• Journal of Korean Society of Transportation
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• v.34 no.5
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• pp.476-488
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• 2016

This study reviewed factors and causes that affect on reliability and accuracy of transportation demand forecasting. In general, the causes of forecasting errors come from variety and irregularity of trip behaviors, data limitation, data aggregation and model simplification. Theoretical understanding about the inevitable errors will be helpful for reasonable decision making for practical transportation policies. The study especially focused on traffic assignment with the KTDB data, and described the factors and causes of errors by classifying six categories such as (1) errors in input data, (2) errors due to spacial aggregation and representation method of network, (3) errors from representing values for variations of traffic patterns, (4) errors from simplification of traffic flow model, and (5) errors from aggregation of route choice behavior.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.241-250
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• 2008

With the ever-growing amount of traffic, there is an increasing need for good quality travel time information. Various existing outlier filtering and missing data imputation algorithms using AVI data for interrupted and uninterrupted traffic flow have been proposed. This paper is devoted to development of an outlier filtering and missing data imputation algorithm by using Toll Collection System (TCS) data. TCS travel time data collected from August to September 2007 were employed. Travel time data from TCS are made out of records of every passing vehicle; these data have potential for providing real-time travel time information. However, the authors found that as the distance between entry tollgates and exit tollgates increases, the variance of travel time also increases. Also, time gaps appeared in the case of long distances between tollgates. Finally, the authors propose a new method for making representative values after removal of abnormal and "noise" data and after analyzing existing methods. The proposed algorithm is effective.

• Journal of Korean Society of Transportation
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• v.18 no.6
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• pp.19-32
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• 2000

우리나라 비신호 교차로의 운영방법은 미국과는 다르게 대부분 완전 비제어식 운영(Totally Uncontrol)방법으로 되어있다. 본 연구에서는 우리나라 비신호 교차로들 중 미국 HCM의 TWSC 교차로에 관한 분석방법이 적용 가능하다고 판단되는 비신호 교차로들을 대상으로 임계간격과 추종시간의 기준을 분석하였다. 본 연구에서는 수도권을 중심으로 총 11개의 4-지 비신호 교차로들을 대상으로 이동류별, 차량의 종류별에 따른 차량들의 임계간격과 추종시간을 조사 및 분석하였다. 그 결과 임계간격은 3.8초 - 5.4 초로 나타났으며, 추종시간은 2.4초- 2.7초 범위를 나타내었다. 또한, 임계간격의 경우 차량의 종류별에 따른 차이는 나타나지 않았으나, 이동류별로는 차이를 나타내었다. 이에 반하여, 추종시간은 이동류별에 따른 차이가 없으며, 차량의 종류별에 따라 차이가 존재하는 것으로 분석되었다. 본 연구의 분석 결과인 임계간격과 추종시간의 기준치를 국외 나라들의 연구결과와 비교 분석한 결과는 국외보다 낮게 분석되었으며, 이는 우리나라와 국외의 운전자 운전특성과 비신호 교차로의 운영방식의 차이에서 나타난 결과로 판단된다. 본 연구에서 제안한 임계간격과 추종시간의 기준은 수도권 지역의 4-지 비신호 교차로들 중 주도로와 부도로의 교통량과 속도 등의 현저한 차이가 있는 교차로 분석시 이 기준으로 적용할 수 있고, 우리 나라의 TWSC 방법을 갖는 비신호 교차로에 관한 용량과 지체시간의 이론적 모형 개발과 용량 및 지체시간의 분석을 위한 기초 자료로 활용할 수 있을 것으로 판단된다.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.157-167
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• 2010

Density is the most important congestion indicator among the three fundamental flow variables, flow, speed and density. Measuring density in the field has two different ways, direct and indirect. Taking photos with wide views is one of direct ways, which is not widely used because of its cost and lacking of proper positions. Another direct density measuring method using two point detectors has been introduced with the concept of instantaneous density, average density and measurement interval. The relationship between accuracy and measurement interval has been investigated using the SIMULATION data produced by Paramics Application Programming Interface function. We analyze the affect of segment density accuracy by sensing gap each road condition such as sensing segment length, lane and LOS after gathering data by Paramics Application Programming Interface.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.83-97
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• 2000

The traffic management schemes through traffic signal control and information provision could be effective when the link-level data and trip-level data were used simultaneously in analysis Procedures. But, because the trip-level data. such as origin, destination and departure time, can not be obtained through the existing surveillance systems directly. It is needed to estimate it using the link-level data which can be obtained easily. Therefore the objective of this study is to develop the model to estimate O-D demand using only the link flows in highway network as a real time. The methodological approaches in this study are kalman filer, least-square method and normalized least-square method. The kalman filter is developed in the basis of the bayesian update. The normalized least-square method is developed in the basis of the least-square method and the natural constraint equation. These three models were experimented using two kinds of simulated data. The one has two abrupt changing Patterns in traffic flow rates The other is a 24 hours data that has three Peak times in a day Among these models, kalman filer has Produced more accurate and adaptive results than others. Therefore it is seemed that this model could be used in traffic demand management. control, travel time forecasting and dynamic assignment, and so forth.

• Journal of Korean Society of Transportation
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• v.16 no.2
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• pp.9-22
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• 1998

본 연구는 U턴 효율적 운영에 필요한 기초자료 즉, U턴 이동류에 대한 차두시간, 출발손실시간, 승용차 환산계수 그리고 포화교통류율을 산정하였다. 또한 다중 U턴 횟수, U턴 허용 길이별 U턴 용량 특성을 분석하였다. 연구결과를 보면, U턴 평균 차두시간은 2.43초이고 이에 따른 포화교통율은 1,480(pcph)로 분석되었다. 출발손실은 두 번째 차량까지 손실이 있어 1.57초로 산정 되었다. 승용차 환산계수는 대형차 혼입율에 따라 1.98에서 1.35사이에 분포하며 평균값은 1.78이다. 그리고 주기당 다중 U턴 횟수가 많을수록 포화교통류율이 커졌으며 횟수가 1회, 2회 및 3회일 때 포화교통류율이 각각 1,600, 1,650 및 1,800pcph로 나타났다. U턴 허용 길이가 늘어나면 포화교통류율이 늘어나며 18m, 21m 및 30m로 증가될 때 1,570, 1,610, 및 1,640pcph로 증가하는 것이로 분석되었다.

• Spatial Information Research
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• v.23 no.4
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• pp.79-89
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• 2015

This study aims to design and implement a traffic model that can simulate the traffic behavior on the microscopic level by using the GIS. In the design of the model, the vehicle in the simulation environment recognizes the GIS road centerline data as road network data reflecting number of lanes, speed limit and so on. In addition, the behavior model was designed by dividing functions into the environmental perception model, time headway distribution model, car following model, and lane changing model. The implemented model was applied to Jahamun-road of Jongno-gu district to verify the accuracy of the model. As a result, the simulation results on the Jahamun-road had no great error compared with the actual observation data. In the aspect of usability of model, it is judged that this model will be able to effectively contribute to analysis of amount of carbon emission by traffic, evaluation of traffic flow, plans for location of urban infrastructure and so on.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.3-11
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• 2013

Under the consumption of bad weather situation affects traffic flows, the study scope is focused on highway capacity and speed variations among other highway traffic flow characteristic changes according to snowfall density. Thus, this study carried out through the data collection and statistical analysis by focusing on capacity and speed changes. Traffic volume, speed and density were selected as factors to explain the property change of a traffic flow for analysis, and 7 basic sections such as 3 highways in Gyeonggi-do and 4 highways near the meteorological observatory were selected as survey points for data collection. Snowfall levels were classified into 3 steps(Light, Medium, Heavy Snow) to analyze the capacity change by snowfall levels. As a result of analysis, the change of capacity depending on snowfall levels decreased 13.2% in case of light snow compared to a good weather, 18.6% in case of medium snow and 32.0% in case of heavy snow, so the capacity reduction rate increased as the snowfall level increased. The worsening weather appeared to have a very big possibility to act as a factor to reduce the operational efficiency of a road, so a road design and operation method considering this should be presented in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4D
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• pp.443-450
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• 2009

Uninterrupted facility - since there is a close relationship between traffic volume, speed and density -, when a ramp traffic flow merges into the main line, will change the traffic speed or density, and the corresponding correlational model equation will be changed. Thus, this study, using time and space-series traffic data on areas under the influence of such a merging, identified sections which changed the correlation between speed and density variables, and examined such changes. As a result, the upstream and merging sections showed the "Underwood"-shaped exponent, and the downstream after passing the merging section showed a straight line "Greenshields" model. The downstream section which changed the correlation between speed and density showed a gradual downstream movement phenomenon within 100 m-500 m from the end of the third lane linking with the ramp, as the traffic approached the inner lanes. Also, the upstream section, merging section, and downstream section involving a change showed heterogeneous traffic flows which, in the speed-density model, have a statistically different free flow speed (constant) and a different ratio of free flow speed to jam density (gradient).

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.71-79
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• 2005

Existing arterial link travel time estimation methods relying on either aggregate point-based or individual section-based traffic data have their inherent limitations. This paper demonstrates the utility of data fusion for improving arterial link travel time estimation. If the data describe traffic conditions, an operator wants to know whether the situations are going better or worse. In addition, some traffic information providing strategies require predictions of what would be the values of traffic variables during the next time period. In such situations, it is necessary to use a prediction algorithm in order to extract the average trends in traffic data or make short-term predictions of the control variables. In this research. a multi-step ahead prediction algorithm using Data fusion was developed to predict a link travel time. The algorithm performance were tested in terms of performance measures such as MAE (Mean Absolute Error), MARE(mean absolute relative error), RMSE (Root Mean Square Error), EC(equality coefficient). The performance of the proposed algorithm was superior to the current one-step ahead prediction algorithm.