scholarly journals Application of support vector machine in a traffic lights control

2020 ◽  
Vol 14 ◽  
pp. 37-42
Author(s):  
Artur Całuch ◽  
Adam Cieślikowski ◽  
Małgorzata Plechawska-Wójcik
Keyword(s):  
Support Vector Machine ◽  
Cycle Length ◽  
Support Vector ◽  
Light Cycle ◽  
Traffic Light ◽  
Traffic Lights ◽  
Using Data

This article presents the process of adapting support vector machine model’s parameters used for studying the effect of traffic light cycle length parameter’s value on traffic quality. The survey is carried out using data collected during running simulations in author’s traffic simulator. The article shows results of searching for optimum traffic light cycle length parameter’s value.

scholarly journals Developing Hybrid Simulation Model to Improve Road Traffic Management

2020 ◽  
Vol 4 (01) ◽  
pp. 56-65
Author(s):  
Hayati Mukti Asih
Keyword(s):  
Simulation Model ◽  
Urban Areas ◽  
Traffic Management ◽  
Road Traffic ◽  
Hybrid Simulation ◽  
Simulation Method ◽  
Vital Role ◽  
Light Cycle ◽  
Traffic Light ◽  
Traffic Lights

Yogyakarta has increasing trends in the number of vehicles and consequently intensifying the traffic volume and will effect to higher emission and air pollution. Traffic lights duration plays a vital role in congestion mitigation in the critical intersections of urban areas. This study has objective to minimize the number of vehicles waiting in line by developing the hybrid simulation method. First of all, the MKJI and Webster method were calculated to determine the green traffic light. Then, the simulation model was developed to evaluate the number of vehicles waiting in line according to different duration of green traffic lights from MKJI and Webster method. A case study will then be provided in Pelemgurih intersection located in Yogyakarta, Indonesia for demonstrating the applicability of the developed method. The result shows that the duration of green traffic lights calculated by Webster method provides lower number of vehicles waiting in line. It is due to the short duration of green traffic light resulted by Webster method so that the traffic light cycle becomes shorter and it effects the number of vehicles waiting in line which is lower than MKJI method. The results obtained can help the generating desired decision alternatives that will important for Department of Transportation, Indonesia to enhance the road traffic management with low number of vehicles waiting in line.

scholarly journals Classification of Hospital Web Security Efficiency Using Data Envelopment Analysis and Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Han-Ying Kao ◽  
Tao-Ku Chang ◽  
Yi-Cheng Chang
Keyword(s):  
Support Vector Machine ◽  
Data Envelopment Analysis ◽  
Web Security ◽  
Support Vector ◽  
Data Envelopment ◽  
Learning Patterns ◽  
Dea Models ◽  
Efficiency Estimation ◽  
Hybrid Data ◽  
Using Data

This study proposes the hybrid data envelopment analysis (DEA) and support vector machine (SVM) approaches for efficiency estimation and classification in web security. In the proposed framework, the factors and efficiency scores from DEA models are integrated with SVM for learning patterns of web security performance and provide further decision support. The numerical case study of hospital web security efficiency is demonstrated to support the feasibility of this design.

scholarly journals DIAGNOSA KERUSAKAN BANTALAN BOLA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Muhammad Fathurrohman ◽  
R. Lulus Lambang G. H ◽  
Didik Djoko Susilo
Keyword(s):  
Support Vector Machine ◽  
Ball Bearing ◽  
Training Model ◽  
Support Vector ◽  
Classification Problems ◽  
Vibration Signals ◽  
Svm Classification ◽  
Outer Race ◽  
Rbf Kernel ◽  
Using Data

<p><em>Bearings are the critical part of any rotating machine. The catastrophic failure of the bearing can lead to fatal and harmful to the operation of the machine. Therefore, predictive maintenance based on condition monitoring of bearing is very important. The objective of this research is to apply Support Vector Machine (SVM) method for fault diagnosis of the ball bearing. The research was carried out at the bearing test rig. Four types of ball bearing condition, such as normal, inner race defect, ball defect, and outer race defect were measured of the vibration signals using data acquisition with a sampling frequency of 20 kHz at the constant speed of 1400 RPM. Various features were extracted from vibration signals in time domain, such as RMS, variance, standard deviation, crest factor, shape factor, skewness, kurtosis, log energy entropy and sure entropy. PCA transformation was employed to reduce the dimension of feature extracted data. SVM classification problems were solved using MATLAB 2016a. The results showed that the application of RBF kernel function with the C parameter =1 was the best configuration. The training model accuracy was 98.93% and the testing accuracy of SVM was 97.5%. Finally, the research results show that the SVM classification method can be used to diagnose the fault condition of the ball bearing.</em><em>.</em></p>

scholarly journals Optimization of Traffic Flow On KS Tubun Street using Greenberg Model

2019 ◽  
Vol 8 (2S7) ◽  
pp. 83-86
Keyword(s):  
Traffic Flow ◽  
Red Light ◽  
Green Light ◽  
Light Cycle ◽  
Traffic Light ◽  
Traffic Jam ◽  
Traffic Lights ◽  
The Road ◽  
Road Users ◽  
Time Parameters

KS Tubun Street is a street in Bogor, which has a fairly high vehicle volume and become one of a high-traffic jam area. This is caused by KS Tubun Street is the main road for road users from Jakarta and Bogor. Traffic jam problem that occurs due to the confluence interchange of traffic flow and traffic lights settings that are not proportional to the volume of vehicles across the road. Optimization of traffic flow at KS Tubun Street performed by the stages of forming a model of traffic flow, determining the density and velocity of the vehicle is based on the Greenberg model, and determining the length of the traffic lights to avoid a buildup of vehicles. The result is a traffic flow model with distance and time parameters. The density of vehicles that occurs on the streets of KS. Tubun street based on the Greenberg model between 180 to 240 unit car of passanger (ucp) with the average velocity of vehicles 15 to 19.5 km per hour. The density of vehicles on KS. Tubun street can be break down by increasing time. Traffic light cycle time can be reduced for 8 seconds with the red light glowing time is 80 seconds and the green light glowing time is 62 seconds.

A Method for Classification Using Data Mining Technique for Diabetes

2016 ◽  
pp. 738-761
Author(s):  
Ahmad Al-Khasawneh
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Health Information System ◽  
Parameters Optimization ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Predictive Data Mining ◽  
Severity Of The Disease ◽  
Using Data

Many researchers in the health information system field have been attracted to develop computer applications that help in the diagnosis process. Imperatively, data mining algorithms address the vital role in all of these applications. Many contributions were made in this area. There has always been a debate on the algorithm that gives the best classifier, the parameters to be used, the dataset pre-processing steps, etc. In this paper, the author largely emphasizes that the best way to build a predictive model with relatively high classification accuracy is to build several predictive models and to choose the model that gives the best results through parameters optimization. Diagnosing diabetes mellitus has gained considerable attention in the last few decades due to the increased severity of the disease. In this research, the author reviews four predictive data mining approaches that are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset; k-nearest neighbour, support vector machine, multilayer perceptron neural network, and naive bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.

Nonlinearity Compensation for Thermocouple Vacuum Gauge Using Particle Swarm Optimized Least Squares Support Vector Machine

2013 ◽  
Vol 321-324 ◽  
pp. 2177-2182
Author(s):  
Yao Geng Tang ◽  
Song Gao ◽  
Xing Qu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Vacuum Gauge ◽  
Swarm Optimization ◽  
Svm Model ◽  
Using Data ◽  
Nonlinearity Characteristic

A method for compensating nonlinear characteristic of thermocouple vacuum gauge is proposed. Least squares support vector machine (LS-SVM) is adopt as compensation model, of which parameters are optimized using particle swarm optimization (PSO) algorithm. Experimental results using data obtained on-site show that the proposed approach effectively compensates the nonlinearity characteristic, and the accuracy of this method is higher than those obtained by SVM model.

scholarly journals Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition

2020 ◽  
Author(s):  
Hendrik Macedo ◽  
Thiago Almeida ◽  
Leonardo Matos ◽  
Bruno Prado
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Background Suppression ◽  
Processing Unit ◽  
Traffic Light ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Computational Resources

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

scholarly journals Recent development of smart traffic lights

2021 ◽  
Vol 10 (1) ◽  
pp. 224
Author(s):  
A’isya Nur Aulia Yusuf ◽  
Ajib Setyo Arifin ◽  
Fitri Yuli Zulkifli
Keyword(s):  
Traffic Flow ◽  
Time Allocation ◽  
Traffic Density ◽  
Light Cycle ◽  
Traffic Light ◽  
Large Cities ◽  
Traffic Lights ◽  
Smart Traffic ◽  
Very High ◽  
Signal Time

<span id="docs-internal-guid-288f4dcc-7fff-1e8c-0350-5032593b6e4f"><span>Increased traffic flow causes congestion, especially in large cities. Even though congestion is not unusual, traffic jams still result in very high economic and social losses. Several factors cause congestion, one of which is traffic lights. Therefore, a mechanism is needed so that traffic lights can intelligently and adaptively manage signal time allocation according to traffic flow conditions. A traffic light with this type of mechanism is known as a smart traffic light. Smart traffic light cycle settings can be grouped based on the traffic density, scenarios for emergency vehicles, and the interests of pedestrians. This paper analyzes the methods and technologies used in the development of smart traffic light technology from the perspective of these three situations as well as the development of smart traffic light technology in the future.</span></span>

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