• Proceedings of the KOR-KST Conference
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• 1998.10b
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• pp.408-408
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• 1998

교통신호기는 다양한 교통통행에 우선권을 부여하는 교통안전시설물로서, 교통소통과 안전에 지대한 영향을 끼치는 매우 중요한 통제시설이다. 그러나 현행 부적절한 신호기 설치로 차량 교통의 흐름을 방해하거나 교통사고를 증가시키는 경우가 있다. 본 연구는 교통안전시설실무편람에 제세된 9가지 신호기 설치준거 중 보행자 신호기 설치 준거에 대하여 국내도로상황 및 보행자 특성에 맞는 새로운 설치준거를 제시하는데 목적이 있다. 교통운영 측면에서 보면, 보행자 신호기는 보행자가 도로를 횡단하는데 적절한 간격을 찾을 수 없을 때 인위적으로 횡단간격을 만들어 주기 위한 교통제어시설이다. 따라서 보행자가 횡단보도에서 최대로 대기할 수 있는 시간을 기준으로 설치 여부를 결정하는 것으로 가정하고, 보행자가 보도상에서 기다릴 수 있는 최대한도 대기시간은 단일로상의 무신호 횡단보도에 교통신회가 설치되었을 경우 한 주기에서 녹색시간을 감한 시간으로 가정할 수 있다. 무신호 횡단보도 현장조사를 통하여 보행자 횡단행태, 횡단보행속도, 보행자 대기시간 등을 분석하였다. 차량의 간섭에 의한 보행자 회단간격과 차량 교통량과의 관계를 도출하고, 보행자 간섭에 의한 차량 교통량과 보행자 교통량과의 관계를 도출하였다. 결론적으로 차로수별로 차량 교통량과 보행자 교통량 상관관계에 의한 신호기 설치, 설치고려, 미설치 영역을 구분하여 보행자 신호기 설치준거(안)을 제시하였다.

• Journal of Korean Society of Transportation
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• v.25 no.1 s.94
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• pp.121-132
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• 2007

Up to now Permanent traffic volumes have been counted by Automatic Vehicle Classification (AVC) on National Highways. When counted data have missing items or errors, the data must be revised to stay statistically reliable This study was carried out to estimate correct data based on outoregression and seasonal AutoRegressive Integrated Moving Average (ARIMA). As a result of verification through seasonal ARIMA, the longer the missed period is, the greater the error. Autoregression results in better verification results than seasonal ARIMA. Traffic data is affected by the present state mote than past patterns. However. autoregression can be applied only to the cases where data include similar neighborhood patterns and even in this case. the data cannot be corrected when data are missing due to low qualify or errors Therefore, these data shoo)d be corrected using past patterns and seasonal ARIMA when the missing data occurs in short periods.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.81-88
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• 2008

This paper presents the travel demand estimation using interval estimation methods during the trip generation stage, and then followed the other three stages of the four stage trip estimation. We have used real data of Dae-jun City. To estimate travel demand using the interval estimation method, a reliability level was set to 95% by a upper bound value, a middle value and a lower bound value. The four stage traffic demand analysis procedure was equally applied and finally interval traffic was estimated. The result showed a difference between maximum values and middle values depending on the destination during the trip generation stage. It depends on an explanation ability of regression analysis. Most of interval estimation ratio resulted in the traffic assignment stage showed ${\pm}5{\sim}18%$ difference on the average and ${\pm}30{\sim}50%$ at the most.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.129-137
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• 2009

Operation of intelligent transport systems technologies in transportation networks and more detailed analysis give rise to necessity of dynamic traffic analysis model. Existing static models describe network state in average. on the contrary, dynamic traffic analysis model can describe the time-dependent network state. In this study, a dynamic traffic model for the expressway system using FTMS data is developed. Time-dependent origin-destination trip tables for nationwide expressway network are constructed using TCS data. Computation complexity is critical issue in modeling nationwide network for dynamic simulation. A subarea analysis model is developed which converts the nationwide O-D trip tables into subarea O-D trip tables. The applicability of the proposed model is tested under various scenario. This study can be viewed as a starting point of developing deployable dynamic traffic analysis model. The proposed model needs to be expanded to include arterial as well without critical computation burden.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.589-599
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• 2014

This paper analyzes the forecasting errors of traffic volumes by comparing forecasted volumes for the opening year with the observed ones in the years after the urban railway construction in the metropolitan areas. The result shows that the average inaccuracy of traffic volumes for each station was estimated at around 7.27. Based on the confirmed factors of demand estimation errors, this study seeks for an alternative method to reduce estimation errors in feasibility studies. It is noted that there is a tendency that the inaccuracy varies by regions and the longer construction period or the shorter station spacing is, the overestimation increases. If urban railway projects are proceeded as planed, therefore, the level of the inaccuracy for traffic volume forecast will be decreased. In addition, thanks to the theoretical progress, recent estimation results show higher accuracy than before. In that sense, when we introduce the new railway line, it is necessary to make an accurate and realistic demand forecast based on actual outcomes and tendency of the previous estimation. The limitation of our study is that we only cover the errors of the initial period, the opening year and deal with the exogenous variables. Further research including other variables which might be considered to cause overestimation or errors would be needed for increasing the estimation accuracy of traffic volumes.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.353-361
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• 2020

Due to the rapid urbanization, various traffic problems such as traffic jams during commute and regular traffic jams are occurring. In order to solve these traffic problems, it is necessary to quickly and accurately estimate and analyze traffic volume. ITS (Intelligent Transportation System) is a system that performs optimal traffic management by utilizing the latest ICT (Information and Communications Technology) technologies, and research has been conducted to analyze fast and accurate traffic volume through various techniques. In this study, we proposed a deep learning-based vehicle detection method using UAV (Unmanned Aerial Vehicle) video for real-time traffic analysis with high accuracy. The UAV was used to photograph orthogonal videos necessary for training and verification at intersections where various vehicles pass and trained vehicles by classifying them into sedan, truck, and bus. The experiment on UAV dataset was carried out using YOLOv3 (You Only Look Once V3), a deep learning-based object detection technique, and the experiments achieved the overall object detection rate of 90.21%, precision of 95.10% and the recall of 85.79%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.3
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• pp.371-377
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• 2022

Urban rails and buses are representative public transit systems that not only cooperate with each other, but also compete with each other. In other words, there is a possibility that the overall demand for public transportation may increase due to the introduction of a competitive public transportation system, or there is a possibility that demand will be maintained at the level that is simply converted to a competitive system. The objective of this study is to analyze the change in public transit flow when an additional transit system is introduced in a city with alternative public transit systems. To carry out this objective, we analyzed changes in public transit passenger flow before and after the introduction of an urban rail transit line 3 in Daegu Metropolitan City, where two public transit systems, urban rail and bus, exist. For accurate analysis, big data collected by passenger transportation cards were utilized for one week in the second week of April 2015, 2016, and 2019. From the analysis, it was found that although the urban rail passenger flow increased due to the additional urban rail transit system, the change in the overall public transit passenger flow in the city was insignificant. In other words, it is interpreted that the bus transit passengers have been shifted to the urban transit systems. Based on the results, this study suggested various policies to increase the demand for public transit rather than simply adding public transit systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.32-38
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• 2018

Studies on classification of national roads have been continued, but there is little research on the classification of urban arterial roads. Due to the increase of traffic volume, urban arterial roads do not perform well as main roads. In this paper, the function of urban arterial road was established by using cluster analysis using traffic characteristics. Traffic characteristics such as traffic volume, weekend coefficient and speed coefficient were used to establish the functions of 55 main arterial roads in Seoul. The results of this paper are compared with those of the method using AADT. The method using AADT classifies the characteristics according to the traffic volume of the whole lane. In this paper, however, the results are derived using the traffic volume per lane reflecting the actual traffic volume. In addition, the functional classification of the arterial roads in Seoul was compared with the results of this paper to verify that the traffic characteristics were reflected. As a result, the method presented in this paper is more effective in showing traffic characteristics than the current highway functional classification method, and the functional classification system will be helpful for road extension and planning design.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.10-20
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• 2012

This study shows how to estimate AADT(Annual Average Daily Traffic) on temporary count data using new grouping method. This study deals with clustering permanent traffic counts using monthly adjustment factor, daily adjustment factor and a percentage of hourly volume. This study uses a percentage of hourly volume comparing with other studies. Cluster analysis is used and 5 groups is suitable. First, make average of monthly adjustment factor, average of daily adjustment factor, a percentage of hourly volume for each group. Next estimate AADT using 24 hour volume(not holiday) and two adjustment factors. Goodness of fit test is used to find what groups are applicable. MAPE(Mean Absolute Percentage Error) is 8.7% in this method. It is under 1.5% comparing with other method(using adjustment factors in same section). This method is better than other studies because it can apply all temporary counts data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.4
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• pp.887-896
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• 2015

This study estimates future AADT using historical AADT and socio-economic factors in isolated area. Multiple linear regression method by socio-economic factors are lower MAPE and higher R-square than using historical AADT. Analysis of socio-economic factors influence AADT in isolated typical areas, varied socio-economic factors influence on AADT. In isolated coastal areas, oil price influence on AADT. AADT forecasting model in isolated area is excellent when analysising $R^2$ and MAPE. It is assume that estimation of AADT in isolated area using multiple linear regression is accurate because of a little passed traffic volume and traffic volume fluctuation.