• Journal of information and communication convergence engineering
• /
• v.21 no.3
• /
• pp.225-232
• /
• 2023

Owing to advancements in intelligent transportation systems (ITS) and artificial-intelligence technologies, various machine-learning models can be employed to simulate and predict the number of traffic accidents under different weather conditions. Furthermore, we can analyze the relationship between weather and traffic accidents, allowing us to assess whether the current weather conditions are suitable for travel, which can significantly reduce the risk of traffic accidents. In this study, we analyzed 30000 traffic flow data points collected by traffic cameras at nearby intersections in Washington, D.C., USA from October 2012 to May 2017, using Pearson's heat map. We then predicted, analyzed, and compared the performance of the correlation between continuous features by applying several machine-learning algorithms commonly used in ITS, including random forest, decision tree, gradient-boosting regression, and support vector regression. The experimental results indicated that the gradient-boosting regression machine-learning model had the best performance.

• ETRI Journal
• /
• v.42 no.3
• /
• pp.311-323
• /
• 2020

Encrypted traffic classification plays a vital role in cybersecurity as network traffic encryption becomes prevalent. First, we briefly introduce three traffic encryption mechanisms: IPsec, SSL/TLS, and SRTP. After evaluating the performances of support vector machine, random forest, naïve Bayes, and logistic regression for traffic classification, we propose the combined approach of entropy estimation and artificial neural networks. First, network traffic is classified as encrypted or plaintext with entropy estimation. Encrypted traffic is then further classified using neural networks. We propose using traffic packet's sizes, packet's inter-arrival time, and direction as the neural network's input. Our combined approach was evaluated with the dataset obtained from the Canadian Institute for Cybersecurity. Results show an improved precision (from 1 to 7 percentage points), and some application classification metrics improved nearly by 30 percentage points.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.4
• /
• pp.346-352
• /
• 2011

This paper presents a system of road extraction for traffic images from a single camera. The road in the images is subject to large changes in appearance because of environmental effects. The proposed system is based on the integration of color correlograms and random forest. The color correlogram depicts the color properties of an image properly. Using the random forest, road extraction is formulated as a learning paradigm. The combined effects of color correlograms and random forest create a robust system capable of extracting the road in very changeable situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.6
• /
• pp.36-56
• /
• 2022

Traffic accidents occur in Korea are calculated with the 「Automobile Accident Negligence Ratio Certification Standard」 prepared by the 'General Insurance Association of Korea' and the insurance company's agreement or judgment is made. However, disputes are frequently occurring in calculating the negligence ratio. Therefore, it is thought that a more effective response would be possible if accident type according to the standard could be quickly identified using traffic accident information prepared by police. Therefore, this study aims to develop a model that learns the accident information prepared by the police and classifies it to match the accident type in the standard. In particular, through data mining, keywords necessary to classify the accident types of the standard were extracted from the accident data of the police. Then, models were developed to derive the types of accidents by learning the extracted keywords through decision trees and random forest models.

• Journal of Broadcast Engineering
• /
• v.20 no.6
• /
• pp.938-949
• /
• 2015

In this paper, we propose a robust speed-limit sign recognition system which is durable to any sign changes caused by exterior damage or color contrast due to light direction. For recognition of speed-limit sign, we apply CNN which is showing an outstanding performance in pattern recognition field. However, original CNN uses multiple hidden layers to extract features and uses fully-connected method with MLP(Multi-layer perceptron) on the result. Therefore, the major demerit of conventional CNN is to require a long time for training and testing. In this paper, we apply randomly-connected classifier instead of fully-connected classifier by combining random forest with output of 2 layers of CNN. We prove that the recognition results of CNN with random forest show best performance than recognition results of CNN with SVM (Support Vector Machine) or MLP classifier when we use eight speed-limit signs of GTSRB (German Traffic Sign Recognition Benchmark).

• Journal of Korean Medicine Rehabilitation
• /
• v.30 no.4
• /
• pp.31-39
• /
• 2020

Objectives Danggwisoo-san and Jakyakgamcho-tang are frequently prescribed for traffic accident patients in Korea. The aim of this study was to examine index compound analysis, antioxidant activity and amount of starch measurement by extraction method. Methods Danggwisoo-san and Jakyakgamcho-tang were extracted with water and 70% ethanol. Antioxidant activity was measured by 2,2-Diphenyl-1-picrylhydrazyl, 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) and ferric reducing antioxidant power according to the standard protocol. The contents of the indicator components nodakenin, paeoniflorin, and glycyrrhizin were measured by high-performance liquid chromatography, respectively. All starches were hydrolyzed and then total D-glucose was measured and compared. Results Antioxidant activity was excellent in 70% ethanol in all assays. The index component was jagged because its solubility was different depending on the extraction solvent. Starch content was significantly lower in 70% alcohol extract than water extract. Conclusions The results of this study showed that physiological activities and components are different according to extraction conditions. Each herbal medicine has a suitable extraction solvent. Also, the difference in starch content is an object to be considered as it may affect digestion and absorption.

• Journal of Korea Multimedia Society
• /
• v.17 no.6
• /
• pp.681-686
• /
• 2014

In this paper, we propose a traffic adaptive mechanism that controls the receiver's wakeup periods based on the generated traffic amounts. The proposed control mechanism is designed for military, wild animal monitoring, and forest fire surveillance applications. In these environments, a low-rate data transmission is usually required between sensor nodes. However, continuous data is generated when events occur. Therefore, legacy mechanisms are ineffective for these applications. Our control mechanism showed a better performance in energy efficiency compared to the RI-MAC owing to the elimination of the sender node's idle listening.

• International Journal of Computer Science & Network Security
• /
• v.23 no.12
• /
• pp.91-100
• /
• 2023

As SDN devices and systems hit the market, security in SDN must be raised on the agenda. SDN has become an interesting area in both academics and industry. SDN promises many benefits which attract many IT managers and Leading IT companies which motivates them to switch to SDN. Over the last three decades, network attacks becoming more sophisticated and complex to detect. The goal is to study how traffic information can be extracted from an SDN controller and open virtual switches (OVS) using SDN mechanisms. The testbed environment is created using the RYU controller and Mininet. The extracted information is further used to detect these attacks efficiently using a machine learning approach. To use the Machine learning approach, a dataset is required. Currently, a public SDN based dataset is not available. In this paper, SDN based dataset is created which include legitimate and non-legitimate traffic. Classification is divided into two categories: binary and multiclass classification. Traffic has been classified with or without dimension reduction techniques like PCA and LDA. Our approach provides 98.58% of accuracy using a random forest algorithm.

• Proceedings of the Korean Society of Disaster Information Conference
• /
• 2023.11a
• /
• pp.257-258
• /
• 2023

본 연구는 2019년부터 2021년의 고속도로 교통사고 위치 좌표를 콘존 데이터와 결합한 후 파이캐럿을 활용하여 고속도로 교통사고 심각도에 영향을 끼치는 요인을 분석할 수 있는 최적 모델을 선정하고 채택된 Random Forest 기법으로 고속도로 교통사고 심각도에 영향을 끼치는 요인을 분석하고자 하였으며, 향후 전국 고속도로 교통사고에 영향을 주는 요인으로 확대하여 분석하고 사고 심각도 개선을 위한 대안 방안 마련이 가능할 것으로 판단된다.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.95-100
• /
• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.