• Title/Summary/Keyword: 지능형 교통체계

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Accuracy Estimation of Video Image Detector Considering Heteroscedasticity (이분산성을 고려한 영상검지기 정확도 추정)

  • Lee, Cheong-Won;Song, Yeong-Hwa
    • Journal of Korean Society of Transportation
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    • v.25 no.2 s.95
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    • pp.7-15
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    • 2007
  • The accuracy of a Video Image Detector (VID) is gradually reduced due to various environmental and mechanical factors. However, there has been no systematic research about the decrease of VID accuracy. To maintain a proper level of VID accuracy for advanced traffic management, a regular VID calibration process needs to be introduced. However, the calibration cannot be performed frequently because of the cost. In this study, the researchers collected field data for accuracy estimation and inferred an accuracy decreasing function by using regression and considering the heteroscedasticity problem. Using the invented data collection equipment which was used for checking adaptability, some data in the field were collected and analyzed. Although the data were limited, the results are promising. More data need to be investigated in the future and this study will help to maintain the data quality for broad utilization of the data in ITS centers.

Evaluation of Transportation Policy Using Multidimensional Scaling Method (다차원척도법에 의한 교통정책 평가 인지 차이 분석에 관한 연구)

  • Lee, Won Gyu;Jung, Hun Young;Ko, Sang Seon;Yoon, Hang Mook
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.30 no.3D
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    • pp.255-261
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    • 2010
  • The evaluation regarding a transportation policy by an evaluation volition viewpoint there is a difference. Consequently the insurgent analysis which is simple compared to against the evaluation object it was accurate, the analysis which leads the order anger probably is necessary. The research which it sees for the evaluation regarding the transportation policy of the metropolis divided in road being understood, public transportation, parking and pedestrian environment, wide area transportation and transportation information and transportation field whole. And against these field it tried the ALSCAL method and MDPREF method which is a Multidimensional Scale method and it analyzed. The regression analysis result for a dimensional analysis ALSCAL method the case of the transportation policy star improvement degree which it follows in introduction presence of intelligence transportation system and MDPREF method it confronted to the transportation policy star improvement degree which it follows in expansion to construction of specific function appeared with the fact that it is the tendency probably. And the evaluation object and evaluation in the object which will cut the positioning one result was each divided in 4 group. And two methods all it was visible a similar tendency. The ALSCAL method currently transportation system construction degree condition in base and, the MDPREF method currently improvement degree of the transportation policy which it follows in traffic system construction appeared with the fact that it is desirable to establish a hereafter traffic policy in base.

Location reference technique of ITS Space Database supporting interoperability (상호운용성을 지원하는 ITS 공간 데이터베이스의 위치참조 기법)

  • Kim, Suk-Hee;Choi, Kee-Choo;Jang, Jeong-Ah
    • Journal of Korean Society for Geospatial Information Science
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    • v.12 no.1 s.28
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    • pp.45-53
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    • 2004
  • The purpose of this paper is to study a scheme to ITS service which enables the data (spatial, non-spatial and image) sharing among heterogeneous system (various environment) with employing the concept of object orientedness and to show Location Reference Technique of ITS Space DB for interoperability. Data warehouse service, query object service, interface object service, and naming object service have been identified for this. In addition, a metadata management object service and persistent object service based system framework has been devised. The proposed skeletal framework would be expected to be functioning well for ITS data sharing environment and for the interoperability support.

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Design and Implementation for Incident Detection Algorithm in Intelligent Transportation System (ITS 유고검지 시스템 설계 및 구현)

  • 전성주;백청호;최진탁
    • Journal of the Korea Computer Industry Society
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    • v.5 no.3
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    • pp.337-344
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    • 2004
  • ITS(Intelligent transportation system) provides users with realtime traffic information based on technologies such as advanced information & telecommunication, electronic control and transportation engineering. To operate efficient ITS, it is necessary to quickly identify and take actions for incidents(accidents, broken vehicles, public functions, traffic control, etc.). However, there have been few reliable incident detection algorithms developed so far. The algorithm presented in this study greatly resolved the problems in the existing incident detection algorithms, which determine incidents according to the input of constant values, by defining ranges based on the concept of pseudo level of service. With this improvement, operators can determine the incident detection parameters more accurately.

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LOS Analysis Frame for COSMOS at Isolated Intersections (실시간신호제어 독립교차로 서비스수준 분석 방법론)

  • Kim, Jin-Tae;Kim, Kang-Huy;Lee, Don-Ju
    • Journal of Korean Society of Transportation
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    • v.26 no.4
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    • pp.161-172
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    • 2008
  • The level of service (LOS) evaluation method of the Highway Capacity Manual (HCM) is limited to intersections with pre-timed signal operation, while advanced real-time traffic control systems have been expending in the field. This paper proposes a preliminary framework for LOS analysis at isolated intersections controlled by COSMOS, the real-time traffic control systems robustly utilizing the degree of saturation as basic inputs. The proposed LOS evaluation framework was devised with a pilot model developed to estimate the average cycle length and green times of COSMOS. The validation test showed that the proposed framework was able to accurately project the LOS, which was separately evaluated based upon field data.

Rate of Probe Vehicles for the Collection of Traffic Information on Expressways (고속도로 교통정보 취득을 위한 프루브 차량 비율 산정 연구)

  • Kim, Jiwon;Jeong, Harim;Kang, Sungkwan;Yun, Ilsoo
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.18 no.6
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    • pp.262-274
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    • 2019
  • The purpose of this study is to estimate the minimum proportion of probe vehicles for obtaining expressway traffic information using VISSIM, a micro traffic simulation model, between Yongin IC and Yangji IC on Yeongdong Expressway. 7,200 scenarios were created for the experiment, and 40 scenarios were adopted using the Latin hypercube sampling method because it was difficult to perform all the scenarios through experiments. The reliability of the experiment was improved by adding a situation when the general situation and the accident situation exist. In the experiments, the average travel time of probe vehicles at different market penetration rates were compared with the average travel time of the entire vehicles. As a result, the minimum market penetration rate of probe vehicles for obtaining expressway traffic information was found to be 45%. In addition, it is estimated that 25% market penetration rate of probe vehicle can meet 70% of traffic situations in accident scenario.

Traffic Control using Q-Learning Algorithm (Q 학습을 이용한 교통 제어 시스템)

  • Zheng, Zhang;Seung, Ji-Hoon;Kim, Tae-Yeong;Chong, Kil-To
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.12 no.11
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    • pp.5135-5142
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    • 2011
  • A flexible mechanism is proposed in this paper to improve the dynamic response performance of a traffic flow control system in an urban area. The roads, vehicles, and traffic control systems are all modeled as intelligent systems, wherein a wireless communication network is used as the medium of communication between the vehicles and the roads. The necessary sensor networks are installed in the roads and on the roadside upon which reinforcement learning is adopted as the core algorithm for this mechanism. A traffic policy can be planned online according to the updated situations on the roads, based on all the information from the vehicles and the roads. This improves the flexibility of traffic flow and offers a much more efficient use of the roads over a traditional traffic control system. The optimum intersection signals can be learned automatically online. An intersection control system is studied as an example of the mechanism using Q-learning based algorithm, and simulation results showed that the proposed mechanism can improve the traffic efficiency and the waiting time at the signal light by more than 30% in various conditions compare to the traditional signaling system.

A Study on Improving Performance of the Vehicular WAVE Antenna System using the EBG structure for ITS wireless communications (동향분석ITS 무선통신을 위한 EBG 구조를 적용한 자동차용 WAVE 안테나 시스템 성능향상연구)

  • Yeon, KyuBong;Lee, DuHo;Hwang, JinKyu;Yang, TaeHoon
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.16 no.1
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    • pp.176-185
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    • 2017
  • This paper describes a design of the WAVE antenna system in V2X wireless communication systems for Intelligent Transport Systems. The WAVE standard protocols defined 5.825~5.9GHz frequency range for wireless communications with V2X. In a recent, A study of WAVE communication system it has been studied mainly the base station and the OBU technology in order to improve the communication performance of the communication distance. In this paper, the proposed vehicular WAVE antenna using the EBG structure is to improve performance. The proposed WAVE antenna with EBG shows improvement of return loss and radiation beam pattern. The performance of WAVE communication systems for intelligent transport systems is dependent on the performance of antenna. The proposed vehicular antenna for WAVE communication systems shows improvement of return loss for performance.

Development of a Mid-/Long-term Prediction Algorithm for Traffic Speed Under Foggy Weather Conditions (안개시 도시고속도로 통행속도 중장기 예측 알고리즘 개발)

  • JEONG, Eunbi;OH, Cheol;KIM, Youngho
    • Journal of Korean Society of Transportation
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    • v.33 no.3
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    • pp.256-267
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    • 2015
  • The intelligent transportation systems allow us to have valuable opportunities for collecting wide-area coverage traffic data. The significant efforts have been made in many countries to provide the reliable traffic conditions information such as travel time. This study analyzes the impacts of the fog weather conditions on the traffic stream. Also, a strategy for predicting the long-term traffic speeds is developed under foggy weather conditions. The results show that the average of speed reductions are 2.92kph and 5.36kph under the slight and heavy fog respectively. The best prediction performance is achieved when the previous 45 pattern cases data is used, and the 14.11% of mean absolute percentage error(MAPE) is obtained. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

Application of Deep Learning Method for Real-Time Traffic Analysis using UAV (UAV를 활용한 실시간 교통량 분석을 위한 딥러닝 기법의 적용)

  • Park, Honglyun;Byun, Sunghoon;Lee, Hansung
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.38 no.4
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    • pp.353-361
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    • 2020
  • Due to the rapid urbanization, various traffic problems such as traffic jams during commute and regular traffic jams are occurring. In order to solve these traffic problems, it is necessary to quickly and accurately estimate and analyze traffic volume. ITS (Intelligent Transportation System) is a system that performs optimal traffic management by utilizing the latest ICT (Information and Communications Technology) technologies, and research has been conducted to analyze fast and accurate traffic volume through various techniques. In this study, we proposed a deep learning-based vehicle detection method using UAV (Unmanned Aerial Vehicle) video for real-time traffic analysis with high accuracy. The UAV was used to photograph orthogonal videos necessary for training and verification at intersections where various vehicles pass and trained vehicles by classifying them into sedan, truck, and bus. The experiment on UAV dataset was carried out using YOLOv3 (You Only Look Once V3), a deep learning-based object detection technique, and the experiments achieved the overall object detection rate of 90.21%, precision of 95.10% and the recall of 85.79%.