• Title/Summary/Keyword: Traffic Light

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Design and Application of Traffic Safety Technology in Chungcheong non-urban Region (충청권 비도심 지역의 교통안전기술 설계 및 적용)

  • Cho, Choong-Yeon;Kim, Yun-Sik;Lee, Min-Jae
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.4
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    • pp.264-272
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    • 2016
  • In previous research, we analyzed traffic accident characteristics in the Chungcheong region through factor analysis, cluster analysis, and a questionnaire using traffic accident analysis system data to enhance Korea's traffic safety. Based on the analysis results, we investigated the design and application of traffic safety technology in non-urban areas in this study. Three technologies are proposed to improve traffic safety facilities for the region: a recognition light at pedestrian crossing works, a recognition light on the road for the underprivileged in traffic works, and a safety LED sign for operation of agricultural machine works. Each technology complements the light pollution problem about snow removal and road safety when applied to existing facilities in the non-urban areas. Solar-based indigenous technology is expected to contribute to road safety in rural areas.

Predicting of the Severity of Car Traffic Accidents on a Highway Using Light Gradient Boosting Model (LightGBM 알고리즘을 활용한 고속도로 교통사고심각도 예측모델 구축)

  • Lee, Hyun-Mi;Jeon, Gyo-Seok;Jang, Jeong-Ah
    • The Journal of the Korea institute of electronic communication sciences
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    • v.15 no.6
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    • pp.1123-1130
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    • 2020
  • This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.

Development of A Traffic Network Controller using Fuzzy Logic (퍼지 논리를 사용한 교통망 제어기의 개발)

  • Kim, Jong-Wan;Han, Byung-Joon
    • The Transactions of the Korea Information Processing Society
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    • v.5 no.11
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    • pp.2908-2914
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    • 1998
  • This paper presents an intelligent signal for controling the traffic lights on traffic junction network with dynamic traffic flow, When a junction is connected to adjacent junctions on four sides. Prior researches have been done on the single traffic junction. However, it is dificult to apply single junction controller to real traffic situation. In this paper, we develop a fuzzy taffic network controller which adjusts the extension time of current green phase by using teh fuzzy input variables such as the number of entering cars at the green light, the number of waiting cars during the red light, and the traffic volume. The proposed method was compared to the existing junction signal control methods on controllers in terms of average delay time of cars and the cost function defined in this paper.

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Darknet Traffic Detection and Classification Using Gradient Boosting Techniques (Gradient Boosting 기법을 활용한 다크넷 트래픽 탐지 및 분류)

  • Kim, Jihye;Lee, Soo Jin
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.32 no.2
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    • pp.371-379
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    • 2022
  • Darknet is based on the characteristics of anonymity and security, and this leads darknet to be continuously abused for various crimes and illegal activities. Therefore, it is very important to detect and classify darknet traffic to prevent the misuse and abuse of darknet. This work proposes a novel approach, which uses the Gradient Boosting techniques for darknet traffic detection and classification. XGBoost and LightGBM algorithm achieve detection accuracy of 99.99%, and classification accuracy of over 99%, which could get more than 3% higher detection accuracy and over 13% higher classification accuracy, compared to the previous research. In particular, LightGBM algorithm could detect and classify darknet traffic in a way that is superior to XGBoost by reducing the learning time by about 1.6 times and hyperparameter tuning time by more than 10 times.

FPGA-based Traffic Message Delivery System for Car-to-car Communications Using Visible Light Communication Link (가시광 통신링크를 이용한 FPGA기반 차량 메시지 전송 시스템)

  • Kim, Jong-Young;Cho, Eunbyeol;Hwang, Sung-Jo;Park, Bong-Seok;Lee, Chung Ghiu
    • Transactions of the Korean Society of Automotive Engineers
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    • v.24 no.4
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    • pp.386-391
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    • 2016
  • A traffic message delivery system using visible light communication(VLC) link has been demonstrated. The system is proposed to deliver simple traffic messages between cars at low speed. The message set is programmed in an FPGA-based digital board and one of the messages is sent to the other car. Considering the outdoor and indoor environments, the effects of sunlight and fluorescent lamps on received signal waveforms are described. The delivered message is successfully recovered over 2 meter. The link for message delivery can be concatenated.

Customers' perception of the attributes of different formats of menu labeling: a comparison between Korea and the U.S.

  • Bosselman, Robert;Choi, Hyung-Min;Lee, Keum Sil;Kim, Eojina;Cha, Jaebin;Jeong, Jin-Yi;Jo, Mina;Ham, Sunny
    • Nutrition Research and Practice
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    • v.14 no.3
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    • pp.286-297
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    • 2020
  • BACKGROUND/OBJECTIVES: This study compared the perception of customers from Korea and the U.S. on the attributes of different formats of menu labeling The specific objectives were 1) to compare the customers' perceived usefulness, ease-of-understanding, clarity, and attractiveness of different formats of menu labeling between Korea and the U.S.; and 2) to compare the customers' use intention to different formats of menu labeling between Korea and the U.S. SUBJECTS/METHODS: A survey was conducted in Korea and the U.S. The participants were allocated randomly to view 1 of the 7 restaurant menus that varied according to the following types of menu labeling formats: (type 1) kcal format, (type 2) traffic-light format, (type 3) percent daily intake (%DI) format, (type 4) kcal + traffic-light format, (type 5) kcal + %DI format, (type 6) traffic-light + %DI format, and (type 7) kcal + traffic-light + %DI format. A total of 279 Koreans and 347 Americans were entered in the analysis. An independent t-test and 1-way analysis of variance were performed. RESULTS: Koreans rated type 4 format (kcal + traffic light) the highest for usefulness and attractiveness. In contrast, Americans rated type 7 (kcal + traffic light + %DI) the highest for usefulness, ease-of-understanding, attractiveness, and clarity. Significant differences were found in the customers' perceived attributes to menu labeling between Korea and the U.S. Americans perceived higher for all the 4 attributes of menu labeling than Koreans. CONCLUSIONS: The study is unique in identifying the differences in the attributes of different formats of menu labeling between Korea and the U.S. Americans rated the most complicated type of menu labeling as the highest perception for the attributes, and showed a higher use intention of menu labeling than Koreans. This study contributes to academia and industry for practicing menu labeling in different countries using different formats.

A development of traffic information detection using camera

  • 김양주;한민홍
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1995.04a
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    • pp.316-323
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    • 1995
  • This paper presents an image processing technique to get traffic information such as vehicle volume, velocity, and occupancy for measuring the traffic congestion rate. To obtain these information, two horizontal lines are previously set on the screen. A moving vehicle is detected using the gray level difference on each line, and also template matching method at night. Threshold values are determined by sampling pavement grey level, and updated dynamically to cope with the change of ambient light conditions. These technique is successfully used to calculate vehicle volume, occupancy, and velocity. This study can be applied to traffic signal control system for minimizing traffic congestion in urban areas.

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Link Quality Estimation in Static Wireless Networks with High Traffic Load

  • Tran, Anh Tai;Mai, Dinh Duong;Kim, Myung Kyun
    • Journal of Communications and Networks
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    • v.17 no.4
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    • pp.370-383
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    • 2015
  • Effective link quality estimation is a vital issue for reliable routing in wireless networks. This paper studies the performance of expected transmission count (ETX) under different traffic loads. Although ETX shows good performance under light load, its performance gets significantly worse when the traffic load is high. A broadcast packet storm due to new route discoveries severely affects the link ETX values under high traffic load, which makes it difficult to find a good path. This paper presents the design and implementation of a variation of ETX called high load - ETX (HETX), which reduces the impact of route request broadcast packets to link metric values under high load. We also propose a reliable routing protocol using link quality metrics, which is called link quality distance vector (LQDV). We conducted the evaluation of the performance of three metrics - HETX, ETX and minimum hop-count. The simulation results show that HETX improves the average route throughput by up to 25% over ETX under high traffic load. Minimum hop-count has poor performance compared with both HETX and ETX at all of the different traffic loads. Under light load, HETX and ETX show the same performance.

Forcasting of Real Time Traffic Situation (실시간 교통상황 예보)

  • 홍유식;진현수;최명복;박종국
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2000.05a
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    • pp.292-297
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    • 2000
  • This paper proposes a new concept of coordinating green time which controls 10 traffic intersection systems. For instance, if we have a baseball game at 8 pm today, traffic volume toward the baseball game at 8 pm today, traffic volume toward the baseball game will be increased 1 hour or 1 hour and 30 minutes before the baseball game. At that time we can not predict optimal green time Even though there have smart elctro-sensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dosen't consider coordinating green time.

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The Complex Interrelationship of Work-Related Factors Underlying Risky Driving Behavior of Food Delivery Riders in Athens, Greece

  • Papakostopoulos, Vassilis;Nathanael, Dimitris
    • Safety and Health at Work
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    • v.12 no.2
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    • pp.147-153
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    • 2021
  • Background: In this study, the association of work and demographic characteristics with different traffic offenses committed by food delivery riders in Greece was examined. Previous research has identified various factors related to risky driving however, there is a need for exploring the complex interrelationship of work-related factors underlying risky driving behavior. Materials and Methods: A 2-items uestionnaire was used exploring delivery riders demographic characteristics, terms of employment, issues of concern during work and type of traffic offenses committed. In total, uestionnaires were analyzed using logistic regression in order to identify characteristics independently associated with serious traffic offenses, namely, red-light running and helmet non-use. Results: The analysis showed that: (i) typical health and safety measures had no effect on serious traffic offenses, (ii) young age was related to both offenses however (iii) different sets of work conditions were associated with reports of red-light running (i.e. low work experience, use of personal vehicle for work, and payment by hour) and helmet non-use respectively (i.e. intense work pace, high tip income per day and low concern about vehicle condition). Conclusion: The above findings provide evidence that serious traffic offenses are manifestations of underlying conflict experienced by the riders between safety and various performance criteria. Each one of the two offenses is related to different rider profiles aiming to satisfy different goals, namely, those mainly trying to maximize profit non-helmet users and those, mostly inexperienced ones, trying to cope with work pressure red light runners. Potential regulatory measures to alleviate risky practices are discussed.