinforcement learning multi-agents system (ERL-MA) for intelligent traffic light control. The ERL-MA system combines computational intelligence and machine learning. It consists of two layers: a modeling layer that uses intersection modeling to understand junction constraints, and a decision layer that uses a novel greedy genetic algorithm and Q-learning to determine optimal phase sequences and signal timings. The approach is evaluated using a real-world traffic simulation scenario from Bologna, Italy. Results show the ERL-MA system achieves competitive performance compared to other adaptive traffic control systems in terms of various metrics.
Integrated tripartite modules for intelligent traffic light systemIJECEIAES
The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
This document discusses using machine learning algorithms to predict traffic flow and reduce congestion at intersections. It compares linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor models on a UK road traffic dataset. All models performed well according to evaluation metrics, indicating they are suitable for an adaptive traffic light system. The system was implemented using a random forest regressor model and simulations showed it reduced traffic congestion by 30.8%, justifying its effectiveness.
A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Inter...ijtsrd
In this paper, a two stage fuzzy logic system has been proposed to control an isolated intersection adaptively. The aim of this work is to minimize the average waiting time for a different traffic flow rates in real time means. In the first stage, the system consists of two modules named next phase selection module and the green phase extension module. In the second stage the system consists of the decision named module. The study was performed using SUMO traffic simulator. A comparison is made between a fuzzy logic controller and a conventional fixed time controller. As a result, fuzzy logic controller has shown better performance. Taha Mahmood | Muzamil Eltejani Mohammed Ali | Akif Durdu ""A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Intersection Based on Real Data using SUMO Simulator"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23873.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23873/a-two-stage-fuzzy-logic-adaptive-traffic-signal-control-for-an-isolated-intersection-based-on-real-data-using-sumo-simulator/taha-mahmood
Towards a new intelligent traffic system based on deep learning and data int...IJECEIAES
Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.
A Brief review to the intelligent controllerswhich used to control trafficflowjournal ijrtem
Abstract: Nowadays, with the social progress and economic development, the transport is playing a pivotal role in cities. The main problem is the traffic jams due to vehicle congestion phenomena at intersection. To solve this problem an intelligent traffic control system that continuously sensing and monitoring traffic conditions and adjusting the timing of traffic lights according to the actual traffic load must be implemented. At present , a variety of traffic control has been designed using electrical technologies.Traffic load is highly dependent on parameters such as day-time , season weather and unpredictable situationssuch as accidents, special events or construction activities, these parameters will cause delay on the traffic flow. The traffic system in Libya is still controlled by old fashion ( i.e equally time interval signal control)and no intelligent system used to monitor and control the traffic flow. The scope of this paper is to review the main Intelligent controllerswhich used in smart traffic systems. Keywords: Traffic, Intelligent control, Programmable logic, Neural network, Fuzzy logic
This document presents a study on developing an artificial intelligence system to manage real-time traffic. The study developed a traffic simulator using Python to model vehicle and traffic light behavior at an intersection. A linear regression model was then used to control the traffic lights dynamically based on current traffic conditions, collected from sensors. Testing showed the AI-based dynamic system improved traffic flow compared to a static traffic light system, allowing more vehicles to pass through the intersection in a given time period. The authors conclude the linear regression model provides better real-time traffic management than existing approaches and suggest further improving it with deep learning techniques.
A multi-objective evolutionary scheme for control points deployment in intell...IJECEIAES
One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the Non dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength pareto evolutionary algorithm-II (SPEA-II); and the pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.
IRJET- Simulation based Automatic Traffic Controlling SystemIRJET Journal
This document summarizes a research paper that proposes a simulation-based automatic traffic controlling system. The system uses image processing and algorithms like SCOOT (Split Cycle Offset Optimization Technique) or UTC (Urban Traffic Control) to determine optimal traffic light timing based on real-time vehicle counts. It aims to reduce traffic congestion and waiting times by adapting light cycles dynamically. The system prioritizes lanes with emergency vehicles by stopping other lanes. It was tested in simulations of a four-way intersection with results showing promise for improving traffic flow. Further work is needed to coordinate traffic lights across multiple intersections.
Integrated tripartite modules for intelligent traffic light systemIJECEIAES
The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
This document discusses using machine learning algorithms to predict traffic flow and reduce congestion at intersections. It compares linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor models on a UK road traffic dataset. All models performed well according to evaluation metrics, indicating they are suitable for an adaptive traffic light system. The system was implemented using a random forest regressor model and simulations showed it reduced traffic congestion by 30.8%, justifying its effectiveness.
A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Inter...ijtsrd
In this paper, a two stage fuzzy logic system has been proposed to control an isolated intersection adaptively. The aim of this work is to minimize the average waiting time for a different traffic flow rates in real time means. In the first stage, the system consists of two modules named next phase selection module and the green phase extension module. In the second stage the system consists of the decision named module. The study was performed using SUMO traffic simulator. A comparison is made between a fuzzy logic controller and a conventional fixed time controller. As a result, fuzzy logic controller has shown better performance. Taha Mahmood | Muzamil Eltejani Mohammed Ali | Akif Durdu ""A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Intersection Based on Real Data using SUMO Simulator"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23873.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23873/a-two-stage-fuzzy-logic-adaptive-traffic-signal-control-for-an-isolated-intersection-based-on-real-data-using-sumo-simulator/taha-mahmood
Towards a new intelligent traffic system based on deep learning and data int...IJECEIAES
Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.
A Brief review to the intelligent controllerswhich used to control trafficflowjournal ijrtem
Abstract: Nowadays, with the social progress and economic development, the transport is playing a pivotal role in cities. The main problem is the traffic jams due to vehicle congestion phenomena at intersection. To solve this problem an intelligent traffic control system that continuously sensing and monitoring traffic conditions and adjusting the timing of traffic lights according to the actual traffic load must be implemented. At present , a variety of traffic control has been designed using electrical technologies.Traffic load is highly dependent on parameters such as day-time , season weather and unpredictable situationssuch as accidents, special events or construction activities, these parameters will cause delay on the traffic flow. The traffic system in Libya is still controlled by old fashion ( i.e equally time interval signal control)and no intelligent system used to monitor and control the traffic flow. The scope of this paper is to review the main Intelligent controllerswhich used in smart traffic systems. Keywords: Traffic, Intelligent control, Programmable logic, Neural network, Fuzzy logic
This document presents a study on developing an artificial intelligence system to manage real-time traffic. The study developed a traffic simulator using Python to model vehicle and traffic light behavior at an intersection. A linear regression model was then used to control the traffic lights dynamically based on current traffic conditions, collected from sensors. Testing showed the AI-based dynamic system improved traffic flow compared to a static traffic light system, allowing more vehicles to pass through the intersection in a given time period. The authors conclude the linear regression model provides better real-time traffic management than existing approaches and suggest further improving it with deep learning techniques.
A multi-objective evolutionary scheme for control points deployment in intell...IJECEIAES
One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the Non dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength pareto evolutionary algorithm-II (SPEA-II); and the pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.
IRJET- Simulation based Automatic Traffic Controlling SystemIRJET Journal
This document summarizes a research paper that proposes a simulation-based automatic traffic controlling system. The system uses image processing and algorithms like SCOOT (Split Cycle Offset Optimization Technique) or UTC (Urban Traffic Control) to determine optimal traffic light timing based on real-time vehicle counts. It aims to reduce traffic congestion and waiting times by adapting light cycles dynamically. The system prioritizes lanes with emergency vehicles by stopping other lanes. It was tested in simulations of a four-way intersection with results showing promise for improving traffic flow. Further work is needed to coordinate traffic lights across multiple intersections.
Improving traffic and emergency vehicle clearance at congested intersections ...IJECEIAES
Traffic signals play an important role in controlling and coordinating the traffic movement in cities especially in urban areas. As the traffic is exponentially increasing in cities and the pre-timed traffic light control is insufficient in effective timing of the traffic lights, it leads to poor traffic clearance and ultimately to heavy traffic congestion at intersections. Even the Emergency vehicles like Ambulance and Fire brigade are struck at such intersections and experience a prolonged waiting time. An adaptive and intelligent approach in design of traffic light signals is desirable and this paper contributes in applying fuzzy logic to control traffic signal of single four-way intersection giving priority to the Emergency vehicle clearance. The proposed control system is composed of two parallel controllers to select the appropriate lane for green signal and also to decide the appropriate green light time as per the real time traffic condition. Performance of the proposed system is evaluated by using simulations and comparing with pre-timed control system in changing traffic flow condition. Simulation results show significant improvement over the pre-timed control in terms of traffic clearance and lowering of Emergency vehicle wait time at the intersection especially when traffic intensity is high.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
This document describes research into using the Tecnomatix Plant Simulation software to simulate and visualize traffic processes at a traffic node. The researchers created a simulation model and methodology for simulating traffic light intersections using this software, which was originally intended for production and logistics processes. Their goal was to develop a simulation model that could jointly simulate both production logistics and city logistics processes. Their work demonstrates that Tecnomatix Plant Simulation has potential for creating microscopic traffic simulation models and visualizing traffic processes at intersections. The proposed methodology could enable combining simulations of production logistics and their effects on public transportation networks and urban mobility in a single integrated model.
A novel k-means powered algorithm for an efficient clustering in vehicular ad...IJECEIAES
Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%.
Fuzzy Logic Model for Traffic CongestionIOSR Journals
Abstract: Traffic congestion has become a serious problem in the urban districts. This is mainly due to the
rapid increase in the number and the use of vehicles. Travel time, travel safety, environmental quality, and life
quality are all adversely affected by traffic congestion. Many traffic control systems have been developed and
installed to alleviate the problem with limited success. Traffic demands are still high and increasing. The main
focus of this report is to introduce a versatile fuzzy logic traffic flow model capable of making optimal traffic
predictions. This model can be used to evaluate various traffic-light timing plans. More importantly, it provides
a framework for implementing adaptive traffic signal controllers based on fuzzy logic technology. When
implemented it solved the problem of waiting time, travel cost, accident, traffic congestion.
Key words: Traffic Congestion, fuzzy logic, Traffic Density, fuzzy controller, conventional controller.
Integrating Machine Learning and Traffic Simulation for Enhanced Traffic Mana...IRJET Journal
This document presents a research study that integrates machine learning and traffic simulation to optimize traffic management. The study develops a traffic simulator using Pygame library that generates vehicles in four lanes. Machine learning models analyze vehicle distribution and predict traffic patterns to intelligently adjust signal timings. This reduces congestion and improves traffic flow by prioritizing lanes with higher vehicle density. The study shows the proposed approach is effective in optimizing traffic flow compared to fixed signal timings.
Traffic Lights Control System for Indian Cities using WSN and Fuzzy ControlIRJET Journal
This document proposes a traffic light control system for Indian cities that uses a wireless sensor network (WSN) and fuzzy control. Sensors would monitor traffic in real-time and transmit data to a centralized control system. Multiple fuzzy logic controllers, one for each traffic light phase, would work in parallel to dynamically manage both the phase timing and green light times. This approach aims to reduce vehicle wait times under heavy traffic by combining the advantages of WSNs and parallel fuzzy controllers that can manage phases individually. A simulation showed this multi-controller approach outperformed single-controller methods.
Smart Traffic Congestion Control System: Leveraging Machine Learning for Urba...IRJET Journal
This document proposes a smart traffic congestion control system that leverages machine learning technologies like CNNs, YOLOv4, LSTM, and PPO to optimize traffic flow in urban environments. The system aims to dynamically adjust signal timings in real-time using data analysis and predictive modeling from cameras and sensors. Convolutional neural networks are used for congestion detection from camera images, while YOLOv4 performs object detection to ensure safety. LSTM networks capture temporal traffic data for predictions, and PPO optimizes signal timings based on current conditions. The system has potential to revolutionize traffic management by intelligently reducing congestion through data-driven decision making.
A VISION-BASED REAL-TIME ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM USING VEHICULA...JANAK TRIVEDI
In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.
Real time deep-learning based traffic volume count for high-traffic urban art...Conference Papers
This document proposes and tests a deep learning-based system for real-time traffic volume counting on high-traffic urban arterial roads. Video clips from 4 camera views along arterial roads with estimated annual average daily traffic over 50,000 vehicles were used to test the system. The system achieved average accuracy rates between 93.84-97.68% across the camera views for 5 and 15-minute video clips. It was also able to process frames in real-time at an average of 37.27ms per frame. The proposed system provides an accurate and efficient method for traffic authorities to conduct traffic volume surveys on busy urban roads.
Design of intelligent traffic light controller using gsm & embedded systemYakkali Kiran
This document describes the design of an intelligent traffic light controller using an embedded system. The proposed system aims to make traffic light control more efficient by using sensor networks and embedded technology to dynamically determine light timings based on real-time traffic conditions. This allows the system to optimize traffic flow and reduce congestion compared to traditional fixed-time controllers. Key features include emergency vehicle detection and providing traffic information to drivers via GSM. The performance of the intelligent controller is evaluated against a conventional fixed-time controller based on metrics like waiting time, vehicle travel distance, and efficient emergency response.
This document summarizes research on inter-vehicular communication using packet network theory. It discusses how vehicle-to-vehicle and vehicle-to-infrastructure communication can improve road safety and efficiency. The paper proposes using localization techniques combined with GPS to determine vehicle positions, and applying congestion algorithms to decongest traffic lanes. It also outlines algorithms for lane detection, pedestrian detection, and modifying Dijkstra's algorithm for optimal vehicle routing.
Comparative study of traffic signals with and without signal coordination of ...IRJET Journal
This document presents a study that compares traffic signals with and without signal coordination at various intersections.
The study focuses on quantifying congestion at intersections by updating signal timing to improve intersection capacity, reduce delays, and enhance overall traffic efficiency. Signal coordination is identified as the most effective method to maximize vehicle flow across intersections with minimum stops and accidents.
The study designs traffic signals for various intersections based on field data using Webster's method. Signal timing and offsets are theoretically coordinated for a route between intersections to establish a green wave bandwidth. Simulation results show that with coordination, delays, queue lengths and fuel consumption are reduced compared to without coordination.
Comparative study of traffic signals with and without signal coordination of ...IRJET Journal
1) The document presents a comparative study of traffic signals with and without signal coordination at various intersections. It aims to quantify congestion and update signal timing to improve traffic flow.
2) A literature review is presented on previous studies related to signal optimization and coordination. Simulation software is used to model traffic behavior and coordinate signal timing.
3) Field data on traffic volume and speed is collected. Signals are designed using Webster's method and coordinated theoretically to maximize green bandwidth. Simulation results show reduced delays, queue lengths and fuel consumption with coordination.
IRJET- Image Processing based Intelligent Traffic Control and Monitoring ...IRJET Journal
This document summarizes a research paper on an intelligent traffic control and monitoring system using image processing and the Internet of Things. The system aims to reduce traffic congestion by controlling traffic lights based on real-time traffic density detected through image processing of vehicle images. It consists of hardware and software modules. The hardware uses cameras to capture vehicle images and the software uses image processing techniques like object detection and classification to detect and count vehicles in real-time and estimate traffic density. This information is then used to dynamically adjust traffic light timings with the goal of optimizing traffic flow and reducing waiting times at signals. The system is meant to provide a more efficient solution to traffic management than conventional fixed-time traffic light control systems.
Automated signal pre-emption system for emergency vehicles using internet of ...IAESIJAI
Vehicle administration systems are one of the major highlights especially in urban areas. One important critical component that requires attention are signal preemption systems. Every single work on traffic congestion identification either requires prior learning or long time to distinguish and perceive the closeness of congestion. FutureSight performs predictive analysis and control of traffic signals through the application of machine learning to aide ambulances in such a way that, a signal turns green beforehand so as to ensure an obstacle free path to the ambulance from source to destination based on various parameters such as traffic density, congestion length, previous wait times, arrival time thereby eliminating the need for human intervention. The method allows flexible interface to the driver to enter the hospital details to reach the destination with in time. The app then plans out the fastest route from the pickup spot to the selected hospital and sends this route to the system. The system then predict the amount of time that is required by the signal to remain green so as to clear all traffic at that specific junction before the ambulance arrives at that location.
Intelligent Traffic Light Control SystemIRJET Journal
This document proposes an Intelligent Traffic Light Control System (ITLCS) that uses cameras and deep learning to classify vehicles and dynamically adjust traffic light timings based on real-time traffic conditions. The system aims to reduce average wait times and account for changes in traffic to ensure optimal traffic flow and safety. It would require object detection using data acquisition and training a deep learning model to identify vehicle classes. Implementing ITLCS could address traffic congestion issues and reduce accidents at intersections by providing a more efficient alternative to traditional static traffic control systems.
IRJET - Unmanned Traffic Signal Monitoring SystemIRJET Journal
This document describes a proposed unmanned traffic signal monitoring system that uses image processing and computer vision techniques. A camera would be installed alongside traffic lights to capture images of the road. Image processing would be used to calculate traffic density in real-time based on the images in order to dynamically switch the traffic light signals according to vehicle congestion. The system aims to reduce traffic congestion by minimizing the time green lights are on for empty roads. It would also detect ambulances using ZigBee transmission and switch all traffic lights to green to clear a path for emergency vehicles.
Congestion Control System Using Machine LearningIRJET Journal
This document proposes a machine learning-based system to address road congestion problems. It uses ML algorithms programmed in Python to develop automated traffic management solutions that can handle large volumes of traffic and ensure emergency vehicles like ambulances can move through congested roads quickly. The system detects vehicles like ambulances and motorcycles without helmets using object detection algorithms like YOLOv4. It recognizes license plates and sends violation notices to motorcycle riders detected without helmets. The system aims to provide priority to emergency vehicles at traffic lights using a Compact Prediction Tree algorithm based on deep learning. It analyzes previous research on dynamic traffic light control systems and proposes developing a continuous surveillance system and automated priority system for emergency vehicles.
Traffic light control design approaches: a systematic literature reviewIJECEIAES
To assess different approaches to traffic light control design, a systematic literature review was conducted, covering publications from 2006 to 2020. The review’s aim was to gather and examine all studies that looked at road traffic and congestion issues. As well, it aims to extract and analyze protruding techniques from selected research articles in order to provide researchers and practitioners with recommendations and solutions. The research approach has placed a strong emphasis on planning, performing the analysis, and reporting the results. According to the results of the study, there has yet to be developed a specific design that senses road traffic and provides intelligent solutions. Dynamic time intervals, learning capability, emergency priority management, and intelligent functionality are all missing from the conventional design approach. While learning skills in the adaptive self-organization strategy were missed. Nonetheless, the vast majority of intelligent design approach papers lacked intelligent fear tires and learning abilities.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Improving traffic and emergency vehicle clearance at congested intersections ...IJECEIAES
Traffic signals play an important role in controlling and coordinating the traffic movement in cities especially in urban areas. As the traffic is exponentially increasing in cities and the pre-timed traffic light control is insufficient in effective timing of the traffic lights, it leads to poor traffic clearance and ultimately to heavy traffic congestion at intersections. Even the Emergency vehicles like Ambulance and Fire brigade are struck at such intersections and experience a prolonged waiting time. An adaptive and intelligent approach in design of traffic light signals is desirable and this paper contributes in applying fuzzy logic to control traffic signal of single four-way intersection giving priority to the Emergency vehicle clearance. The proposed control system is composed of two parallel controllers to select the appropriate lane for green signal and also to decide the appropriate green light time as per the real time traffic condition. Performance of the proposed system is evaluated by using simulations and comparing with pre-timed control system in changing traffic flow condition. Simulation results show significant improvement over the pre-timed control in terms of traffic clearance and lowering of Emergency vehicle wait time at the intersection especially when traffic intensity is high.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
This document describes research into using the Tecnomatix Plant Simulation software to simulate and visualize traffic processes at a traffic node. The researchers created a simulation model and methodology for simulating traffic light intersections using this software, which was originally intended for production and logistics processes. Their goal was to develop a simulation model that could jointly simulate both production logistics and city logistics processes. Their work demonstrates that Tecnomatix Plant Simulation has potential for creating microscopic traffic simulation models and visualizing traffic processes at intersections. The proposed methodology could enable combining simulations of production logistics and their effects on public transportation networks and urban mobility in a single integrated model.
A novel k-means powered algorithm for an efficient clustering in vehicular ad...IJECEIAES
Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%.
Fuzzy Logic Model for Traffic CongestionIOSR Journals
Abstract: Traffic congestion has become a serious problem in the urban districts. This is mainly due to the
rapid increase in the number and the use of vehicles. Travel time, travel safety, environmental quality, and life
quality are all adversely affected by traffic congestion. Many traffic control systems have been developed and
installed to alleviate the problem with limited success. Traffic demands are still high and increasing. The main
focus of this report is to introduce a versatile fuzzy logic traffic flow model capable of making optimal traffic
predictions. This model can be used to evaluate various traffic-light timing plans. More importantly, it provides
a framework for implementing adaptive traffic signal controllers based on fuzzy logic technology. When
implemented it solved the problem of waiting time, travel cost, accident, traffic congestion.
Key words: Traffic Congestion, fuzzy logic, Traffic Density, fuzzy controller, conventional controller.
Integrating Machine Learning and Traffic Simulation for Enhanced Traffic Mana...IRJET Journal
This document presents a research study that integrates machine learning and traffic simulation to optimize traffic management. The study develops a traffic simulator using Pygame library that generates vehicles in four lanes. Machine learning models analyze vehicle distribution and predict traffic patterns to intelligently adjust signal timings. This reduces congestion and improves traffic flow by prioritizing lanes with higher vehicle density. The study shows the proposed approach is effective in optimizing traffic flow compared to fixed signal timings.
Traffic Lights Control System for Indian Cities using WSN and Fuzzy ControlIRJET Journal
This document proposes a traffic light control system for Indian cities that uses a wireless sensor network (WSN) and fuzzy control. Sensors would monitor traffic in real-time and transmit data to a centralized control system. Multiple fuzzy logic controllers, one for each traffic light phase, would work in parallel to dynamically manage both the phase timing and green light times. This approach aims to reduce vehicle wait times under heavy traffic by combining the advantages of WSNs and parallel fuzzy controllers that can manage phases individually. A simulation showed this multi-controller approach outperformed single-controller methods.
Smart Traffic Congestion Control System: Leveraging Machine Learning for Urba...IRJET Journal
This document proposes a smart traffic congestion control system that leverages machine learning technologies like CNNs, YOLOv4, LSTM, and PPO to optimize traffic flow in urban environments. The system aims to dynamically adjust signal timings in real-time using data analysis and predictive modeling from cameras and sensors. Convolutional neural networks are used for congestion detection from camera images, while YOLOv4 performs object detection to ensure safety. LSTM networks capture temporal traffic data for predictions, and PPO optimizes signal timings based on current conditions. The system has potential to revolutionize traffic management by intelligently reducing congestion through data-driven decision making.
A VISION-BASED REAL-TIME ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM USING VEHICULA...JANAK TRIVEDI
In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.
Real time deep-learning based traffic volume count for high-traffic urban art...Conference Papers
This document proposes and tests a deep learning-based system for real-time traffic volume counting on high-traffic urban arterial roads. Video clips from 4 camera views along arterial roads with estimated annual average daily traffic over 50,000 vehicles were used to test the system. The system achieved average accuracy rates between 93.84-97.68% across the camera views for 5 and 15-minute video clips. It was also able to process frames in real-time at an average of 37.27ms per frame. The proposed system provides an accurate and efficient method for traffic authorities to conduct traffic volume surveys on busy urban roads.
Design of intelligent traffic light controller using gsm & embedded systemYakkali Kiran
This document describes the design of an intelligent traffic light controller using an embedded system. The proposed system aims to make traffic light control more efficient by using sensor networks and embedded technology to dynamically determine light timings based on real-time traffic conditions. This allows the system to optimize traffic flow and reduce congestion compared to traditional fixed-time controllers. Key features include emergency vehicle detection and providing traffic information to drivers via GSM. The performance of the intelligent controller is evaluated against a conventional fixed-time controller based on metrics like waiting time, vehicle travel distance, and efficient emergency response.
This document summarizes research on inter-vehicular communication using packet network theory. It discusses how vehicle-to-vehicle and vehicle-to-infrastructure communication can improve road safety and efficiency. The paper proposes using localization techniques combined with GPS to determine vehicle positions, and applying congestion algorithms to decongest traffic lanes. It also outlines algorithms for lane detection, pedestrian detection, and modifying Dijkstra's algorithm for optimal vehicle routing.
Comparative study of traffic signals with and without signal coordination of ...IRJET Journal
This document presents a study that compares traffic signals with and without signal coordination at various intersections.
The study focuses on quantifying congestion at intersections by updating signal timing to improve intersection capacity, reduce delays, and enhance overall traffic efficiency. Signal coordination is identified as the most effective method to maximize vehicle flow across intersections with minimum stops and accidents.
The study designs traffic signals for various intersections based on field data using Webster's method. Signal timing and offsets are theoretically coordinated for a route between intersections to establish a green wave bandwidth. Simulation results show that with coordination, delays, queue lengths and fuel consumption are reduced compared to without coordination.
Comparative study of traffic signals with and without signal coordination of ...IRJET Journal
1) The document presents a comparative study of traffic signals with and without signal coordination at various intersections. It aims to quantify congestion and update signal timing to improve traffic flow.
2) A literature review is presented on previous studies related to signal optimization and coordination. Simulation software is used to model traffic behavior and coordinate signal timing.
3) Field data on traffic volume and speed is collected. Signals are designed using Webster's method and coordinated theoretically to maximize green bandwidth. Simulation results show reduced delays, queue lengths and fuel consumption with coordination.
IRJET- Image Processing based Intelligent Traffic Control and Monitoring ...IRJET Journal
This document summarizes a research paper on an intelligent traffic control and monitoring system using image processing and the Internet of Things. The system aims to reduce traffic congestion by controlling traffic lights based on real-time traffic density detected through image processing of vehicle images. It consists of hardware and software modules. The hardware uses cameras to capture vehicle images and the software uses image processing techniques like object detection and classification to detect and count vehicles in real-time and estimate traffic density. This information is then used to dynamically adjust traffic light timings with the goal of optimizing traffic flow and reducing waiting times at signals. The system is meant to provide a more efficient solution to traffic management than conventional fixed-time traffic light control systems.
Automated signal pre-emption system for emergency vehicles using internet of ...IAESIJAI
Vehicle administration systems are one of the major highlights especially in urban areas. One important critical component that requires attention are signal preemption systems. Every single work on traffic congestion identification either requires prior learning or long time to distinguish and perceive the closeness of congestion. FutureSight performs predictive analysis and control of traffic signals through the application of machine learning to aide ambulances in such a way that, a signal turns green beforehand so as to ensure an obstacle free path to the ambulance from source to destination based on various parameters such as traffic density, congestion length, previous wait times, arrival time thereby eliminating the need for human intervention. The method allows flexible interface to the driver to enter the hospital details to reach the destination with in time. The app then plans out the fastest route from the pickup spot to the selected hospital and sends this route to the system. The system then predict the amount of time that is required by the signal to remain green so as to clear all traffic at that specific junction before the ambulance arrives at that location.
Intelligent Traffic Light Control SystemIRJET Journal
This document proposes an Intelligent Traffic Light Control System (ITLCS) that uses cameras and deep learning to classify vehicles and dynamically adjust traffic light timings based on real-time traffic conditions. The system aims to reduce average wait times and account for changes in traffic to ensure optimal traffic flow and safety. It would require object detection using data acquisition and training a deep learning model to identify vehicle classes. Implementing ITLCS could address traffic congestion issues and reduce accidents at intersections by providing a more efficient alternative to traditional static traffic control systems.
IRJET - Unmanned Traffic Signal Monitoring SystemIRJET Journal
This document describes a proposed unmanned traffic signal monitoring system that uses image processing and computer vision techniques. A camera would be installed alongside traffic lights to capture images of the road. Image processing would be used to calculate traffic density in real-time based on the images in order to dynamically switch the traffic light signals according to vehicle congestion. The system aims to reduce traffic congestion by minimizing the time green lights are on for empty roads. It would also detect ambulances using ZigBee transmission and switch all traffic lights to green to clear a path for emergency vehicles.
Congestion Control System Using Machine LearningIRJET Journal
This document proposes a machine learning-based system to address road congestion problems. It uses ML algorithms programmed in Python to develop automated traffic management solutions that can handle large volumes of traffic and ensure emergency vehicles like ambulances can move through congested roads quickly. The system detects vehicles like ambulances and motorcycles without helmets using object detection algorithms like YOLOv4. It recognizes license plates and sends violation notices to motorcycle riders detected without helmets. The system aims to provide priority to emergency vehicles at traffic lights using a Compact Prediction Tree algorithm based on deep learning. It analyzes previous research on dynamic traffic light control systems and proposes developing a continuous surveillance system and automated priority system for emergency vehicles.
Traffic light control design approaches: a systematic literature reviewIJECEIAES
To assess different approaches to traffic light control design, a systematic literature review was conducted, covering publications from 2006 to 2020. The review’s aim was to gather and examine all studies that looked at road traffic and congestion issues. As well, it aims to extract and analyze protruding techniques from selected research articles in order to provide researchers and practitioners with recommendations and solutions. The research approach has placed a strong emphasis on planning, performing the analysis, and reporting the results. According to the results of the study, there has yet to be developed a specific design that senses road traffic and provides intelligent solutions. Dynamic time intervals, learning capability, emergency priority management, and intelligent functionality are all missing from the conventional design approach. While learning skills in the adaptive self-organization strategy were missed. Nonetheless, the vast majority of intelligent design approach papers lacked intelligent fear tires and learning abilities.
Similar to Evolutionary reinforcement learning multi-agents system for intelligent traffic light control: new approach and case of study (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
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Evolutionary reinforcement learning multi-agents system for intelligent traffic light control: new approach and case of study
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 5, October 2022, pp. 5519~5530
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i5.pp5519-5530 5519
Journal homepage: http://ijece.iaescore.com
Evolutionary reinforcement learning multi-agents system for
intelligent traffic light control: new approach and case of study
Mohamed Amine Basmassi1
, Sidina Boudaakat2
, Jihane Alami Chentoufi1
, Lamia Benameur3
,
Ahmed Rebbani2
, Omar Bouattane2
1
Informatics, Systems and Optimization Laboratory, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco
2
Signals Distributed Systems and Artificial Intelligence Laboratory, Ecole Normale Supérieure de l'Enseignement Technique
Mohammedia, Hassan II University of Casablanca, Mohammedia, Morocco
3
Information Technology and Systems Modelization Laboratory, Faculty of Sciences, Abdelmalik Essaadi University, Tétouan, Morocco
Article Info ABSTRACT
Article history:
Received Jun 29, 2021
Revised May 28, 2022
Accepted Jun 25, 2022
Due to the rapid growth of urban vehicles, traffic congestion has become
more serious. The signalized intersections are used all over the world and
still established in the new construction. This paper proposes a self-adapted
approach, called evolutionary reinforcement learning multi-agents system
(ERL-MA), which combines computational intelligence and machine
learning. The concept of this work is to build an intelligent agent capable of
developing senior skills to manage the traffic light control system at any
type of junction, using two powerful tools: learning from the confronted
experience and the assumption using the randomization concept. The
ERL-MA is an independent multi-agents system composed of two layers:
the modeling and the decision layers. The modeling layer uses the
intersection modeling using generalized fuzzy graph technique. The decision
layer uses two methods: the novel greedy genetic algorithm (NGGA), and
the Q-learning. In the Q-learning method, a multi Q-tables strategy and a
new reward formula are proposed. The experiments used in this work relied
on a real case of study with a simulation of one-hour scenario at Pasubio
area in Italy. The obtained results show that the ERL-MA system succeeds
to achieve competitive results comparing to urban traffic optimization by
integrated automation (UTOPIA) system using different metrics.
Keywords:
Adaptive traffic signal control
Fuzzy modeling
Genetic algorithm
Q-learning
Reinforcement learning
Traffic simulation
This is an open access article under the CC BY-SA license.
Corresponding Author:
Mohamed Amine Basmassi
Laboratory ISO, Faculty of Sciences, Ibn Tofail University, University Campus
Kenitra, BP 133, Morocco
Email: basmassi.med.amine@gmail.com
1. INTRODUCTION
The traffic congestion is one of the biggest problems related to big cities in the world. This problem
can be frequented for several reasons such as peak periods and big events or at tourist destinations [1] which
not only impacts people’s travel but also limits the development of the urban economy. Traffic control is one
of the important tools to contain the congestion, restrain traffic flow and reduce emissions. Many researchers
have proposed adaptive traffic signal control (ATSC) to solve the traffic congestion problem using a variety
of optimization techniques, such as heuristics [2]–[4], evolutionary algorithms [5], [6] and self-organization
strategy [7].
Several researchers have studied this problem using different concepts. The ATSC has the potential
to efficiently adjust signal timing in real time conforming to travel demand, weather or seasonal traffic
fluctuations. Systems using this technology have outperformed the actuated control and pre-timed methods
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[8]. The two most famous ATSC are the split cycle offset optimization technique (SCOOT) [9] and Sydney
coordinated adaptive traffic system (SCATS). The optimization process of those systems is based on real
time data and predictions to assign a signal timing plan. Other control system like optimization policies for
adaptive control (OPAC) [10] and real time hierarchical optimized distributed effective system (RHODES)
[11] using similar principle where they intervene into the fluctuation of the time variation by dynamically
adjusting the signal timing parameters. Also, the urban traffic optimization by integrated automation
(UTOPIA) [12] is a hierarchical decentralized traffic signal control strategy used in many countries. It
focuses on optimizing the traffic flows and gives selective priority to public transport with taking into
consideration the travel times of the private traffic [13].
Recently, a new generation of traffic control system starts the implementation of machine learning
methods, where multiple optimization and prediction techniques have been beneficial to create more
powerful system with the ability to solve complex traffic condition issues [14]. Reinforcement learning (RL)
is a third machine learning paradigm that is used for self-learning traffic signal control in the stochastic road
traffic environment [15], [16]. Frequent studies have proved the capabilities to solve the ATSC problem
using the Q-learning which is a RL method [15].
In this perspective, this paper presents a new approach called evolutionary reinforcement learning
multi-agents system (ERL-MA) which is an independent multi-agents system composed of two layers. The
first one is the modeling layer that uses the information provided by the intersection modeling using
generalized fuzzy graph (IMGFG) [17] to understand the junction constraint and complexity. The second one
is the decision layer which is composed of two activities: activity per cycle and activity per phase. In fact, the
novel greedy genetic algorithm (NGGA) [18] is used in the activity per cycle to compose the optimal
sequence of phases, and the accumulated knowledge of the Q-learning is applied to generate flexible signal
traffic light timing in the activity per phase.
A real-world scenario from Bologna city, built in the project of iTERTRIS (an integrated wireless
and traffic platform for real-time road traffic management solutions), was prepared and described by Bieker
et al. [19]. This real-world scenario is used as a case of study, which gives the opportunity for the best
evaluation and appropriate demonstration of the proposed approach. This paper is organized. In the next
section, the background of this study is presented. In section 3, the proposed approach is detailed. The
experimental results are shown in section 4 where a real case of study is simulated. Finally, a discussion and
conclusion are given in section 5.
2. BACKGROUND
This section will cover the background of the material presented in the paper. The first step in
optimizing the signal timing for traffic congestion is finding a powerful model; this model should present the
junction’s influencing parameters and display their correlation. The traffic congestion problem in signalized
junction (or intersection) depends on traffic fluctuations, green timing, cycle length, phasing. Therefore, an
efficient system should have a powerful model to present the influencing parameters of the junction.
Modeling traffic behavior has been for years interesting issue research, traditionally accomplished using a
variety of methods. As queuing theory [20], Dotoli and Fanti [21], cell transmission model [22] and
intersection graph [17], [23], [24]. Boudaakat et al. [17] have introduced the intersection modeling IMGFG,
where a generalized fuzzy graph concept with fuzzy vertices and fuzzy edges, through the modeling of an
isolated intersection in two stages, steady elements in the first stage which leads to fuzzy graph with crisp
vertices and a situational status which leads to a generalized fuzzy graph. In this work, the IMGFG is adopted
as the modeling technique because this approach has the potential to present precisely the congestion points
and conflicts level.
Reinforcement learning has been proved to be an effective method for developing adaptive traffic
signal controllers by adjusting signal timing in response to traffic fluctuations [25], [26]. The objectives of
the Q-learning which is a technique of model-free reinforcement learning algorithm are gain experience to
make better decisions. However, regarding all the advantages of the existing ATSC, the safety impact of
applying these methods is still unclear. Some studies showed that implementing ATSC algorithms leads to
lower traffic safety and extends traffic conflicts significantly [27] or a minor reduction on traffic collisions
[28]. The mobility optimization does not necessarily assure the safety optimization, so discounting the traffic
safety as a main objective in the existing ATSC is probably responsible of the inconsistency in the safety
impact [29]. Therefore, the proposed ATSC is a new approach combining computational intelligence and
reinforcement learning; it adjusts optimal signal timing in real-time and ensures the appropriate phasing with
maximum traffic safety.
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3. ERL-MA APPROACH
The proposed approach is an independent multi-agents system, where each agent supervises a traffic
light system (composed of mono-junction or multi-junction). The agent is designed to solve the traffic light
control problem in such a way that fits with all junction formats (with multi-lanes or mono-lanes edges,
directed and undirected lanes) and adapt traffic lights to the instantaneous flow changes. As depicted in
Figure 1, the agent, in the proposed approach, consists of two main layers. The IMGFG modeling layer is
responsible for modeling and transforming the junction to an adjacency matrix and provides the necessary
parameters. The second layer is a decision part divided into two activities: activity per cycle and activity per
phase. In the activity per cycle, the NGGA is applied to construct homogeneous clusters with the maximum
traffic safety and fluidity depending on the congestion level and the traffic demand density. In the activity per
phase, the Q-learning algorithm is employed to associate the adequate signal timing. The agent takes into
account the occurred changes at the intersection in every cycle and phase to manage the intersection.
Figure 1. The ERL-MA system
3.1. The modeling layer
The modeling layer uses the IMGFG technique [17] where a generalized fuzzy graph coloring
approach is used to model any signalized intersection. All possible conflicts between outgoing lanes and the
congestion level in lanes at the intersection are presented in this generalized fuzzy graph called the situational
graph. In this generalized graph, the vertices represent lanes, the vertices weight are the level of congestion,
the edges express the existing conflicts between those lanes, and the edges weight are the kinds of this
conflicts. The adjacency matrix represents the situational graph that will be sent to next layer. The decision
layer is divided into two activities: activity per cycle and activity per phase.
3.2. The decision layer
3.2.1. The activity per cycle
In this activity, the NGGA combines genetic and greedy algorithm. The genetic algorithm is
selected as the framework that explores the search space. Also, the rate at which the algorithm can explore a
space of possibilities is satisfying and adequate to combinatorial problems. The greedy algorithm is added as
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a function in the genetic algorithm to accelerate the search and improve chromosomes fitness [18]. In fact,
the NGGA was implemented on fuzzy graphs and has proved its efficiency to this kind of problem [30]. In
the proposed approach, the algorithm takes as an input the adjacency matrix generated from the IMGFG, and
provides at the output an optimal combination of lanes (k-clusters) that ensures a high flux with the
maximum safety. These clusters present phases that the agent will apply at the intersection during this cycle.
After that, the agent sorts these phases referring to the average queue length and the waiting time of each
phase to produce a sequence phasing set for the next step.
3.2.2. The activity per phase
In this activity, the agent uses the Q-learning algorithm to decide the green timing period for every
upcoming phase sequence. The Q-learning strategy adopted in this work uses a set of Q-tables, where every
Q-table concerns a cycle format. A Q-table is a matrix [31], where each row represents a specific state
(intersection situation) and each column represents a specific action (green time). When the agent decides to
apply a cycle with k-phases, the Q-tablek is called to give the adequate green timing (action) for the
associated phasing sequence (state). The agent compares all the Q-values of the requested state and the action
with the highest Q-value is chosen. The characteristics of this activity are:
Search space: In the signalized traffic control problem, the space of states and actions can be very
large depending on the parameters used to present them. The search space grows exponentially when the
states are numerous which impacts the Q-learning knowledge space (Q-learning matrices). Bigger the search
space is less parts of the Q-matrix are explored. In the proposed approach, the Q-tables number varies from
an intersection to another where the maximum and minimum numbers of phases are the first parameters to
define in order to know how many Q-tables exist. In order to reduce the number of states describing all
possible situations generated by the NGGA, states are represented with normalized forma where one state can
describe a number of similar situations in every Q-table. Also, actions selected at every Q-table are limited to
the maximum phasing time.
State definition: The general format of a state at the search space is [K, CP, AQW table], where K
represents the number of phases in the cycle, CP the current phase index and AQW table the average of the
queue weight at every phase. The states existing in every Q-tablek are represented with [CP, AQW table],
where the average queue weight values are limited at Ø, low (L), medium (M) and high (H) and the average
queue weight table size depends on the number of phases used at the sequence phasing, see Figure 2.
Figure 2. Illustration of a state in cycle with four phases
Action definition: Action presents green time phasing. For every state in the knowledge matrices
(Q-tables) different timing [min phase time, max phase time] are subject of test in the learning stage. The
max green time per phase differs from a Q-tablek to another and depends on the max cycle time and the
number of phases (k) in the cycle. The best-rated actions are actions with the max benefit for the current
phase and the less negative impact on the intersection.
Reward definition: Any action applied on a phase has a positive impact on the related lanes and a
negative impact on the other lanes. In order to get the action with the max benefit to the intersection, this
discrimination is necessary to balance action/effect. The reward is calculated in (1) based on the effect of the
selected action in all lanes of the intersection, where the degree of passed vehicles and the queue weight
change are used as a metric parameter for lanes belonging to the current phase; for the lanes belonging to
other phases, the average of the cumulative waiting time and the occurring queue weight are used.
𝑅𝑖 = 𝑃𝑉𝑖 ∗ (1 + (𝑄𝑊𝐵𝑖 − 𝑄𝑊𝐴𝑖)) −
∑ 𝑄𝑊𝐴𝑗∗(𝑊𝑇𝐴𝑗−𝑊𝑇𝐵𝑗)
𝑁𝑏𝑟𝑃ℎ𝑎𝑠𝑒𝑠
𝑗=1 , 𝑗≠𝑖
(𝑁𝑏𝑟𝑃ℎ𝑎𝑠𝑒𝑠−1)∗𝐴𝑖
(1)
Where, PVi is the degree of passed vehicles at the current phase. QWBi is average queue weight of the current
phase before the action. QWAi is average queue weight of the current phase after the action. QWAj is average
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queue weight of the j phase after the action. WTAj is cumulative waiting time of the j phase after the action.
WTBj is cumulative waiting time of the j phase before the action. Ai is current action.
Q-values and coefficient: The Q-table in (2) defines the knowledge space where every Q-tablek
presents a sub-space with a common format; k is the number of phases presenting the state.
𝑄𝑡𝑎𝑏𝑙𝑒(𝑘) =
{
𝑄𝑡𝑎𝑏𝑙𝑒𝑖, 𝑘 = 𝑖
𝑄𝑡𝑎𝑏𝑙𝑒𝑖+1, 𝑘 = 𝑖 + 1
.
.
.
𝑄𝑡𝑎𝑏𝑙𝑒𝑗, 𝑘 = 𝑗
(2)
The Q-learning uses the experience of each state transition to update one element of a Q-tablek. A Q-tablek is
a matrix in which each row represents a specific state and each column represents a specific action. Each cell
in this matrix represents a Q-value for a specific state-action pair Qk (s, a) [32]. The Q-value, in general, is
used to compare various actions at a specific state. The Q-learning algorithm improves its policy by updating
the Q-tablek in (3) according to Bellman’s equation [33].
𝑄𝑘
𝑡+1(𝑠𝑡
, 𝑎𝑡) = 𝑄𝑘
𝑡 (𝑠𝑡
, 𝑎𝑡) +∝𝑡+1 [𝑟𝑡+1
+ 𝛾 𝑚𝑎𝑥 𝑄𝑘
𝑡 (𝑠𝑡+1
, 𝑎𝑡+1) − 𝑄𝑘
𝑡 (𝑠𝑡
, 𝑎𝑡)] (3)
Where st
, at
is the current state and the selected action at the current state. Qt+1
, Qt
is the updated and the old
Q-value. rt+1
is the reward of applying action at state st
. st+1
, at+1
is the new state and the best action at the
new state. αt+1
is the learning rate. Γ is the discount rate.
Training stage: Before testing the ERL-MA system, a learning phase is mandatory. Every agent
undergoes a training stage to acquire the knowledge of behaving with the intersection, and be able to assign
the adequate action to a current situation taking into consideration the structure of the intersection and impact
of the action on it. The policy matrices are set to zeros; then, the agent passes to learning stage where it is
exposed to a random generated scenario combining low, medium and high flows in order to access at the
maximum state-actions component of the Q-tables. Also, the action selection is subject to a noise coefficient
to stimulate the exploration. In result, the agent becomes ready for the evaluation with optimal policy
matrices. The agent does not stop upgrading the optimal policy matrix in this stage, but is able to become
greater with the future experience.
4. EXPERIMENTAL RESULTS
4.1. Simulation environment
In this paper, simulation of urban mobility (SUMO) [34] is used as the simulation environment. It is
a free and open-source microscopic traffic simulation. It has been available since 2001 and allows modeling
of intermodal traffic systems including road vehicles, pedestrians and public transport; it is a tool widely used
for traffic research.
4.2. Testbed network
Traffic light signal plans are rarely open to the public and are often not available in digital format.
To replicate a part of a real road network, gathering, converting, and adapting all the data is time-consuming.
Also, the correction and the validation of the responsible municipality of the studied area are hardly possible
and mandatory to allow performing real-world evaluations and fair comparisons. Real-world scenarios from
Bologna built in the project of iTERTRIS (co-funded by the European Commission and contributed by the
municipality of bologna as a project partner) where all the previous conditions are respected, prepared and
available to the public within the SUMO package by Bieker et al. [19]. The proposed approach is applied on
the network Pasubio area in Bologna in Italy as shown in Figure 3.
The Pasubio scenario extends the scenarios by the area around the hospital and includes also
common routes to the football stadium as shown in Figure 4. Due to the situation and the traffic problems in
Bologna, the municipality of Bologna delivered a large set of data and simulation scenarios. The given data
included representations of the areas around the Pasubio roads, as input files for the commercial microscopic
traffic simulation. The scenarios modeled the peak hour in Bologna (8:00–9:00 am) [19].
The congestion level in Bologna in 2019 is 25% and 205 ranked in the world, the congestion level
by road type in highways is 17% and in non-highways is 31%. The real-world scenarios from Bologna were
prepared and are described under project iTETRIS which is an integrated wireless and traffic platform for
real-time road traffic management solutions to help estimate road traffic engineering. The scenario of
Bologna traffic was built to illustrate the traffic congestion in both areas Pasubio and Andrea Costa.
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Figure 3. Location of Bologna in Italy
Figure 4. Pasubio traffic network in SUMO
The Pasubio scenario of Bologna city in Italy [19] presented in Figure 4 was chosen to test the
proposed approach. The selected area represents 2.45 km2
of a real city with a total of eight traffic lights,
sixty-five nodes and one hundred eleven edges. the scenario displays the real traffic at the morning peak hour
in 8:00-9:00 am. The city of Bologna uses the UTOPIA system for traffic light control. UTOPIA [35]
optimizes traffic light schedules and sorts the traffic light phases to satisfy traffic demand.
4.3. Simulation
The tested area is composed of 8 traffic lights control system. Some of them use multi junctions
(intersections) with one traffic lights control system. Every traffic light control system (TLC) is supervised
by an agent as shown in Table 1. Every agent undergoes the training stage separately until it acquires the
necessary skills. As shown in Figure 5, the Junctions 4 and 14 are controlled by the TLC 230 and the agent 1
supervise it. The accumulated knowledge and the scenarios exposed during the learning stage for agent 1are
presented in Figures 6 and 7. All the others agents of the network are exposed to the same scenarios.
Table 1. Junctions and controlling agents
Agent TLC Id Junctions Ids
1 230 14, 4
2 231 9, 10, 12
3 232 29, 27
4 233 15
5 220 36
6 219 1, 32
7 282 18
8 218 0, m0
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Figure 5. Illustration of junctions with the associated TLC
Agent one is exposed to a random generated scenario combining low, medium and high flows in
order to access at the maximum state-actions component of the Q-tables, Figure 6 show the quantities of the
new knowledge acquired at the end of every scenario; in the begging of the learning phases the quantity of
the new knowledge is massive because the agent is uninformed, with time the new knowledge accumulated
per scenario get lower until it become insignificant, in this point the optimal policy is obtained and the agent
is ready for evaluation. Figure 7 show the associated running time to all the random scenarios applied to the
agent 1 at the learning stage.
Figure 6. The acquired knowledge variation in the learning phase
Figure 7. The running time of random generated scenarios in the learning phase
4.4. Approach evaluation
A real-world scenario will evaluate the proposed approach. The network area is 2.45 m2
with eight
traffic lights, 8,776 loaded vehicles, and different types of signalized intersections; the proposed system is
compared to the UTOPIA system implemented at the Pasubio network. After, applying the learning phase to
all agents controlling the TLC in the Pasubio area, the evaluation of the same scenario provided as data test
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were run in 50 simulations to evaluate the performance of the proposed approach comparing the UTOPIO
system. Figure 8 shows that the proposed approach exceeds the UTOPIA system in 100% with a performance
of [6.60%, 12.44%] less time. The UTOPIA system finishes the simulation in 4,605 sec and the proposed
approach running time varies between 4,032 and 4,301 sec; this non-stability is due to the characteristic of
the NGGA which is a stochastic algorithm. The effect of the randomness characteristic can be seeing at the
first look as a weakness, but with a deep look it should be seeing as a powerful tool that distinguishes this
approach, having this characteristic mean that agent is always able to benefice from new experiences and
develop new skills to manage the traffic light control system.
The waiting time graphs results show that the average waiting time was controlled by the ERL-MA
perfectly compared to UTOPIA. Agent one at junction four and agent eight at junction zero found some
difficulty controlling the waiting time. However, generally, they succeeded in handling it compared to
UTOPIA, as shown in Figure 9. Likewise, the simulation results presented in Figure 10 show that ERL-MA
agents have improved the queue length of all junctions comparing to the UTOPIA system.
Figure 8. Illustration of simulation duration
Figure 9. Illustration of the average waiting time at the Pasubio junctions
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Figure 10. Illustration of the max queue length at the Pasubio junctions
As a result of the above graphs, the CO2 and CO have been significantly reduced. Due to the shorted
waiting time and accumulated queue length of vehicles. Also, the lower simulation time have contributed at
CO2 and CO reducing. The ERL-MA approach reduced CO2 and CO emissions produced by vehicles, as
depicted in Figure 11.
The experiments results determined that ERL-MA system can generate a flexible traffic signal
timing to traffic variation for the Pasubio scenario and different intersection forma. The proposed approach
was tested using a real-world scenario. The approach was compared with the implemented system at the
Pasubio network. The ERL-MA agent use the information provided by the IMGFG to understand the
intersection constraint, the NGGA to compose the optimal sequence phasing, and the accumulated
knowledge of the Q-learning to generate a flexible signal traffic light timing. However, the model can
certainly be extended to consider different aspects such as the communication between agents or between
lights and vehicles. The ERL-MA has the ability to obtain excellent solution for traffic light control problem.
Figure 11. Illustration of CO and CO2 emission of vehicles
5. CONCLUSION
The proposed approach uses two powerful tools of human beings which are learning and assumption
to build an intelligent agent capable of managing the traffic light control system. Agents are designed to fit
with junction format and have the potential to adjust signal timing conforming to the instantaneous changes.
The proposed approach does not request expensive changes or massive conditions demanding. Furthermore,
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it does not impose changes at the infrastructure of the intersection or requires direct communication with
vehicles. This approach was designed to fit and self-adapt with intersection infrastructure and TLC.
Computational intelligence, machine learning approach, and fuzzy graph modeling are used to build this
approach. The proposed system ERL-MA was evaluated with a real-world scenario and compared to the
existing system. The obtained results showed that the proposed approach succeeded to achieve competitive
results using different metrics.
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BIOGRAPHIES OF AUTHORS
Mohamed Amine Basmassi is currently Ph.D. student in the informatics,
systems and optimization laboratory in Ibn-Tofail University, Kenitra, Morocco. In 2015, he
received the M.S degree in Computer Engineering from the Abdelmalek Essaadi University,
Tetouan, Morocco. In 2012, he had the B.S. degree in fundamental mathematics and computer
science from Cadi Ayyad University, Marrakech-Safi, Morocco. His research interests fall in
machine learning, heuristic techniques, computational intelligence and multi-agent system to
solve problems related to transport and traffic management, combinatorial optimization and
decision-making problems. He can be contacted at email: basmassi.med.amine@gmail.com.
Sidina Boudaakat received the B.S. degree from Mohamed V University of
Rabat, Morocco in 2013, and M.S degree in Abdelmalek Essaadi University, Tetouan,
Morocco, in 2015. Since 2016 he has been working toward the Ph.D. degrees on
Computational Intelligence in Urban Traffic to solved problems related to the management
of road traffic and road safety at Laboratory SSDIA in Normal School of Technical
Education (NSTE), Mohammadia, Morocco. He can be contacted at email:
boudaakat.sidina@gmail.com.
Jihane Alami Chentoufi is a Professor and Ph.D. supervisor in the Research on
Computer Science Laboratory in Ibn Tofail University, Faculty of Sciences-Kenitra. She
received her M.S. and Ph.D. degrees in Computer Sciences from Mohammed V University,
Faculty of Sciences-Rabat, Morocco in 2003 and 2007, respectively. Her main research
interests include metaheuristics, combinatorial optimization and decision-making problems.
She can be contacted at: j.alami@uit.ac.ma.
Lamia Benameur is a Professor and Ph.D. supervisor in the Research on
Information Technology and systems modelization Laboratory in Abdelmalik Essaadi
University, Faculty of Sciences-Tetouan. She received her M.S. and Ph.D. degrees in
Computer Sciences from Mohammed V University, Faculty of Sciences-Rabat, Morocco in
2003 and 2010, respectively. Her main research interests include metaheuristics, combinatorial
optimization and decision-making problems. She can be contacted at: l.benameur@uae.ac.ma.
12. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 12, No. 5, October 2022: 5519-5530
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Ahmed Rebbani he received the B.S. degree in Electronics in 1988, the M.S.
degree in Applied Electronics in 1992 from the ENSET Institute, Mohammedia, Morocco. He
received the DEA diploma in information processing in 1997 from the faculty of sciences Ben
Msik, Casablanca, Morocco. has his Ph.D. degree in 2014 In electrical energy storage from the
faculty of sciences and techniques Mohammedia, Morocco. He is now a teacher and
researcher at the University Hassan II, ENSET Institute. His research is focused on Internet of
things and renewable energy. He can be contacted at email: a.rebbani@gmail.com.
Omar Bouattane he received his PhD from the University Hassan II of
Casablanca, MOROCCO in 2001 in Parallel Computing and Image processing. He currently
serves as a full Professor in the Department of Electrical Engineering at “Ecole Normale
Superieure de l’enseignement technique” ENSET of Mohammedia. He has more than 150
scientific publications in various domains of Computational Intelligence, high performance
computing, image processing and renewable energy. He has registered 6 national and PCT
international Patents regarding to the networking technology and signal synthesis. He was
awarded as the owner of the best PCT patent in Morocco on 2011. Since 2012, He was the
head of the laboratory of Signals Distributed Systems and Artificial Intelligence. He involved
his laboratory in several partnership activities and developed many funded projects in
Morocco and in his university. Overall, Prof. Bouattane work has received more than 300
citations. Prof. Bouattane has been the principal investigator and leader in 4 academic projects,
funded either publicly or privately, in the USA, Canada and France. He was the supervisor
from Morocco of a partnership program entitled “Linkage for entrepreneurship achievement
program” funded by the USAID and HED of USA from December 2012 to December 2014.
He supervised beside his US partners a scholarship program named “study abroad, Moroccain
culture” December 2016 to December 2018. All his academic and research activities are in the
ResearchGate portal at: https://www.researchgate.net/profile/Omar_Bouattane/contributions.
He can be contacted at email: o.bouattane@gmail.com.