• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.83-93
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• 2020

With the rapid increase of vehicles, various traffic problems, e.g., car crashes, traffic congestions, etc, frequently occur in the road environment of the urban area. To overcome such traffic problems, intelligent transportation systems have been developed with a traffic flow analysis. The traffic flow, which can be estimated by the vehicle counting scheme, plays an important role to manage and control the urban traffic. In this paper, we propose a novel vehicle counting method based on predicted centers of each lane. Specifically, the centers of each lane are detected by using the accumulated movement of vehicles and its filtered responses. The number of vehicles, which pass through extracted centers, is counted by checking the closest trajectories of the corresponding vehicles. Various experimental results on road CCTV videos demonstrate that the proposed method is effective for vehicle counting.

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.53-63
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• 2003

The Origin-Destination(OD) matrix is very important in describing transport movements in a region. The OD matrix can be estimated using traffic counts on links in the transport network and other available information. This information on the travel is often contained in a target OD matrix and traffic counts in links. To estimate an OD matrix from traffic counts, they are the major input data which obviously affects the accuracy of the OD matrix estimated, Generally, the quality of an estimated OD matrix depends much on the reliability of the input data, and the number and locations of traffic counting points in the network. Any Process regarding the traffic counts such as the amount and their location has to be carefully studied. The objective of this study is to select of the optimal location of traffic counting points for the OD matrix estimation. The model was tested in nationwide network. The network consists of 224 zones, 3,125 nodes and 6,725 links except to inner city road links. The OD matrix applied for selection of traffic counting points was estimated to 3-constrained entropy maximizing model. The results of this study follow that : the selected alternative to the best optimal counting points of six alternatives is the alternative using common links of OD matrix and vehicle-km and traffic density(13.0% of 6,725 links), however the worst alternative is alternative of all available traffic counting points(44.9% of 6,725 links) in the network. Finally, it should be concluded that the accuracy of reproduced OD matrix using traffic counts related much to the number of traffic counting points and locations.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.48-57
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• 2020

Recently, with the rapid development of image recognition technology, the demand for object analysis in road CCTV videos is increasing. In this paper, we propose a method that can adaptively find the counting line for traffic analysis in road CCTV videos. First, vehicles on the road are detected, and the corresponding positions of the detected vehicles are modeled as the two-dimensional pointwise Gaussian map. The paths of vehicles are estimated by accumulating pointwise Gaussian maps on successive video frames. Then, we apply clustering and linear regression to the accumulated Gaussian map to find the principal direction of the road, which is highly relevant to the counting line. Experimental results show that the proposed method for detecting the counting line is effective in various situations.

• Journal of Korean Society of Transportation
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• v.19 no.5
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• pp.49-59
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• 2001

In the transportation planning process, origin and destination(O-D) trip matrix is one of the most important elements. There have been developments and applications of the methodology to adjust old matrices using link traffic counts. Commonly, the accuracy of an adjusted O-D matrix depends very much on the reliability of the input data such as the numbers and locations of traffic counting points in the road network. In the real application of the methodology, decisions on the numbers and locations of traffic counting points are one of the difficult problems, because usually as networks become bigger, the numbers of traffic counting points are required more. Therefore, this paper investigates these issues as an experiment using a nationwide network in Korea. We have compared and contrasted the set of link flows assigned by the old and the adjusted O-D matrices with the set of observed link flows. It has been analyzed by increasing the number of the traffic counting points on the experimental road network. As a result of these analyses, we can see an optimal set of the number of counting links through statistical analysis, which are approximately ten percentages of the total link numbers. In addition, the results show that the discrepancies between the old and the adjusted matrices in terms of the trip length frequency distributions and the assigned and the counted link flows are minimized using the optimal set of the counted links.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.38-47
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• 2008

A vehicle counting system at intersection is designed and implemented using analyzing a video stream from a camera. To separate foreground image from background, we compare three different methods, among which Li's method is chosen. Blobs are extracted from the foreground image using connected component analysis and the blobs are tracked by a blob tracker, frame by frame. The primary tracker use only the size and location of blob in foreground image. If there is a collision between blobs, the mean-shift tracking algorithm based on color distribution of blob is used. The proposed system is tested using real video data at intersection. If some huristics is applied, the system shows a good detection rate and a low error rate.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.385-392
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• 2016

Market analysis for a business plain is required for the success in the modern world. Most important part in this analysis is pedestrian traffic counting. A traditional way for this is counting it in person. However, it causes high labor costs and mistakes. This paper proposes an automatic algorithm to measure the pedestrian traffic count using images with webcam. The proposed algorithm is composed of two parts: pedestrian area detection and movement tracking. In pedestrian area detection, moving blobs are extracted and pedestrian areas are detected using HoG features and Adaboost algorithm. In movement tracking, multi-level matching and false positive removal are applied to track pedestrian areas and count the pedestrian traffic. Multi-level matching is composed of 3 steps: (1) the similarity calculation between HoG area, (2) the similarity calculation of the estimated position with Kalman filtering, and (3) the similarity calculation of moving blobs in the pedestrian area detection. False positive removal is to remove invalid pedestrian area. To analyze the performance of the proposed algorithm, a comparison is performed with the previous human area detection and tracking algorithm. The proposed algorithm achieves 83.6% accuracy in the pedestrian traffic counting, which is better than the previous algorithm over 11%.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.1
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• pp.15-22
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• 2015

Estimating the number of distinct values is really well-known problems in network data measurement and many effective algorithms are suggested. Recent works have built upon technique called Linear Counting to solve the estimation problem for massive sets or spreaders in small memory. Sampling is used to reduce the measurement data, and it is assumed that sampling gives bad effect on the accuracy. In this paper, however, we show that the sampling on multi-set estimation sometimes gives better results for CSE with sampling than for MCSE that examines all the packets without sampling in terms of accuracy and estimation range. To prove this, we presented mathematical analysis, conducted experiment with real data, and compared the results of CSE, MCSE, and CSES.

• Journal of the Society of Disaster Information
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• v.11 no.4
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• pp.634-638
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• 2015

Four major social safety indexes including industrial accident, traffic accident, fire, and violent crime were selected, and transition of those values by time series data analysis since 2003 was presented. Comparing with the 2003 figure, the index of industrial accident was reduced by 27.8%, which was the most improved safety index. The indicators describing the traffic accident and violent crime rate were reduced by approximately 12%. However, the fire safety index showed an increase of 40% compared with the base year because national fire classification system was changed so that minor fire is also included in the counting since 2006.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• International Journal of Highway Engineering
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• v.9 no.4
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• pp.83-92
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• 2007

In 2004, the number of traffic crashes and deaths in Korea are 220,755 and 6,563, respectively. Korea Road Traffic Safety Authority reported that the number of traffic accidents occupies over 25% out of total accidents, and found that traffic crash probability is extremely high at intersections since intersections have various traffic conflict points. A Safety study using Traffic Conflict Technique is much more useful than a study using reported traffic accident data. Existing traffic conflict research hardly considered conflict severity occurring at intersections. So, the study developed new criteria considering conflict severity. Analytic methods precisely detecting crashing points using field surveying data, and applied an application of our new criteria. Opposing left-turn conflict criteria was devided by three groups(high severe conflict, middle severe conflict, and less severe conflict) based on conflict boundary by means of a standard vehicle length. After analyzing field surveying data(3hours), we found totally 41 opposing left-turn conflicts. 3 cases are high severe conflict, and another 10 cases are middle severe conflicts, and the other cases are less severe. Studies related in conflict severity are considerably important to evaluate intersection's detailed safety index, and existing studies(purely conflict counting does not consider severity) have a limitation to clearly determine the level of safety of intersections for an application.