• 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 Korean Society for Atmospheric Environment
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• v.11 no.3
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• pp.273-278
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• 1995

This study is to estimate the ventilation volume by the traffic that originated from driving automobiles for two tunnels (Kugi tunnel and Kumhwa tunnel) that adopted natural ventilation system among tunnels of Seoul, and on the basis of which, we estimated the ventilation velume at various conditions. With the result of the estimation, we will present the basic method that can be operated with the optimum condition for the ventilation system. Estimating the predicted ventilation volume in the tennel by the pollutant concentration, we used traffic volume and CO emission data by the automobile speed and CO concentration in the tunnel. And, when we estimated the traffic ventilation volume by natural and traffic ventilation force, we used traffic volume, automobile speed, tunnel area, automobile area data and so on. As the result of simple regression between predicted ventilation volume and traffic ventilation volume, we attained the regression coefficient 0.88, and achieved the relation form that predicted ventilation volume equal 0.12x traffic ventilation volume-92, 000. Using this equation, we estimated the ventilation volume to satisfy the enviromnental standards of several space, and calculated the required volume for mechanical ventilation. Incase of Kumhwa Tunnel, there is a need of mechanical ventilation all day long to satisfy air quality standard 9 ppm for 8 hours average and 10 ppm for the indoor air quality standard of public facilities.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.5
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• pp.22-35
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• 2013

With the increase in image detectors, concerns about the reliability of traffic information are increasing. In this paper, we propose a method to generate reliable traffic volume using analysis of the point detector data as a representative value. Therefore, targeting expressway, we analyzed the difference in traffic volume collected by loop and image detector, and verified statistically using t-test, and finally analyzed the error rate compare to the real traffic volume. Analysis revealed that there was a statistically difference the traffic date collected by the loop detector and the image detector, in the same period, the same time, respectively. In addition, the difference between the actual traffic volume and traffic that have been collected in a loop detector was the lowest Therefore, creating a traffic volume of representative value, we proposed a method to use loop detector than the average traffic volume collected by each detector. It shows that it is more important to use one high-quality data rather than various low-quality data to produce a representative value.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.19-27
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• 2010

Historical archived traffic data management system enables a long term time-series analysis and provides data necessary to acquire the constantly changing traffic conditions and to evaluate and analyze various traffic related strategies and policies. Such features are provided by maintaining highly reliable traffic data through scientific and systematic management. Now, the management systems for massive traffic data have a several problems such as, the storing and management methods of a large volume of archive data. In this paper, we describe how to storing and management for the massive traffic data and, we propose methodology for logical and physical architecture, collecting and storing, database design and implementation, process design of massive traffic data.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.297-303
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• 2011

Major ports in Northeastern Asia engage in fierce competition to attract transshipment traffic volume. Existing time series analyses for analyzing port competition relationships examine the types of competition and relations through the signs of coefficients in cointegration equations using the transshipment traffic volume results. However, there are cases for which analyzing competing relationships is not possible based on the results of the transshipment traffic volume data differences and limitations in the forecasting of traffic volume. Accordingly, we used the Lotka-Volterra (L-V) model,also known as the ecosystem competitive relation model, to analyze port competition relations for the long-term forecast of South Korean transshipment traffic volume.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.2
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• pp.1-13
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• 2015

Traffic volume data is basic information that is used in a wide variety of fields. Existing missing traffic volume data imputation method did not take the effect on the rainfall. This research analyzed considering of the rainfall effect in missing traffic volume data imputation method. In order to consider the effect of rainfall, established the following assumption. When missing of traffic volume data generated in rainy days it would be more accurate to use only the traffic volume data of the past rainy days. To confirm this assumption, compared for accuracy of imputed results at three kinds of imputation method(Unconditional Mean, Auto Regression, Expectation-Maximization Algorithm). The analysis results, the case on consideration of the rainfall effect was more low error occurred.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.19-27
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• 2021

In this paper, we present a method for effectively predicting traffic volume based on vehicle location data that are collected by using LDP (Local Differential Privacy). The proposed solution in this paper consists of two phases: the process of collecting vehicle location data in a privacy-presering manner and the process of predicting traffic volume using the collected location data. In the first phase, the vehicle's location data is collected by using LDP to prevent privacy issues that may arise during the data collection process. LDP adds random noise to the original data when collecting data to prevent the data owner's sensitive information from being exposed to the outside. This allows the collection of vehicle location data, while preserving the driver's privacy. In the second phase, the traffic volume is predicted by applying deep learning techniques to the data collected in the first stage. Experimental results with real data sets demonstrate that the method proposed in this paper can effectively predict the traffic volume using the location data that are collected in a privacy-preserving manner.

• Journal of Korean Society of Transportation
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• v.30 no.3
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• pp.31-41
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• 2012

Due to growing interests in the distribution of traffic volume through information dissemination such as VMS and traffic broadcasting system, the research on the driver's reaction and effect of the traffic report has continued. In this study, we propose a methodology, which estimates the traffic volume of diversion and the consequential diversion rate using FTMS data and TCS data, and the estimation is based on the analysis of the national highway and IC, in which real-time FTMS and TCS data are established. We also calculate the diversion rate of actual targeted sections and analyze the changes in time and spatial diversion rate. In this study, we define a deviation (considering a deviation due to dynamic properties of traffic conditions) found when the outflow traffic volume is temporarily higher than the average outflow traffic volume on a relevant time slot after providing traffic information. The diverting volume is considered to be caused by the traffic information, and the study determines the ratio of traffic volume on highways to that of route diversion as the diversion rate. The analysis on changes in the diversion rate in accordance with the time flow, the initial change in the diversion rate on upstream IC that first acquires the report on the traffic congestion is significant. After that, the change in the diversion rate on upstream IC affects the route diversion on downstream IC with spatial and time flow, and this again leads the change in upstream IC. Thereby, we confirmed that there is a feedback-control circulation system in the route diversion.

• Journal of Korean Society of Transportation
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• v.12 no.1
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• pp.97-116
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• 1994

Today advanced traffic management systems are required because of a high increase in traffic demand. Accordingly, the objective of this study is to take advantage of image processing systems and present image processing methods available for collection of the data on traffic characteristics, and then to investigate the possibility of traffic flow analysis by means of comparison and analysis of measured traffic flow. Data were collected at two places of Daegu city and Kyongbu expressway by using VTR. Rear view (down stream) and frontal view (up stream) methods were employed to compare and analyze traffic characteristics including traffic volume, speed, time-headway, time-occupancy, and vehicle-length, by analysis of measured traffic flow and image processing respectively. Judging from the results obtained by this study, image processing techniques are sufficient for the analysis of traffic volume, but a frame grabber equipped with high speed processor is necessary as well, with low level system judged to be sufficient for traffic volume analysis.

• International Journal of Highway Engineering
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• v.9 no.3
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• pp.21-28
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• 2007

We suggest a new spatial linear interpolation method to substitute linear interpolation method which widely used in transportation engineering to impute the missing daily traffic volume. We layout daily traffic volume which is time series data over the virtual lattice space to consider the spatial correlation. We used Moran Index to evaluate the spatial correlations among daily traffic volume in same week and same date traffic volume by week considering the circularity of daily traffic volume. For real application, we used daily traffic volume on November, 2004 provided by Korea Institute of Construction Technology(KICT) and transformed daily traffic volume to 4 times 7 virtual lattice space to reflect the spatial correlation. Finally we showed that the spatial linear interpolation method has good performance for missing data imputation based on MAPE, RMSE, and Theil's U criteria.