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.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...cscpconf
The intensive development of traffic engineering and technologies that are integrated into vehicles, roads and their surroundings, bring opportunities of real time transport mobility modeling. Based on such model it is then possible to establish a predictive layer that is capable of predicting short and long term traffic flow behavior. It is possible to create the real time model of traffic mobility based on generated data. However, data may have different geographical, temporal or other constraints, or failures. It is therefore appropriate to develop tools that artificially create missing data, which can then be assimilated with real data. This paper presents a mechanism describing strategies of generating artificial data using microsimulations. It describes traffic microsimulation based on our solution of multiagent framework over which a system for generating traffic data is built. The system generates data of a structure corresponding to the data acquired in the real world.
Approximation of regression-based fault minimization for network trafficTELKOMNIKA JOURNAL
This research associates three distinct approaches for computer network traffic prediction. They are the traditional stochastic gradient descent (SGD) using a few random samplings instead of the complete dataset for each iterative calculation, the gradient descent algorithm (GDA) which is a well-known optimization approach in deep learning, and the proposed method. The network traffic is computed from the traffic load (data and multimedia) of the computer network nodes via the Internet. It is apparent that the SGD is a modest iteration but can conclude suboptimal solutions. The GDA is a complicated one, can function more accurate than the SGD but difficult to manipulate parameters, such as the learning rate, the dataset granularity, and the loss function. Network traffic estimation helps improve performance and lower costs for various applications, such as an adaptive rate control, load balancing, the quality of service (QoS), fair bandwidth allocation, and anomaly detection. The proposed method confirms optimal values out of parameters using simulation to compute the minimum figure of specified loss function in each iteration.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
interface for communication between agents.
class for communication management.
Agent Factory: class for agent creation.
Agent Directory: class for agent registration.
Agent Behavior: abstract class for agent behavior definition.
Concrete Agent: concrete agent implementation.
The core of the architecture is based on three main classes:
- Manager - represents the highest level of hierarchy, manages lower level agents.
- Agent - represents basic autonomous entity, encapsulates behavior and communication.
- Structure - represents geographical area, contains reference to lower level agents.
Agents are organized hierarchically according to geographical areas they represent. Manager is
the root of hierarchy, structures represent areas and agents are located
Evolutionary reinforcement learning multi-agents system for intelligent traf...IJECEIAES
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.
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 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.
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.
Our journal has been unwavering commitment to showcasing cutting-edge research. The journal provides a platform for researchers to disseminate their work on next-generation technologies. In an era where innovation is the driving force behind progress, JST plays a crucial role in shaping the discourse on emerging technologies, thus contributing to their rapid development and implementation.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...cscpconf
The intensive development of traffic engineering and technologies that are integrated into vehicles, roads and their surroundings, bring opportunities of real time transport mobility modeling. Based on such model it is then possible to establish a predictive layer that is capable of predicting short and long term traffic flow behavior. It is possible to create the real time model of traffic mobility based on generated data. However, data may have different geographical, temporal or other constraints, or failures. It is therefore appropriate to develop tools that artificially create missing data, which can then be assimilated with real data. This paper presents a mechanism describing strategies of generating artificial data using microsimulations. It describes traffic microsimulation based on our solution of multiagent framework over which a system for generating traffic data is built. The system generates data of a structure corresponding to the data acquired in the real world.
Approximation of regression-based fault minimization for network trafficTELKOMNIKA JOURNAL
This research associates three distinct approaches for computer network traffic prediction. They are the traditional stochastic gradient descent (SGD) using a few random samplings instead of the complete dataset for each iterative calculation, the gradient descent algorithm (GDA) which is a well-known optimization approach in deep learning, and the proposed method. The network traffic is computed from the traffic load (data and multimedia) of the computer network nodes via the Internet. It is apparent that the SGD is a modest iteration but can conclude suboptimal solutions. The GDA is a complicated one, can function more accurate than the SGD but difficult to manipulate parameters, such as the learning rate, the dataset granularity, and the loss function. Network traffic estimation helps improve performance and lower costs for various applications, such as an adaptive rate control, load balancing, the quality of service (QoS), fair bandwidth allocation, and anomaly detection. The proposed method confirms optimal values out of parameters using simulation to compute the minimum figure of specified loss function in each iteration.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
interface for communication between agents.
class for communication management.
Agent Factory: class for agent creation.
Agent Directory: class for agent registration.
Agent Behavior: abstract class for agent behavior definition.
Concrete Agent: concrete agent implementation.
The core of the architecture is based on three main classes:
- Manager - represents the highest level of hierarchy, manages lower level agents.
- Agent - represents basic autonomous entity, encapsulates behavior and communication.
- Structure - represents geographical area, contains reference to lower level agents.
Agents are organized hierarchically according to geographical areas they represent. Manager is
the root of hierarchy, structures represent areas and agents are located
Evolutionary reinforcement learning multi-agents system for intelligent traf...IJECEIAES
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.
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 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.
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.
Our journal has been unwavering commitment to showcasing cutting-edge research. The journal provides a platform for researchers to disseminate their work on next-generation technologies. In an era where innovation is the driving force behind progress, JST plays a crucial role in shaping the discourse on emerging technologies, thus contributing to their rapid development and implementation.
Taxi Demand Prediction using Machine Learning.IRJET Journal
This document presents research on using machine learning models to predict taxi demand. The researchers used data on past taxi rides in New York City to train and test linear regression, random forest, and XGBoost regression models to predict future taxi demand. They found that the XGBoost model achieved the highest prediction accuracy of 88% based on metrics like root mean squared error. The proposed system aims to help taxi companies optimize resource allocation based on demand forecasts from machine learning models.
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%.
IRJET- Smart Railway System using Trip Chaining MethodIRJET Journal
This document proposes a smart railway system using trip chaining and big data analysis of passenger information from smart cards. The system would collect data like passenger name, age, travel time, source and destination stations from smart cards. It would then use k-means clustering to group passengers by age and travel patterns. A naïve bayes classifier would predict passenger counts at each station. This analysis of passenger data could help the railway department improve infrastructure and services based on demand.
DEEP LEARNING NEURAL NETWORK APPROACHES TO LAND USE-DEMOGRAPHIC- TEMPORAL BA...civejjour
Land use and transportation planning are inter-dependent, as well as being important factors in forecasting urban development. In recent years, predicting traffic based on land use, along with several other variables, has become a worthwhile area of study. In this paper, it is proposed that Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) methods could be used to predict traffic. These methods used three key variables: land use, demographic and temporal data. The proposed methods were evaluated with other methods, using datasets collected from the City of Calgary, Canada. The proposed DNN-Regression focused on demographic and land use variables for traffic prediction. The study also predicted traffic temporally in the same geographical area by using DNN-RNN. The DNN-RNN used long short-term memory to predict traffic. Comparative experiments revealed that the proposed DNN-Regression and DNN-RNN models outperformed other methods.
Deep Learning Neural Network Approaches to Land Use-demographic- Temporal bas...civejjour
This document describes research that uses deep learning neural network approaches to predict traffic based on land use, demographic, and temporal data. Specifically, it proposes using Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) models. DNN-Regression focuses on demographic and land use variables to predict traffic, while DNN-RNN predicts traffic temporally in the same geographic area using long short-term memory. The models are evaluated using datasets from Calgary, Canada, and are found to outperform other existing approaches to traffic prediction.
Classification Approach for Big Data Driven Traffic Flow Prediction using Ap...IRJET Journal
This document discusses a proposed system for predicting traffic flow using big data and classification approaches. The system uses K-Nearest Neighbors (KNN) classification to identify traffic patterns and routes. It then uses a Convolutional Neural Network (CNN) to predict traffic flow levels on particular routes. The KNN identifies travel times between locations while the CNN predicts flow levels. The proposed system is evaluated using metrics like root mean squared error and mean relative error, and is found to improve accuracy and reduce prediction time compared to existing methods. The system aims to provide route recommendations to users based on minimum predicted traffic flow.
Real time vehicle counting in complex scene for traffic flow estimation using...Journal Papers
This document presents a multi-level convolutional neural network (mCNN) framework for real-time vehicle counting and classification in complex traffic scenes. The mCNN framework includes five main modules: pre-processing, object detection, tracking, object classification, and quantification. In the pre-processing stage, images are cropped to remove irrelevant details. A CNN is then used for initial object detection and pre-classification. Detected objects are tracked across frames and a second CNN is used for refined classification before vehicle counts are generated. The mCNN framework is tested on 585 minutes of highway video and achieves an average vehicle counting accuracy of 97.53% and weighted average counting with classification accuracy of 91.5%, demonstrating its effectiveness for real-time
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.
DYNAMIC RESOURCE ALLOCATION IN ROAD TRANSPORT SECTOR USING MOBILE CLOUD COMPU...IAEME Publication
Literature review revealed application of various techniques for efficient use of existing resources in road transport sector vehicles, operators and related facilities. This issue assumes bigger dimensions in situations where there are multiple routes and the demand in the routes is highly fluctuating over the day. The application of the existing techniques as reported in literature addresses above issues to a considerable extent. However the main draw back in existing techniques is lack of
proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation.
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
TRAFFIC CONGESTION PREDICTION USING DEEP REINFORCEMENT LEARNING IN VEHICULAR ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
Urban Bus Route Planning Using Reverse Labeling Dijkstra Algorithm for Tempor...IRJET Journal
The document discusses using the Reverse Labeling Dijkstra Algorithm (RLDA) to optimize urban bus route planning by considering real-time traffic conditions. RLDA is an adaptation of Dijkstra's algorithm that finds the shortest path from the destination node backwards towards the source node. This allows it to consider arc attributes representing traffic congestion levels. The document presents the mathematical model and steps of the RLDA algorithm. It then discusses simulating the RLDA approach on a real-time road network to determine efficient bus routes.
Dynamic resource allocation in road transport sector using mobile cloud compu...IAEME Publication
This document discusses dynamic resource allocation in the road transport sector using mobile cloud computing techniques. It provides an overview of existing literature on dynamic resource allocation methods and their limitations in addressing high vehicle and route demand fluctuations. The document then proposes using mobile cloud computing to allow for real-time vehicle-route allocation with minimal processing time by installing mobile devices at stations to communicate demand data to nearby clouds and a central traffic manager for computation and order distribution. Simulation case studies are developed and results are compared to real data to validate the mobile cloud computing approach for improved dynamic resource allocation under heavy demand fluctuations.
A Simulation-Based Dynamic Traffic Assignment Model With Combined ModesAllison Koehn
This document presents a simulation-based dynamic traffic assignment model for an urban transportation network with multiple transportation modes. The model uses a mesoscopic simulation approach with separate modules for vehicle movement simulation and time-dependent demand simulation. It considers four transportation modes (private car, bus, subway, bicycle) and allows travelers to choose between modes and routes based on travel time and costs. The model is tested using a case study area in Beijing to evaluate its performance under different scenarios like changes in demand levels, bus frequencies, parking fees, and information provision.
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.
This document presents a study on predicting taxi demand in real-time using historical taxi trip data. The study uses a long short-term memory (LSTM) recurrent neural network model to predict future taxi demand across different neighborhoods of a city. The model examines relationships between past taxi demand patterns and subsequent demand to effectively forecast future demand. It divides the city into distinct areas and predicts demand for each area. The model was tested on a dataset of New York City taxi requests and was found to outperform other prediction methods. The study suggests demand prediction could be improved by incorporating additional data like weather. Accurately predicting taxi demand can benefit fleet management and reduce passenger wait times.
The realistic mobility evaluation of vehicular ad hoc network for indian auto...ijasuc
In recent years, continuous progress in wireless communication has opened a new research field in
computer networks. Now a day’s wireless ad-hoc networking is an emerging research technology that
needs attention of the industry people and the academicians. A vehicular ad-hoc network uses vehicles as
mobile nodes to create mobility in a network.
It’s a challenge to generate realistic mobility for Indian networks as no TIGER or Shapefile map is
available for Indian Automotive Networks.
This paper simulates the realistic mobility of the Vehicular Ad-hoc Networks (VANETs). The key feature of
this work is the realistic mobility generation for the Indian Automotive Intelligent Transport System (ITS)
and also to analyze the throughput, packet delivery fraction (PDF) and packet loss for realistic scenario.
The experimental analysis helps in providing effective communication for safety to the driver and
passengers.
IRJET - A Review on Pedestrian Behavior Prediction for Intelligent Transport ...IRJET Journal
This document reviews various techniques for predicting pedestrian behavior for intelligent transportation systems. It discusses algorithms that aim to predict pedestrian motion around crossroads to avoid potential collisions with vehicles. The document summarizes several papers that propose different methods for pedestrian behavior prediction, including the use of LSTM neural networks, goal-directed planning with deep neural networks, analyzing pedestrian-vehicle interaction behavior, using CNNs for pedestrian detection and direction prediction, predicting pedestrian intention at night using infrared cameras and fuzzy automata, and memory-based and physical modeling approaches.
Traffic Control System by Incorporating Message Forwarding ApproachCSCJournals
This document summarizes a research paper that proposes a traffic control system using message forwarding in vehicular ad hoc networks (VANETs). The system incorporates scanners that detect abnormal traffic conditions and activate transmitters to broadcast congestion notifications using a reliable directional greedy routing algorithm. The system is simulated using the VEINS simulator. Key aspects of the system include establishing vehicle-to-vehicle and vehicle-to-roadside communication, calculating a roadway congestion index to detect abnormalities, and enabling transmitters to forward messages about detected events to improve traffic flow and safety.
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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DEEP LEARNING NEURAL NETWORK APPROACHES TO LAND USE-DEMOGRAPHIC- TEMPORAL BA...civejjour
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This document describes research that uses deep learning neural network approaches to predict traffic based on land use, demographic, and temporal data. Specifically, it proposes using Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) models. DNN-Regression focuses on demographic and land use variables to predict traffic, while DNN-RNN predicts traffic temporally in the same geographic area using long short-term memory. The models are evaluated using datasets from Calgary, Canada, and are found to outperform other existing approaches to traffic prediction.
Classification Approach for Big Data Driven Traffic Flow Prediction using Ap...IRJET Journal
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proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation.
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
TRAFFIC CONGESTION PREDICTION USING DEEP REINFORCEMENT LEARNING IN VEHICULAR ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
Urban Bus Route Planning Using Reverse Labeling Dijkstra Algorithm for Tempor...IRJET Journal
The document discusses using the Reverse Labeling Dijkstra Algorithm (RLDA) to optimize urban bus route planning by considering real-time traffic conditions. RLDA is an adaptation of Dijkstra's algorithm that finds the shortest path from the destination node backwards towards the source node. This allows it to consider arc attributes representing traffic congestion levels. The document presents the mathematical model and steps of the RLDA algorithm. It then discusses simulating the RLDA approach on a real-time road network to determine efficient bus routes.
Dynamic resource allocation in road transport sector using mobile cloud compu...IAEME Publication
This document discusses dynamic resource allocation in the road transport sector using mobile cloud computing techniques. It provides an overview of existing literature on dynamic resource allocation methods and their limitations in addressing high vehicle and route demand fluctuations. The document then proposes using mobile cloud computing to allow for real-time vehicle-route allocation with minimal processing time by installing mobile devices at stations to communicate demand data to nearby clouds and a central traffic manager for computation and order distribution. Simulation case studies are developed and results are compared to real data to validate the mobile cloud computing approach for improved dynamic resource allocation under heavy demand fluctuations.
A Simulation-Based Dynamic Traffic Assignment Model With Combined ModesAllison Koehn
This document presents a simulation-based dynamic traffic assignment model for an urban transportation network with multiple transportation modes. The model uses a mesoscopic simulation approach with separate modules for vehicle movement simulation and time-dependent demand simulation. It considers four transportation modes (private car, bus, subway, bicycle) and allows travelers to choose between modes and routes based on travel time and costs. The model is tested using a case study area in Beijing to evaluate its performance under different scenarios like changes in demand levels, bus frequencies, parking fees, and information provision.
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.
This document presents a study on predicting taxi demand in real-time using historical taxi trip data. The study uses a long short-term memory (LSTM) recurrent neural network model to predict future taxi demand across different neighborhoods of a city. The model examines relationships between past taxi demand patterns and subsequent demand to effectively forecast future demand. It divides the city into distinct areas and predicts demand for each area. The model was tested on a dataset of New York City taxi requests and was found to outperform other prediction methods. The study suggests demand prediction could be improved by incorporating additional data like weather. Accurately predicting taxi demand can benefit fleet management and reduce passenger wait times.
The realistic mobility evaluation of vehicular ad hoc network for indian auto...ijasuc
In recent years, continuous progress in wireless communication has opened a new research field in
computer networks. Now a day’s wireless ad-hoc networking is an emerging research technology that
needs attention of the industry people and the academicians. A vehicular ad-hoc network uses vehicles as
mobile nodes to create mobility in a network.
It’s a challenge to generate realistic mobility for Indian networks as no TIGER or Shapefile map is
available for Indian Automotive Networks.
This paper simulates the realistic mobility of the Vehicular Ad-hoc Networks (VANETs). The key feature of
this work is the realistic mobility generation for the Indian Automotive Intelligent Transport System (ITS)
and also to analyze the throughput, packet delivery fraction (PDF) and packet loss for realistic scenario.
The experimental analysis helps in providing effective communication for safety to the driver and
passengers.
IRJET - A Review on Pedestrian Behavior Prediction for Intelligent Transport ...IRJET Journal
This document reviews various techniques for predicting pedestrian behavior for intelligent transportation systems. It discusses algorithms that aim to predict pedestrian motion around crossroads to avoid potential collisions with vehicles. The document summarizes several papers that propose different methods for pedestrian behavior prediction, including the use of LSTM neural networks, goal-directed planning with deep neural networks, analyzing pedestrian-vehicle interaction behavior, using CNNs for pedestrian detection and direction prediction, predicting pedestrian intention at night using infrared cameras and fuzzy automata, and memory-based and physical modeling approaches.
Traffic Control System by Incorporating Message Forwarding ApproachCSCJournals
This document summarizes a research paper that proposes a traffic control system using message forwarding in vehicular ad hoc networks (VANETs). The system incorporates scanners that detect abnormal traffic conditions and activate transmitters to broadcast congestion notifications using a reliable directional greedy routing algorithm. The system is simulated using the VEINS simulator. Key aspects of the system include establishing vehicle-to-vehicle and vehicle-to-roadside communication, calculating a roadway congestion index to detect abnormalities, and enabling transmitters to forward messages about detected events to improve traffic flow and safety.
Similar to Adaptive traffic lights based on traffic flow prediction using machine learning models (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%.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Generative AI leverages algorithms to create various forms of content
Adaptive traffic lights based on traffic flow prediction using machine learning models
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 5, October 2023, pp. 5813~5823
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5813-5823 5813
Journal homepage: http://ijece.iaescore.com
Adaptive traffic lights based on traffic flow prediction using
machine learning models
Idriss Moumen, Jaafar Abouchabaka, Najat Rafalia
Department of Computer Science, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco
Article Info ABSTRACT
Article history:
Received Jan 9, 2023
Revised Mar 20, 2023
Accepted Apr 7, 2023
Traffic congestion prediction is one of the essential components of intelligent
transport systems (ITS). This is due to the rapid growth of population and,
consequently, the high number of vehicles in cities. Nowadays, the problem
of traffic congestion attracts more and more attention from researchers in the
field of ITS. Traffic congestion can be predicted in advance by analyzing
traffic flow data. In this article, we used machine learning algorithms such as
linear regression, random forest regressor, decision tree regressor, gradient
boosting regressor, and K-neighbor regressor to predict traffic flow and
reduce traffic congestion at intersections. We used the public roads dataset
from the UK national road traffic to test our models. All machine learning
algorithms obtained good performance metrics, indicating that they are valid
for implementation in smart traffic light systems. Next, we implemented an
adaptive traffic light system based on a random forest regressor model, which
adjusts the timing of green and red lights depending on the road width, traffic
density, types of vehicles, and expected traffic. Simulations of the proposed
system show a 30.8% reduction in traffic congestion, thus justifying its
effectiveness and the interest of deploying it to regulate the signaling problem
in intersections.
Keywords:
Adaptive traffic light system
Intelligent transport systems
Machine learning
Traffic prediction
Traffic simulation
This is an open access article under the CC BY-SA license.
Corresponding Author:
Idriss Moumen
Department of Computer Science, Faculty of Sciences, Ibn Tofail University
B.P 133, University campus, Kenitra, Morocco
Email: idriss.moumen@uit.ac.ma
1. INTRODUCTION
The intelligent transportation system is an essential part of the smart city [1]–[3]. A thorough
understanding of how these systems work and the development of effective traffic management and control
strategies are required to optimize their management. Traffic prediction is crucial in traffic systems. An
accurate and efficient traffic flow prediction system is needed to achieve intelligent transport systems (ITS)
[4], [5]. The traffic flow prediction system is used to provide accurate road status information [6]. However,
traffic congestion is a growing global problem as the population increases. Therefore, each traffic intersection
is likely to gather a lot of vehicles, and the peak period will be more congested than usual. Consequently, to
solve this problem, numerous research studies have been carried out to explore and manage traffic flow to
reduce traffic congestion. Traffic flow data were collected from the Tokyo Expressway. These data were used
as input to the predictive model [7]. In [8], the model predicted the value of traffic speed on each road section at
the next time stamp using data collected from Beijing. By including a multi-task layer at the conclusion of a deep
belief network (DBN) [9]. Concurrently predicted traffic speed and flow. While in [10], to forecast traffic in the
upcoming six timestamps simultaneously, the authors used both the iteration approach and the auto-correlation
coefficient method. Regression models are easy to implement and suited for traffic prediction tasks on a simple
traffic network. According to [11], the mathematical model between inputs and outputs and associated
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parameters are predetermined, and the relationship between each parameter and input data is relatively certain.
The experimental results also show that the linear regression model is more efficient and can provide satisfactory
prediction results.
There are many types of classification methods for machine learning models based on different
perspectives [12]–[17]. In addition, the studies [18], [19] demonstrate the robust performance of linear
regression models. The authors provide a forecasting method that combines linear regression with time
correlation coefficients. Linear regression deals with the spatio-temporal relationship of road networks and can
also use this information to make predictions. Hobeika and Kim [20] provided prediction models, each model
combined with different traffic variables such as upstream, current traffic and historical average.
Kwon et al. [21] combined linear regression with stepwise-variable-selection method and tree-based method.
To avoid the intersection problem, the authors used cross-validation (CV). According to [22], the k-nearest
neighbor (KNN) model is a data-based non-parametric regression method. KNN model finds the k-nearest
neighbors that match the current variable value and use those K data to predict the next period's value.
Furthermore, in [23], four main challenges were pointed out: i) how to define an appropriate state vector,
ii) how to define a suitable distance metric, iii) how to generate forecast, and iv) how to manage the potential
neighbor database. In [24], KNN model was used for traffic flow prediction in different prediction intervals
(15, 30, 45, 60 min). To improve the K nearest neighbors selection process, Zheng and Su [25] chose correlation
coefficient distance as the distance metric to select the more related neighbors. The authors then introduced a
linearly sewing principal component method (LSPC), which steers the final prediction into a minimization
problem. A rather different approach was taken in [26], in which MapReduce-based KNN was introduced for
traffic flow prediction. The authors also employed distance weighted voting to mitigate the impact of the
number of K.
Various techniques have been evaluated in the literature. In [27], [28], chose the support vector
machines (SVM) model as the kernel of the online traffic prediction system. SVM need less computational
requirements. However, SVM has difficulties dealing with a big traffic network problem. Furthermore, when
using grid search, the cost of finding appropriate hyper-parameters is relatively high when compared to KNN
or regression models [28]. However, methods such as continuous ant colony optimization (CACO) [29] and
genetic particle swarm optimization (GPSO) [30] are used to quickly determine the hyper-parameters values.
According to [23], [31], when compared to the traditional linear regression model, the non-parametric method
can better capture the non-linear feature of the traffic pattern, which greatly increases the algorithm's precision.
However, the non-parametric method also comes with drawbacks [32]. This model imposes high demands on
the quantity and quality of historical data, and training costs for nonparametric models can be high compared
to other types of methods. Recurrent neural networks (RNN) were introduced to solve this problem. RNNs can
better describe the impact of a series of traffic records [33]–[35]. RNNs also have drawbacks. If the input series
is too long during the optimization process, it can lead to gradient explosion or gradient vanishing problems.
To avoid this problem, Hochreiter and Schmidhuber [36] proposed long-short term memory (LSTM) model to
solve the long-term dependency. as highlighted by [37], LSTM is limited when the input time series is very
long. Fu et al. [38] compared gated recurrent unit (GRU) and LSTM by using the traffic data collected by 30
random sensors from the performance management system (PeMS) dataset. A strategy for managing traffic is
to use traffic lights [39] with sporadic fixed times for each road, but this cannot be considered a good solution
because congestion will increase on specific roads with high vehicle traffic.
Furthermore, the use of cameras is complemented by modern technologies such as computer graphics,
automated video analysis for traffic monitoring, state-of-the-art cameras and high-end computing power [40].
Delica et al. [41], a simulation program for an intelligent traffic light system was created using the virtual
instrumentation laboratory environment (LabVIEW). It intends to measure traffic density by examining the
number of vehicles in each lane. Kareem and Hoomod [42] presents an integrated three-part module for
intelligent traffic signal systems. This paper presents five machine learning models: linear regression, random
forest regressor, decision tree regressor, gradient boosting regressor, K neighbors regressor; compared in the
task of predicting traffic flow at intersections, with the purpose of applying them to an adaptive traffic light
system, this method allows for a better traffic flow without completely changing the traffic light system
structure. Experiments showed that all models have good vehicular flow prediction capabilities and can be used
effectively to develop our traffic management system. The rest of this paper is organized as follows: section 2
explores several machine learning algorithms that has being employed in this work; section 3 discusses the
experimentation and evaluation; finally, section 4 presents the conclusion and perspectives.
2. METHOD AND IMPLEMENTATION
The goal of this study is to reduce traffic congestion. The measure for traffic congestion is based on
the density of vehicles on the roadways being monitored. Density is a commonly used measure of traffic
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congestion, and it refers to the number of vehicles present on a particular stretch of road at a given time. The
proposed architecture is as follows: We first collect traffic data from cameras and sensors, then deploy the
cross-industry standard process for data mining (CRISP-DM) model to process the collected data and apply
machine learning algorithms. After implementing ML algorithms, we apply the grid search method to find the
best hyper-parameters. Next, a density reduction function is then applied based on the change of the green-
light and red-light duration. Finally, we feed the obtained results to the traffic light controller. This model is
better illustrated in Figure 1.
Figure 1. The proposed adaptive traffic light system
In this paper, the UK National Road traffic estimation statistics publication's road dataset was used to
train, validate, and test the proposed system. This is a public dataset for traffic prediction derived from a variety
of traffic sensors. It is important to note that only a few public transport data are available.
Beginning our analysis with some form of exploratory data analysis is consistently a wise decision.
This way, we can determine dataset properties such as data format, number of samples, and data types. The
dataset contains a total of 495,562 data points with 32 attributes. A sample data is shown in Figure 2. The
dataset's variables were categorized as either categorical or numerical depending on their characteristics.
Handling a large number of features may have an impact on the model's performance and increase the risk of
overfitting, as training time grows exponentially with the number of features.
Using a measure of centrality is the easiest and quickest approach to imputing missing values [43].
Such measures represent the most common value in a variable's distribution and are therefore a logical option
for this method. There are multiple measures of centrality available, such as the mean and median. The most
appropriate measure depends on the distribution of the variable. Figure 3 shows that the data follows a normal
distribution, where all data points are closely gathered around the mean. This measure is the optimal choice to
impute the missing values.
The second step is to explore the relationships between variables. This analysis enables us to identify
the most correlated pairs, pairs that are highly correlated represent the same variance of the data, this allows us
to further investigate the pairs to better understand which attribute is most important for constructing our
models. The correlation matrix in Figure 4 shows a strong correlation between start_iunction_Raod_name,
end_junction_raod-name, day, month, year, road_name, red-time, and green-time attributes.
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Figure 2. A sample of the used dataset containing quantitative and qualitative data
Figure 3. Distribution of vehicles by year and by road across all sensing segments
Figure 4. Correlation matrix as Heat Map shows good correlation between day, month, year, raodName,
startJunctionRaodName, endJunctionRaodName, redTime, and greenTime
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2.1. Data cleaning
A common practice during this task is to correct or delete erroneous values. The problem of dealing
with missing data is an important issue, as this data cannot be ignored in a machine learning analysis. This
issue can be solved by imputation techniques which consist in producing an "artificial value" to replace the
missing value to generate approximately unbiased estimates. For this we chose the method of imputation by
average which consists in replacing the null values by the average of the whole column. And also, the method
more often for categorical fields. We have applied two functions that go through all the quantitative, qualitative
data and replace them using the impute function in the sklearn library.
2.2. Encoding of categorical characteristics
This is an important preprocessing step for structured data sets in supervised learning. Most machine
learning algorithms only utilize numerical data. Our dataset contains four categorical variables that must be
encoded: direction_of_travel, start_junction_road_name, road_name and end_junction_road_name. This
information is detailed about roads that are of a qualitative nature, as a result, we must encode the qualitative
characteristics in a representation in order to convert the machine-readable form. This is accomplished using
the LabelEncoder from the sklearn library.
2.3. Machine learning methods
Five regression models were implemented in Python using scikit-learn library [44]: linear regression,
random forest regression, decision tree regression, gradient boosting regression, and K-neighborhood
regression. All of these models have default parameters. Additionally, most machine learning algorithms have
a large number of parameters that can be tuned to control the modeling process. These measures are called
hyper-parameters and must be adjusted so that the model can obtain optimum results. Grid search method is
used, which is simply an exhaustive search in a manually specified subset of the hyperparameter space of a
learning algorithm, for ease of understanding a list of values is established for each hyperparameter each
hyperparameter and then the one that gives the most accuracy is chosen.
3. RESULTS AND SIMULATION
3.1. Obtained results
To evaluate the performance of machine learning algorithms, we relied on metrics from the scikit-
learn library [44] using mean absolute error (MAE) [45], [46], root mean squared error (RMSE) [47], [48], and
R-squared (R2) [49], [50]. They are defined as (1)-(3):
𝑀𝐴𝐸(𝑦, 𝑦
̂ ) =
1
𝑛
∑ |𝑦𝑖 − 𝑦
̂𝑖|
𝑛−1
𝑖=0 (1)
𝑅2(𝑦, 𝑦
̂ ) = 1 −
∑ (𝑦𝑖−𝑦
̂𝑖)2
𝑛
𝑖=1
∑ (𝑦𝑖−𝑦
̅)2
𝑛
𝑖=0
(2)
𝑅𝑀𝑆𝐸(𝑦, 𝑦
̂ ) = [
1
𝑛
∑ (𝑦𝑖 − 𝑦
̂𝑖)2
𝑛−1
𝑖=0 ]
1
2
(3)
where 𝑛 is number of samples, 𝑦 is observed traffic flow, 𝑦
̂ is predicted traffic flow, and 𝑦
̅ is mean.
MAE and RMSE measure absolute prediction error. Smaller numbers indicate higher prediction
performance for two three metrics. The value of 𝑅2
ranges from 0 to 1, with values closer to 1 indicating a
better fit of the regression model.
Once the data is prepared for model training, machine learning algorithms usually have many
adjustable values or parameters, referred to as hyperparameters, that can influence the model's performance.
Therefore, it is necessary to tune these hyperparameters to optimize the model's ability to solve the machine
learning problem. To achieve this, we utilized the grid search method, which involves exhaustively searching
through a manually specified subset of the hyperparameter space of a learning algorithm. In this approach, a
set of values is established for each hyperparameter, and the one that yields the highest accuracy is chosen.
Table 1 presents the various hyperparameters used for each model.
Table 2 lists the performance metrics of each machine learning models. The results show that for the
exception of linear regression model, random forest regressor gets better results (R-Squared of 0.98) and takes
longer time to train, followed by decision tree regressor (score of 0.97) with better results and less training
time, then gradient boosting regressor (score of 0.97), but with more training time, and finally K-neighbors
regressor, linear regression. Therefore, we can confirm that random forest regressor model is a type of ensemble
learning method that combines multiple decision trees to generate predictions. It is known for its ability to
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handle large datasets with high-dimensional features, while avoiding overfitting. Therefore, it is a robust and
reliable model for traffic flow prediction.
Table 1. Hyperparameters values used in the grid search method for each model
Model Hyperparameters
Random forest regressor (max_depth=30, max_features='auto', min_samples_leaf=3, min_samples_split=6, n_estimators=200)
Decision tree regressor (splitter='best', max_depth=45, min_samples_split=5, min_samples_leaf=2, max_features='auto')
K neighbors regressor (n_neighbors=6, algorithm='brute', weights='distance')
Gradient boosting regressor (learning_rate=0.8, n_estimators=1200, max_depth=10)
Table 2. Comparison of machine learning models performance metrics
Model RMSE R2 MAE
Linear regression 511.908 0.5906 318.717
Random forest regressor 111.844 0.980 54.826
Decision tree regressor 134.135 0.971 63.209
Gradient boosting regressor 125.709 0.975 63.203
K neighbors regressor 147.039 0.966 71.713
In order to evaluate the cost-benefit of implementing the machine learning models proposed in this
study, we conducted experiments to determine the average training time for each model. These experiments
were carried out using the Google Collaboratory platform [51], which allowed us to perform the necessary
computations in a cloud-based environment. For each of the five machine learning models tested in this study,
we recorded the time it took to train the model on the UK National Road Traffic Estimation Statistics
Publication's Road dataset, which was used for both training and testing. The training time was measured in
seconds, and the average training time for each model was calculated by taking the mean of the training times
across all experiments. Figure 4 presents the results of our experiments in the form of a bar chart, with each
bar representing the average training time for a particular machine learning model. As can be seen from the
Figure 5, the training time varies widely across the different models, with the decision tree model having the
shortest average training time and the neural network model having the longest.
Figure 6 shows a comparison between the actual data and the predicted data using five different
machine learning models. Figure 6(a) shows the results of linear regression, Figure 6(b) shows the results of
random forest regressor, Figure 6(c) shows the results of decision tree regressor, Figure 6(d) shows the results
of gradient boosting regressor, and Figure 6(e) shows the results of K-neighbors regressor.
In each subfigure, the orange line represents the actual traffic flow data, while the blue line represents
the predicted traffic flow data. The x-axis represents the time period while the y-axis represents the density
values for each segment of the intersection. The results show that the random forest regressor and gradient
boosting regressor models outperformed the other models, with the lowest prediction error and the highest
accuracy. This comparison was based on different evaluation metrics, such as mean absolute error (MAE),
mean squared error (MSE), and coefficient of determination (R-squared). These metrics were computed by
comparing the actual data with the predicted data using the test split (20%) of the dataset. The results were then
plotted in Figure 6 to provide a visual representation of the performance of each model.
Figure 5. Machine learning model’s execution time
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(a) (b)
(c) (d)
(e)
Figure 6. Comparison between the actual data and the predicted data: (a) linear regression, (b) random forest
regressor, (c) decision tree regressor, (d) gradient boosting regressor, and (e) k neighbor’s regressor
The evaluation of any model is a critical step in determining its effectiveness in solving the targeted
problem. In the case of our proposed system aimed at reducing traffic congestion, the evaluation process is
crucial to understand its performance and efficiency. To conduct this evaluation, we will analyze the predicted
density values for each segment of an intersection before and after the application of the reduction function.
By comparing these values, we can determine the extent to which our proposed system has been successful in
reducing traffic congestion. Table 3 showcases the results of this analysis, highlighting the effectiveness of our
reduction function in minimizing traffic congestion at intersections.
3.2. Traffic simulation
Simulation is a valuable tool for comprehending real-world scenarios, and traffic simulation is widely
employed to analyze road traffic congestion. Numerous studies have employed simulation models to simulate
traffic congestion, with diverse traffic models accessible, predominantly comprising microscopic,
macroscopic, and mesoscopic models. Microscopic models consider the interaction of individual vehicles,
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encompassing parameters such as driver behavior, vehicle position, velocity, acceleration, and more.
Conversely, macroscopic models analyze the collective behavior of traffic flows, while mesoscopic models
integrate features of both macroscopic and microscopic models, describing the impact of nearby vehicles and
approximating the cumulative temporal and spatial traffic behavior. In this scholarly paper, we designed and
implemented a traffic simulation in Python employing Pygame and a microscopic module (as illustrated in
Figure 6 to assess the duration of red and green lights at an intersection. At each time step, the real-time
simulation module defines the state of the traffic light phase (red, green) and the duration of each phase.
Table 3. Results obtained from the prediction made for each segment of one intersection with and without the
application of the reduction function
Segment Density without the function Density with the function
0 378 90
1 666 274
2 1690 806
3 1793 850
Figure 7 illustrate a microscopic model in which, each vehicle in the microscope model is assigned a
number 𝑖. The 𝑖𝑡ℎ
vehicle follows the (𝑖-1)th
vehicle. For the 𝑖𝑡ℎ
vehicle, let 𝑥𝑖 be the position on the road, 𝑣𝑖
be the velocity, and 𝑙𝑖 be the length. And that goes for all vehicles.
𝑆𝑖 = 𝑥𝑖 − 𝑥𝑖−1 − 𝑙𝑖 (4)
∆𝑣𝑖 = 𝑣𝑖 − 𝑣𝑖−1 (5)
The vehicle-to-vehicle (V2V) distance will be represented by 𝑆𝑖 and ∆𝑣𝑖 the velocity difference
between the 𝑖𝑡ℎ
vehicle and the vehicle in front of it (vehicle number i-1). Figure 8 provides a graphical
representation of an intersection during the simulation model, illustrating the layout of the road and the
positioning of traffic lights. The intersection is divided into several segments, with each segment representing
a different part of the road, such as a straight lane or a turning lane. The simulation model takes into account
the movement of various types of vehicles, such as cars and trucks, and their interaction with other vehicles
and the traffic lights. The model also considers the effect of the reduction function on traffic density and adjusts
the traffic light timings accordingly. Overall, the representation of the intersection in Figure 8 provides a clear
understanding of the simulation model and how it factors in various elements to optimize traffic flow.
Figure 7. Illustration of a microscopic model
Figure 8. Representation of an intersection during the simulation
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This simulation evaluates the optimal timing for the green and red signals at an intersection by using
timers on each light to indicate when the light will change. Various types of vehicles are generated and their
movements are influenced by the signals and surrounding vehicles. The control system applies prediction and
density reduction functions to determine signal plant values, which are then sent to the traffic controller to
adjust the traffic lights accordingly. The results of this simulation demonstrate the effectiveness of the proposed
system in significantly reducing traffic congestion.
4. CONCLUSION
The focus of this paper was to develop a machine learning-based system that can predict traffic flow
at intersections. To achieve this, we implemented several machine learning models and compared their
performance. The results showed that the random forest regressor model outperformed the others with an
R-squared and EV score of 0.98. The random forest regressor model is a tree-based ensemble algorithm that
works by constructing a multitude of decision trees and then outputs the mean prediction of the individual trees.
This algorithm is known for its high accuracy, but it also takes more processing time due to its complexity.
The decision tree regressor model had a score of 0.97, which is slightly lower than the random forest regressor,
but it provided a good balance between measurement of results and training time. The gradient boosting
regressor model showed a similar performance to the decision tree regressor but took more processing time.
The K neighbors regressor and linear regression models had relatively lower scores, but their performance did
not differ significantly from the others. To validate the effectiveness of the proposed system, we simulated the
traffic flow at intersections using a microscopic model. The model used a traffic density reduction function to
adjust the green and red-light duration, which helped reduce traffic congestion significantly. The efficiency of
the suggested system in decreasing traffic congestion provides justification for its deployment at key
intersections. However, to enhance the system further, we aim to incorporate additional technologies. One
potential area of improvement involves the data used to train the models. Although the dataset used in this
study was obtained from various traffic sensors, we intend to explore the use of other data sources, such as
traffic camera images, to improve the models' precision. Additionally, we plan to integrate real-time data feeds
into the system, enabling it to make real-time decisions, further enhancing its efficacy in managing traffic flow
at intersections. In conclusion, the proposed system demonstrates promising results in forecasting traffic flow
at intersections and reducing traffic congestion. Although the random forest regressor model proved to be the
most precise, the decision tree regressor offered a good balance between accuracy and training time. This
system has the potential to be a valuable tool for managing traffic at major intersections, and we anticipate
further development and improvement in the future.
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BIOGRAPHIES OF AUTHORS
Idriss Moumen born in 1990 in Kenitra. He received his Master’s degree in
computer science, Computer Sciences Researches from Ibn Tofail University, Kenitra, Morocco.
He is a Ph.D. student in Computer Research Laboratory (LaRI) at Ibn Tofail. His research
interests include big data, data mining, machine and deep learning, distributed computing. He
can be contacted at email: idriss.moumen@uit.ac.ma.
Jaafar Abouchabaka was born in Guersif, Morocco, 1968. He has obtained two
doctorates in Computer Sciences applied to mathematics from Mohammed V University, Rabat,
Morocco. Currently, he is a Professor at Department of Computer Sciences, Ibn Tofail
University, Kenitra, Morocco. His research interests are in concurrent and parallel programming,
distributed systems, multi agent systems, genetics algorithms, big data and cloud computing. He
can be contacted at email: jaafar.abouchabaka@uit.ac.ma.
Najat Rafalia was born in Kenitra, Morocco, 1968. She has obtained three
doctorates in Computer Sciences from Mohammed V University, Rabat, Morocco by
collaboration with ENSEEIHT, Toulouse, France, and Ibn Tofail University, Kenitra, Morocco.
Currently, she is a Professor at Department of Computer Sciences, Ibn Tofail University,
Kenitra, Morocco. Her research interests are in distributed systems, multi-agent systems,
concurrent and parallel programming, communication, security, big data, and cloud computing.
She can be contacted at email: arafalia@yahoo.com.