• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.110-123
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• 2019

Traffic data is the most basic element necessary for transportation planning and traffic system operation. Recently, a method of estimating traffic flow characteristics using distance to a leading vehicle measured by an ADAS camera has been attempted. This study investigated the feasibility of the ADAS vehicle reflecting the distance error of image-based vehicle identification technology as a means to estimate the traffic flow through the normalized root mean square error (NRMSE) based on the number of lanes, traffic demand, penetration rate of probe vehicle, and time-space estimation area by employing the microscopic simulation model, VISSIM. As a result, the estimate of low density traffic flow (i.e., LOS A, LOS B) is unreliable due to the limitation of the maximum identification distance of ADAS camera. Although the reliability of the estimates can be improved if multiple lanes, high traffic demands, and high penetration rates are implemented, artificially raising the penetration rates is unrealistic. Their reliability can be improved by extending the time dimension of the estimation area as well, but the most influential one is the driving behavior of the ADAS vehicle. In conclusion, although it is not possible to accurately estimate the traffic flow with the ADAS camera, its applicability will be expanded by improving its performance and functions.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.3
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• pp.73-84
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• 2010

This study presents a novel method to identify hazardous segments of freeway using global positioning system(GPS) based probe vehicle data. A variety of candidate contributing factors leading to higher potential of accident occurrence were extracted from the probe vehicle dataset. The research problem was defined as a classification problem, then a well-known classifier, bayesian neural network was adopted to solve the problem. A binary logistic regression technique was also used for selecting salient input variables. Test results showed that the proposed method is promising in extracting hazardous freeway sections. The outcome of this study will be effectively used for evaluating the safety of freeway sections and deriving countermeasures to prevent accidents.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.55-68
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• 2010

IEEE WAVE is going through the final standardization process as the wireless access technology which provides drivers in high-speed vehicles with safety-related information and commercial services This paper presents an application-layer protocol for safe driving assistant service and a service system emulator based on this protocol. The safe driving assistant system includes a construction zone information service and a vehicle crash notification service, an emergency vehicle notification service, a probing service and a dilemma zone decision assistant service. Emulator System design for verifying functions of system, The emulator consists of an emulator server that models the movement of all vehicles and road states and a number of clients that models functional units for OBUs, RSUs, and a traffic control center).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.625-637
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• 2015

This study aims to calculate optimal travel speeds based on analysis of the AVI data collected in the uninterrupted traffic flow, and the results are as follows. Firstly, we looked into the distribution of the sectional travel times of each probe vehicle and compared the difference in the sectional travel speeds of each probe vehicle. As a result, it is shown that outliers should be removed for the distribution of the sectional travel times. Secondly, there were differences among type 1(passenger automobiles) & type 2(automobiles for passengers and freight) and type 4(special automobiles) in the non-congestion section. thus it was revealed that there is a necessity to remove type 4(special automobiles) when calculating the sectional travel speeds. Thirdly, Based on the results of these, the optimal outlier removal procedures were applied to this study. As a result, it showed that the MAPE was between 0.3% and 2.0% and RMSE was between 0.3 and 2.3 which are very similar figures to the actual average traffic speed. Also, the minimum sample size was satisfied at the confidence level of 95%. The result of study is expected to serve as a useful basis for the local government to build the AVI. In the future, it will be necessary to study to integrate AVI data and other data for more accurate traffic information.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.3
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• pp.66-77
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• 2014

UTIS(Urban Traffic Information System) directly collects link travel time in urban area by using probe vehicles. Therefore it can estimate more accurate link travel speed compared to other traffic detection systems. However, UTIS includes some missing data caused by the lack of probe vehicles and RSEs on road network, system failures, and other factors. In this study, we suggest a new model, based on k-NN algorithm, for imputing missing data to provide more accurate travel time information. New imputation model is an adaptive k-NN which can flexibly adjust the number of nearest neighbors(NN) depending on the distribution of candidate objects. The evaluation result indicates that the new model successfully imputed missing speed data and significantly reduced the imputation error as compared with other models(ARIMA and etc). We have a plan to use the new imputation model improving traffic information service by applying UTIS Central Traffic Information Center.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.53-63
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• 2008

In this paper, we propose the regional travel time measurement algorithm using the sequence pattern matching to the type of vehicles between the origin of the region and the end of the region, that could be able to overcome the limit of conventional method such as Probe Car Method or AVI Method by License Plate Recognition. This algorithm recognizes the vehicles as a sequence group with a definite length, and measures the regional travel time by searching the sequence of the origin which is the most highly similar to the sequence of the end. According to the assumption of similarity cost function, there are proposed three types of algorithm, and it will be able to estimate the average travel time that is the most adequate to the information providing period by eliminating the abnormal value caused by inflow and outflow of vehicles. In the result of computer simulation by the length of region, the number of passing cars, the length of sequence, and the average maximum error rate are measured within 3.46%, which means that this algorithm is verified for its superior performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.21-30
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• 2008

The travel speed collected by the prove-car equipped with the GPS has the problems, which are the data's stability and finding out the representative travel speed, by the influence of the traffic signal and etc. at the interrupted traffic. This study was conducted to develop the method of filtering the outlier data from the data collected by the prove-car. The method to remove the outlier data from the serial data which were collected by the prove-car was adapted to each of the quartile deviation statistics model and the management graphic statistics model. The rate of removing the outlier data by the quartile deviation method was $0{\sim}3.7%$ while the rate by the management graphic statistic methods was $0.3{\sim}7.2%$. Both methods show the low removal rate at the dawn time when the traffic is inactivity, on the other hand the remove rate is high during the daytime. However, both methods have the problem such that the threshold level for removing the outlier data was established at the low bound in the case as good as the statistics model. Therefore, it is required for the experience calibration.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.23-35
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• 2009

This study proposes a methodology for estimating representative section travel times using individual vehicle travel information under the ubiquitous transportation environment (UTE). A novel approach is to substantialize a concept of dynamic node-links in processing trajectory data. Also, grouping vehicles was conducted to obtain more reliable travel times representing characteristics of individual vehicle travels. Since the UTE allows us to obtain higher accuracy of vehicle positions, travel times for each lane can be estimated based on the proposed methodology. Evaluation results show that less than 10% of mean absolute percentage error was achievable with 20% of probe vehicle rate. It is expected that outcome of this study is useful for providing more accurate and reliable traffic information services.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1595-1597
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• 2011

과거에는 고속도로 상에 일정간격으로 설치하여 운영 중인 VDS(Vehicle Detection System)에서 주기적으로 검지되는 지점자료나 실제로 도로를 주행하면서 교통상황을 측정하는 프로브 차량(Probe Vehicle)들을 이용하여 통행시간을 추정해 왔으나 단순한 현시점에서의 통행시간을 나타내는 점이나 설치구간이 조밀하지 못한 곳에서의 정확성 등 많은 문제점이 있어왔다. 이에 본 연구에서는 고속도로 통행료 수납자료(Toll Collection System)를 출발시각 기준으로 정렬하고, 이를 Fuzzy c-means 클러스터링 기법을 사용하여 고속도로 통행료 수납자료의 특성에 따라 분류한 후 하나의 대푯값으로 추출하여 Kalman Filter 기법에 적용하는 고속도로 통행시간 예측 모형을 설계한다.