• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.129-140
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• 2004

In this paper, we conduct the research about optimal aggregation interval of travel time data on interrupted traffic flow and verify the reliability of AVI collected data by using car plate matching method in RTMS for systematic collection and analysis of link travel time data on interrupted traffic flow rural arterial. We perform Kolmosorov-Smirnov test on AVT collected sample data and on entire population data, and conclude that the sample data does not represent pure random sampling and hence includes sample collection error. We suggest that additional review is necessary to investigate the effectiveness of AVI collected sample data as link representative data. We also develop statistical model by applying two estimation techniques namely point estimation and interval estimation for calculating optimal aggregation interval. We have implemented our model and determine that point estimate is preferable over interval estimate for exactly selecting and deciding optimal aggregation interval. Our final conclusion is that 5-minute aggregation interval is optimal to estimate travel time in RTMS, as is currently being used our investigation is based on AVI data collected from Yang-ji to Yong-in $42^{nd}$ National road.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.514-526
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• 2013

With a long sensing period, the inter-frame spectrum sensing in IEEE 802.22 standard is vulnerable to the effect of the traffic of the primary user (PU). In this article, we address the two degrading factors that affect the inter-frame sensing performance with respect to the random arrival/leaving of the PU traffic. They are the noise-only samples under the random arrival traffic, and the PU-signal-contained samples under the random leaving traffic. We propose the model in which the intra-frame sensing cooperates with the inter-frame one, and the inter-frame sensing uses the time-of-arrival (ToA), and time-of-leave (ToL) detectors to reduce the two degrading factors in the inter-frame sensing time. These ToA and ToL detectors are used to search for the sample which contains either the ToA or ToL of the PU traffic, respectively, which allows the partial cancelation of the unnecessary samples. At the final stage, the remaining samples are input into a primary user detector, which is based on the energy detection scheme, to determine the status of PU traffic in the inter-frame sensing time. The analysis and the simulation results show that the proposed scheme enhances the spectrum-sensing performance compared to the conventional counter-part.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.41-60
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• 2018

Recently, road traffic congestion is becoming a serious urban phenomenon, leading to massive adverse impacts on the ecology and economy. Therefore, solving this problem has drawn public attention throughout the world. One new promising solution is to take full advantage of vehicular ad hoc networks (VANETs). In this study, we propose a new traffic congestion detection and quantification method based on vehicle clustering and fuzzy assessment in VANET environment. To enhance real-time performance, this method collects traffic information by vehicle clustering. The average speed, road density, and average stop delay are selected as the characteristic parameters for traffic state identification. We use a comprehensive fuzzy assessment based on the three indicators to determine the road congestion condition. Simulation results show that the proposed method can precisely reflect the road condition and is more accurate and stable compared to existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3635-3654
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• 2015

With the rapid development of smart devices and mobile Internet, the video application plays an increasingly important role on mobile devices. Understanding user behavior patterns is critical for optimized operation of mobile live streaming systems. On the other hand, volume based billing models on cloud services make it easier for video service providers to scale their services as well as to reduce the waste from oversized service capacities. In this paper, the watching behaviors of a commercial mobile live streaming system are studied in a content-centric manner. Our analysis captures the intrinsic correlation existing between popularity and watching intensity of programs due to the synchronized watching behaviors with program schedule. The watching pattern is further used to estimate traffic volume generated by the program, which is useful on data volume capacity reservation and billing strategy selection in cloud services. The traffic range of programs is estimated based on a naive popularity prediction. In cross validation, the traffic ranges of around 94% of programs are successfully estimated. In high popularity programs (>20000 viewers), the overestimated traffic is less than 15% of real happened traffic when using upper bound to estimate program traffic.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.22-32
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• 2013

Since the percentage of vehicles equipped with Hi-pass, an electronic toll collecting device, has increased rapidly, it is very crucial to determine the optimal location of Hi-pass lanes at a toll plaza in terms of traffic control and operation. In this study, the appropriateness of existing Hi-pass lanes of a toll plaza is evaluated considering its physical geometry and traffic characteristics. A new evaluating criterion called "traffic dispersion rate" is developed in order to measure the level of traffic spreading across the toll booth lanes at a toll plaza. Logistic regression models are constructed to estimate the relationship between the traffic dispersion rate and its affecting variables. The model estimation results show that several variables including Hi-pass lane traffic volume, length of toll plaza, entering/exiting taper lengths, and locations of Hi-pass lanes. The results also suggest that traffic dispersion rate can be increased by adjusting the location of Hi-pass lanes. The study enables us to quantify traffic dispersion rate which can be used to optimize the location and operation of Hi-pass lanes at toll plazas.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.17-29
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• 2008

A volume-delay function(VDF) has been used to describe the relation between traffic volumes and delay experienced by travelers on the roads traveling from origin to destination, which has been usually adopted in traffic assignment. For the purpose of more precise description of traffic pattern, we have to estimate the parameters of VDF in advance. This paper presents a methodology for estimating the parameters, which combined with golden section method. By using the method we have estimated the parameters with real data based on KTDB(2006), and validated them. Compared to the existing values of the parameters, newly estimated values are found to be closer to real world.

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.57-69
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• 2005

The goal of this paper is to develop freeway Origin-Destination (OD) demand estimation model using real-time traffic data collected from Freeway Traffic Management System (FTMS). In existing research, the micro-simulation models had been used to get a link distribution proportion by time process. Because of hi-level problem between the traffic flow model and the optimal OD solution algorithm, it is difficult for the existing models to be loaded at FTMS. The formulation of methodology proposed in this paper includes traffic flow technique to be able to remove the bi-level problem and optimal solution algorithm using a genetic algorithm. The proposed methodology is evaluated by using the real-time data of SOHAEAN freeway, South Korea.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.4
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• pp.1-12
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• 2007

A method for assessing fatigue vulnerability of steel bridge members considering corrosion and truck traffic variation with time is proposed to evaluate the reduction of fatigue strength in steel bridge members. A fatigue limit state function including corrosion and traffic variation effect is established. The interaction between the average corrosion depth and the fatigue strength reduction factor is applied to the limit state function as the reduction term of strength. Three types of truck traffic change is modeled for representing real traffic change trend. Monte-Carlo simulation method is used for reliability analysis which provides the data to obtain fatigue vulnerability curves. The estimation method proposed was verified by comparing with the results of reference study and applying to the steel bridges in service.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.269-279
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• 2020

Traffic estimation mainly involves surveying equipment such as automatic vehicle classification, vehicle detection system, toll collection system, and personnel surveys through CCTV (Closed Circuit TeleVision), but this requires a lot of manpower and cost. In this study, we proposed a method of estimating traffic volume using deep learning and stereo CCTV to overcome the limitation of not detecting the entire vehicle in case of single CCTV. COCO (Common Objects in Context) dataset was used to train deep learning models to detect vehicles, and each vehicle was detected in left and right CCTV images in real time. Then, the vehicle that could not be detected from each image was additionally detected by using affine transformation to improve the accuracy of traffic volume. Experiments were conducted separately for the normal road environment and the case of weather conditions with fog. In the normal road environment, vehicle detection improved by 6.75% and 5.92% in left and right images, respectively, than in a single CCTV image. In addition, in the foggy road environment, vehicle detection was improved by 10.79% and 12.88% in the left and right images, respectively.