• Journal of Korean Society of Transportation
• /
• v.25 no.5
• /
• pp.33-42
• /
• 2007

The current left-turn split model adopted in COSMOS has an inherent limitation when a loop detector in the left-turn lanes was disconnected for a period of time. In this instance, the current model always allocated minimum green time to the left-turn phase, thus optimal split and efficient signal operation for the intersection was not guaranteed. In this paper, four mathmatical models using detector information of the intersection and four empirical models using historical profiles were developed and investigated for different traffic conditions to improve the operational efficiency of the intersection. From the model evaluation test, the empirical model using a four-week historical profile produced the least error among the eight models investigated. NETSIM simulation test results also showed that the proposed model could give significantly reduced delay time as compared to the current model. From these results, the operational efficency of the signalized intersections under the real-time control can be greatly improved by using the model proposed in case of the left-turn detector failure.

• Proceedings of the KIEE Conference
• /
• 2003.04a
• /
• pp.415-417
• /
• 2003

TOA(Time of Arrival) 및 TDOA(Time Difference of Arrival)경우 무선국의 시간동기화를 위해서 고도의 기술을 요구하고 있으며, 시간동기오차에 따른 위치검지의 정밀도가 낮아지는 문제가 있어 이를 극복하기 위하여 위상차를 이용한 새로운 열차검지기법의 제안에 따른 구현을 위하여 무선장치 설계에 대하여 기술하고자 한다. 본 시스템은 전파의 전달 속도($\lambda$)를 응용하여 기준 주파수인 1.5MHz를 송신 시스템과 수신 시스템의 기준 주파수와 비교하여 그 위상의 차이를 비교하여 지연된 시간을 구한 후 이를 거리로 환산하는 시스템으로서 무선장치와 S/W로 구분하여 구현 설계하였다.

• Journal of Korean Society of Transportation
• /
• v.21 no.5
• /
• pp.83-95
• /
• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.

• Journal of KIISE:Software and Applications
• /
• v.33 no.5
• /
• pp.489-498
• /
• 2006

In this paper, we suggested a vehicle classification algorithm using pattern recognition method. At present, Inductive Loop Detector is rarely used for vehicle classification because of its low accuracy. To improve the accuracy, we suggest a new algorithm for Loop Detector using neural networks. In the developed algorithm, the inputs to the neural networks are the variation rate of frequency and occupancy-time. The output is classified vehicles. The developed algorithm was assessed at test sites and the recognition rate was 91.3percent. The results verified that the proposed algorithm improves the vehicle classification accuracy compared to the conventional method based on Loop Detector.

• Proceedings of the KOR-KST Conference
• /
• 2007.05a
• /
• pp.300-309
• /
• 2007

본 연구는 신호교차로에서의 딜레마 죤 범위를 산정하고 신호위반 단속의 허용 범위를 분석하는데 있다. 각 교차로 접근로별 조사 자료를 토대로 딜레마 죤의 범위를 산정하였고, 이를 토대로 신호위반 단속에 있어 딜레마 죤의 영향을 최소화 할 수 있는 방안을 제시하고자 하였다. 기존의 딜레마 죤에 관한 연구에서는 인지-반응 시간과 황색신호시간을 초기값으로 적용하였으나 본 연구에서는 해당 교차로의 조사치를 적용하였다. 조사 방법으로 속도조사는 스피드 건을 이용하여 각각의 대상 교차로별 접근로에서 접근 속도 및 통과 속도를 조사하였으며, 운전자의 인지-반응 시간 및 황색신호시간에 교차로를 통과하는 차량조사는 비디오 촬영을 통하여 조사하였다. 이러한 조사된 자료를 토대로 신호위반 단속기준에 맞추어 딜레마 죤에 관하여 분석하였다. 본 연구에서 딜레마 죤은 최소정지거리($d_0$)가 최대통과거리($d_c$)보다 클 때 존재하는 것으로 보았으며, 그 차이만큼의 딜레마 죤이 발생하는 것으로 정의하였다. 이에 신호위반 단속을 함에 있어딜레마 죤의 영향을 최소화 할 수 있는 방안 등을 제시하였다. 그러나 각각의 방안을 개별적으로 적용시킬 경우 문제점이 발생하였다. 이러한 문제점을 해결하기 위해서 자기감응식 루프검지기의 위치를 재조정함에 있어 하나의 루프 검지기를 정지선 이후에 존재함과 동시에 황색신호시간을 재조정하거나, 자기감응식 루프검지기의 작동시간을 재조정하는 방안을 제시하고자 하였다. 본 연구에서는 3개의 교차로를 비교대상으로 선정하여 각각의 교통환경에 따른 접근로별 딜레마죤의 범위를 최소화하기 위한 대안을 제시하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.6
• /
• pp.111-120
• /
• 2018

This study focuses on utilizing drones for performance evaluation of ITS detectors and analyzing economic feasibility when performance evaluation is conducted by the traffic management center's own personnel using drones. The study sites were selected from DSRC, video detector, and radar detector locations and drone filming was conducted to obtain travel speed, queue length, and delay time and compare with the detector data. It was shown that drones can be very effectively used to evaluate performance of major ITS detectors such as DSRC and video detectors. In addition, it was analyzed that a drone operated by the traffic management center's own personnel provides very economic solution for ITS detector performance evaluation when compared to consignment by external agencies.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.3D
• /
• pp.307-315
• /
• 2008

The aim of this study is to develop travel time estimation model by using Self-Organized Neural network(in brief, SON) algorithm. Travel time data based on vehicles equipped with GPS and number-plate matching collected from National road number 3 (between Jangji-IC and Gonjiam-IC), which is pilot section of National Highway Traffic Management System were employed. We found that the accuracies of travel time are related to location of detector, the length of road section and land-use properties. In this paper, we try to develop travel time estimation using SON to remedy defects of existing neural network method, which could not additional learning and efficient structure modification. Furthermore, we knew that the estimation accuracy of travel time is superior to optimum located detectors than based on existing located detectors. We can expect the results of this study will make use of location allocation of detectors in highway.

• Proceedings of the KSR Conference
• /
• 2005.05a
• /
• pp.1159-1164
• /
• 2005

현재 철도청이 운용중인 열차운행관리 시스템(Railway Traffic Management System, RTMS)은 서울, 대전, 부산, 순천 그리고 영주 등으로 총 5개 지역본부로 분산되어 있어 업무의 중복을 줄이고, 자동화(Automation)된 열차집중제어장치(Central Traffic Control, CTC)를 구축하기 위해 지역본부를 대전으로 통합하는 프로젝트를 진행중이다. 본 논문은 철도청 사령실 통합 신호설비 구축 프로젝트에 의거하여 열차 경합을 검지 또는 예측하고 운영자에게 최소의 시간 내에 최적의 해소 대책을 제시함을 목적으로 하는 열차 경합 검지 시스템을 구현하는 과정에서 작성되었다. 여기에서는 열차 경합 검지에 대한 개요와 검지 가능한 경합 종류에 대해 기술하고, 실제 구현된 알고리즘의 기본적인 내용, 프로세스의 구성도 및 시뮬레이션 결과를 설명하려고 한다.

• Journal of Korean Society of Transportation
• /
• v.23 no.4 s.82
• /
• pp.17-28
• /
• 2005

About 91.1% of Railway-Highway Crossings (RHC) in Korea use a Constant Distance Warning System(CDWS), while about 8.9% use a Constant Warning Time System(CWTS). The CDWS does not recognize speed differences of approaching trains and provides only waiting times to vehicles and pedestrians based on the highest speed of approaching trains. Under the CDWS, therefore, low speed trains provide unnecessary waiting times at crossings which often generates complains to vehicle drivers and pedestrians and may cause wrong decisions to pass the crossings. The objective of this research is to improve the safety of the RHC by developing accurate a CWTS. In this research a train speed and location detection system was developed with ultra sonic detectors. Locations of the detectors was decided based on the highest speed and the minimum warning time of Saemaul of 160 km/h. To validate the algorithms of the newly developed systems the lab tests were conducted. The results show that the train detection system provides accurate locations of trains and the maximum error between real speeds of trains and those of the system was 0.07m/s.

• Journal of Korean Society of Transportation
• /
• v.17 no.4
• /
• pp.111-124
• /
• 1999

The Purpose of this thesis is to develop a vehicle classification algorithm using single Magnetometer detector during presence time of vehicle detection and is to examine a held application from field test. We collected data using Magnetometer detector on freeway and used digital data to change voltage values according to magnetic flux density in analysis. We collected these datum during the presence time and then obtained characteristics from wave form in these datum. Based on these characteristics, We used the following three methods for this a1gorithm :1. Template Matching Method,2. Neural Network Method using Back-propagation Algorithm 3. Complex Method using changed slope points and mixing method 1, 2. Of course, Before processing of over three methods, These data were processed normalizing by 20, 40 of size in only X axis and moving average by 0, 3, 4, 5 of size. Vehicle classification were Processed in three steps ; 2, 3, 5 types classification. In 2 types vehicle classification, recognition rate is 83% by template matching method.