• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.253-263
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• 2021

Traffic data collected using advanced equipment are highly valuable for traffic planning and efficient road operation. However, there is a problem regarding the reliability of the analysis results due to equipment defects, errors in the data aggregation process, and missing data. Unlike other detectors installed for each vehicle lane, radar detectors can yield different error types because they detect all traffic volume in multilane two-way roads via a single installation external to the roadway. For the traffic data of a radar detector to be representative of reliable data, the error factors of the radar detector must be analyzed. This study presents a field survey of variables that may cause errors in traffic volume collection by targeting the points where radar detectors are installed. Video traffic data are used to determine the errors in traffic measured by a radar detector. This study establishes three types of radar detector traffic errors, i.e., artificial, mechanical, and complex errors. Among these types, it is difficult to determine the cause of the errors due to several complex factors. To solve this problem, this study developed a radar detector traffic volume error analysis model using a multiple linear regression model. The results indicate that the characteristics of the detector, road facilities, geometry, and other traffic environment factors affect errors in traffic volume detection.

• Journal of IKEEE
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• v.23 no.4
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• pp.1257-1264
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• 2019

A 3-GSymbol/s/lane transceiver, which supports the mobile industry processor interface (MIPI) C-physical layer (PHY) specification version 1.1, is proposed. It performs channel mismatch correction to improve the signal integrity that is deteriorated by using three-level signals over three channels. The proposed channel mismatch correction is performed by detecting channel mismatches in the receiver and adjusting the delay times of the transmission data in the transmitter according to the detection result. The channel mismatch detection in the receiver is performed by comparing the phases of the received signals with respect to the pre-determined data pattern transmitted from the transmitter. The proposed MIPI C-PHY receiver is designed using a 65 nm complementary metal-oxide-semiconductor (CMOS) process with 1.2 V supply voltage. The area and power consumption of each transceiver lane are 0.136 ㎟ and 17.4 mW/GSymbol/s, respectively. The proposed channel mismatch correction reduces the time jitter of 88.6 ps caused by the channel mismatch to 34.9 ps.

• International Journal of Highway Engineering
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• v.18 no.5
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• pp.117-125
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• 2016

PURPOSES : The density in uninterrupted traffic flow facilities plays an important role in representing the current status of traffic flow. For example, the density is used for the primary measures of effectiveness in the capacity analysis for freeway facilities. Therefore, the estimation of density has been a long and tough task for traffic engineers for a long time. This study was initiated to evaluate the performance of density values that were estimated using VDS data and two traditional methods, including a method using traffic flow theory and another method using occupancy by comparing the density values estimated using vehicular trajectory data generated from a radar detector. METHODS : In this study, a radar detector which can generate very accurate vehicular trajectory within the range of 250 m on the Joongbu expressway near to Dongseoul tollgate, where two VDS were already installed. The first task was to estimate densities using different data and methods. Thus, the density values were estimated using two traditional methods and the VDS data on the Joongbu expressway. The density values were compared with those estimated using the vehicular trajectory data in order to evaluate the quality of density estimation. Then, the relationship between the space mean speed and density were drawn using two sets of densities and speeds based on the VDS data and one set of those using the radar detector data. CONCLUSIONS : As a result, the three sets of density showed minor differences when the density values were under 20 vehicles per km per lane. However, as the density values become greater than 20 vehicles per km per lane, the three methods showed a significant difference among on another. The density using the vehicular trajectory data showed the lowest values in general. Based on the in-depth study, it was found out that the space mean speed plays a critical role in the calculation of density. The speed estimated from the VDS data was higher than that from the radar detector. In order to validate the difference in the speed data, the traffic flow models using the relationships between the space mean speed and the density were carefully examined in this study. Conclusively, the traffic flow models generated using the radar data seems to be more realistic.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1350-1353
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• 2013

자동차의 안전성 향상에 대한 연구는 오랜 기간 다양한 분야에서 진행되고 있다. 이 시스템은 단일 카메라를 이용하여 차선을 감지함으로써 차선 침범을 방지한다. 시스템은 파티클 필터를 이용해 도로 모델 파라미터를 조정하고 두 개의 detector가 도로 모델의 일치도를 계산한다. Detector는 차선의 모양과 색이라는 대표적인 특징을 이용한다. 파티클 필터를 전 프레임에서 사용한 모델 파라미터를 이용하여 신속한 처리를 한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.461-467
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• 2006

To detect individual vehicular speed, double loop detection technique has been widely used. This paper investigates four methodologies to measure individual speed using only a single loop sensor in a traveling lane. Two methods developed earlier include estimating the speed by means of (Case 1) the slop of inductance wave form generated by the sensor and (Case 2) the average vehicle lengths. Two other methods are newly developed through this study, which are estimations by measuring (Case 3) the mean of wheelbases using the sensor installed traversal to the traveling lane and (Case 4) the mean of wheel tracks by the sensor installed diagonally to the traveling lane. These four methodologies were field-tested and their accuracy of speed output was compared statistically. This study used Equality Coefficient and Mean Absolute Percentage Error for the assessment. It was found that the method (Case 1) was best accurate, followed by method (Case 4), (Case 2), and (Case 3).

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.81-94
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• 2016

The Korean real-time traffic responsive control systems, Cycle Offset Split Model of Seoul (COSMOS), employs a single theoretical model to estimate the degree-of-saturation (DS) on approaches. However, the deployment of the system has been accomplished without practical consideration of its field performance. This paper delivers a diagnosis study performed to find the relationships yet known on the DS values against the operational conditions unproved in theory but ordinarily observed in field practice. Based on the analysis of the historical log data (476,505 cycles) obtained from the COSMOS server, it was found; (1) full coverage of lane detections should perform better than the sample coverage of detection in ordinary conditions, (2) the sample coverage of detection perform better than the other case with an exclusive bus lane, (3) detection in which a shared lane is involved provide poor estimation of DS, (4) poor DS estimation when a detection lane is adjacent to a shared lane, and (5) the DS values obtained during a day can hardly be stable all time. The findings suggest traffic engineers a progressive direction to move forward for the next real-time traffic control systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2355-2373
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• 2011

Vision-based vehicle detector systems are becoming increasingly important in ITS applications. Real-time operation, robustness, precision, accurate estimation of traffic parameters, and ease of setup are important features to be considered in developing such systems. Further, accurate vehicle detection is difficult in varied complex traffic environments. These environments include changes in weather as well as challenging traffic conditions, such as shadow effects and jams. To meet real-time requirements, the proposed system first applies a color background to extract moving objects, which are then tracked by considering their relative distances and directions. To achieve robustness and precision, the color background is regularly updated by the proposed algorithm to overcome luminance variations. This paper also proposes a scheme of feedback compensation to resolve background convergence errors, which occur when vehicles temporarily park on the roadside while the background image is being converged. Next, vehicle occlusion is resolved using the proposed prior split approach and through reasoning for rule-based tracking. This approach can automatically detect straight lanes. Following this step, trajectories are applied to derive traffic parameters; finally, to facilitate easy setup, we propose a means to automate the setting of the system parameters. Experimental results show that the system can operate well under various complex traffic conditions in real time.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.49-55
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• 2015

According to the statistics of traffic accidents over recent 5 years, traffic accidents during the night times happened more than the day times. There are various causes to occur traffic accidents and the one of the major causes is inappropriate or missing street lights that make driver's sight confused and causes the traffic accidents. In this paper, with smartphones, we designed and implemented a lane luminance measurement application which stores the information of driver's location, driving, and lane luminance into database in real time to figure out the inappropriate street light facilities and the area that does not have any street lights. This application is implemented under Native C/C++ environment using android NDK and it improves the operation speed than code written in Java or other languages. To measure the luminance of road, the input image with RGB color space is converted to image with YCbCr color space and Y value returns the luminance of road. The application detects the road lane and calculates the road lane luminance into the database sever. Also this application receives the road video image using smart phone's camera and improves the computational cost by allocating the ROI(Region of interest) of input images. The ROI of image is converted to Grayscale image and then applied the canny edge detector to extract the outline of lanes. After that, we applied hough line transform method to achieve the candidated lane group. The both sides of lane is selected by lane detection algorithm that utilizes the gradient of candidated lanes. When the both lanes of road are detected, we set up a triangle area with a height 20 pixels down from intersection of lanes and the luminance of road is estimated from this triangle area. Y value is calculated from the extracted each R, G, B value of pixels in the triangle. The average Y value of pixels is ranged between from 0 to 100 value to inform a luminance of road and each pixel values are represented with color between black and green. We store car location using smartphone's GPS sensor into the database server after analyzing the road lane video image with luminance of road about 60 meters ahead by wireless communication every 10 minutes. We expect that those collected road luminance information can warn drivers about safe driving or effectively improve the renovation plans of road luminance management.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.72-74
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• 2011

최근 IT 기술이 융합된 지능형 자동차 기술에 대한 관심이 높아짐에 따라 이에 대한 연구가 활발히 진행되고 있다. 차선 검출은 지능형 자동차의 주요 과제인 첨단 안전자동차 기술의 핵심적인 부분으로 국내외에서 다양한 방법들에 대한 연구가 진행되었다. 차량의 안전을 향상시키기 위해서는 충분한 제동거리 확보가 가능한 거리까지 정확하고 빠른 차선 검출이 이루어져야 한다. 기존의 경계선 검출기법들은 조명 변화에 따라 그 성능의 변화가 크게 발생하였다. 이는 차선과 도로의 사이의 값의 차이가 조명 조건에 따라 변하기 때문이다. 따라서 본 논문에서는 영상 분석을 통하여 경계선을 판단하는 값을 조절함으로써 환경에 적응적인 경계선 추출 방법을 제안한다. 차량 주행 영상에서 제안한 방법과 기존의 경계선 검출 기법을 적용하여 성능을 비교한다.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.27-40
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• 2006

The traffic information is provided, which based on the volume of traffic, speed, occupancy collected through the currently operating Vehicle Detector System(VDS). In addition to the trend in utilization fold of traffic information is increasing gradually with the applied various fields and users. Missing data in Vehicle detector data means series of data transmitted to controller without specific property. The missing data does not have a data property, so excluded at the whole data Process Hence, increasing ratio of missing data in VDS data inflicts unreliable representation of actual traffic situation. This study presented the imputation process due out which applied the methodologies that utilized adjacent stations reference and historical data utilize about missing data. Applied imputation process methodologies to VDS data or SeoHaeAn/Kyongbu Expressway, currently operation VDS, after processes at missing data ratio of an option. Imputation process held presented to per lane-30seconds-period, and morning/afternoon/daily time scope ranges classified, and analyzed an error of imputed data preparing for actual data. The analysis results, an low error occurred relatively in the results of the imputation process way that utilized a historical data compare with adjacent stations reference methods.