• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.2
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• pp.14-26
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• 2009

This study presented a novel method to estimate travel times under incident conditions. Predictive travel time information was defined and evaluated with the proposed method. The proposed method utilized individual vehicle speeds obtained from global positioning systems (GPS) and inter-vehicle communications(IVC) for more reliable real-time travel times. Individual vehicle trajectory data were extracted from microscopic traffic simulations using AIMSUN. Market penetration rates (MPR) and IVC ranges were explored with the accuracy of travel times. Relationship among travel time accuracy, IVC ranges, and MPR were further identified using regression analyses. The outcomes of this study would be useful to derive functional requirements associated with traffic information systems under forthcoming ubiquitous transportation environment

• Journal of Korean Society of Transportation
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• v.24 no.6 s.92
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• pp.33-43
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• 2006

This research is to the selection of optimal probability distribution as well as the estimation for design hourly factor in consideration of traffic characteristic, such as road function, lane number and AADT. To accomplish the objectives, we are applied to various probability distribution using traffic data that observed at permanent traffic count points in 2005. The parameters or the selected 14 probability distribution were estimated based on the method of maximum likelihood and the validity condition of the estimated parameter The goodness-of-fit test, such as chi-square test. was performed as well as the estimation of design hourly factor. As a result, An appropriate distributions of each case were selected : Pearson V for two lane of rural roads, LogLogistic for the four lane of rural roads, LogLogistic for the urban roads, Extreme value for recreation roads. And optimal K factor are as following : $0.1{\sim}0.2 $ for two lane of rural roads, $0.09{\sim}0.14$ for the four lane of rural roads. $0.07{\sim}0.13$ for the urban roads, $0.1{\sim}0.2$ for recreation roads.

• Journal of Korean Society of Transportation
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• v.20 no.6
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• pp.59-68
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• 2002

AADT(Annual Average Daily Traffic) can be obtained by using short-term counted traffic data rather than using traffic data collected for 365 days. The process is a very important in estimating AADT using short-term traffic count data. Therefore, There have been many studies about estimating AADT. In this Paper, we tried to improve the process of the AADT estimation based on the former AADT estimation researches. Firstly, we found the factor showing differences among groups. To do so, we examined hourly variables(divided to total hours, weekday hours. Saturday hours, Sunday hours, weekday and Sunday hours, and weekday and Saturday hours) every time changing the number of groups. After all, we selected the hourly variables of Sunday and weekday as the factor showing differences among groups. Secondly, we classified 200 locations into 10 groups through cluster analysis using only monthly variables. The nile of deciding the number of groups is maximizing deviation among hourly variables of each group. Thirdly, we classified 200 locations which had been used in the second step into the 10 groups by applying statistical techniques such as Discriminant analysis and Neural network. This step is for testing the rate of distinguish between the right group including each location and a wrong one. In conclusion, the result of this study's method was closer to real AADT value than that of the former method. and this study significantly contributes to improve the method of AADT estimation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.1 s.9
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• pp.1-12
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• 2006

In order to estimate a dynamic origin and destination demand between on and off-ramps in the freeways, a traffic flow theory can be used to calculate a link distribution proportion of traffics moving between them. We have developed a dynamic traffic estimation model based on the three-phase traffic theory (Kerner, 2004), which explains the complexity of traffic phenomena based on phase transitions among free-flow, synchronized flow and moving jam phases, and on their complex nonlinear spatiotemporal features. The developed model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We have estimated the link, on and off-ramp volumes at every time interval by using traffic data collected from vehicle detection systems in Korea freeway sections. The analyzed results show that the developed model describes traffic flows adequately.

• Journal of Korean Society of Transportation
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• v.34 no.2
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• pp.135-145
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• 2016

This study assumes a vehicle choice model based on the multinomial model and analyzes the vehicle choice behaviors of consumer. An SP survey targeting drivers was implemented and data was collected for model estimates, with the possible choice options of the survey takers limited to gasoline, HEV, PHEV, and EV vehicles. The explanatory variable mostly displayed a significance level of under 5%, and excluding variables for price and fuel the remaining variables were all consistent with the logical direction with the plus (+) sign and the results were determined to be rational. Consumers selecting mid-size & full-size vehicles are able to afford more than consumers that selected other vehicle types, so there was relatively little consideration given to low fuel costs when compared to vehicle price. For this reason, it was determined that for the full-size vehicle model the fuel variable could be disregarded. Socio-economic variables that were statistically significant were the age and infor variables for the sub-compact & compact, the age, infor and inc3 variables for the mid-sized & full-size vehicles.

• Journal of Korean Society of Transportation
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• v.33 no.4
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• pp.393-404
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• 2015

Gravity model has had the major problem that the model explains the characteristics of travel behavior with only deterrence factors such as travel time or cost. In modern society, travel behavior can be affected not only deterrence factors but also zonal characteristics or transportation service. Therefore, those features have to be considered to estimate the future travel demand accurately. In this regard, there are two primary aims of this study: 1. to identify the characteristics of inter-zonal travel, 2. to develop the new type of calibration method. By employing accessibility variable which can explain the manifold pattern of trip, we define the zonal travel behavior newly. Furthermore, we suggest 2-phase calibration method, since existing calibration method cannot find the optimum solution when organizing the deterrence function with the new variables. The new method proceeds with 2 steps; step 1.estimating deterrence parameter, step 2. finding balancing factors. The validation results with RMSE, E-norm, C.R show that this study model explains the inter-zonal travel pattern adequately and estimate the O/D pairs precisely than existing gravity model. Especially, the problem with estimation of short distance trip is overcomed. In conclusion, it is possible to draw the conclusion that this study suggests the possibility of improvement for trip distribution model.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.33-42
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• 2004

지하철역별 수요는 개통 후 경과 연도에 따라서 S자 형태로 증가한다. 즉 개통 초기에는 잠재되어 있던 지하철 수요가 시간의 경과에 따라 계속적으로 증가하다가, 개통 후 10$\sim$13년 정도가 경과하면 최대를 나타낸 후 거의 정체하는 현상을 보인다. 그러나 지금까지 지하철 수요를 추정하기 위해서 이용되었던 4단계 모형은 이러한 지하철 수요의 증가 추세를 반영할 수 없기 때문에 실제 수요와 많은 차이를 보였다. 따라서 본 연구에서는 이러한 문제를 해결해 보고자 서울시 지하철 2$\sim$8호선의 실제 수요를 토대로 지하철역별 수요, 특히 순수한 승차인원을 추정하는 모형을 개발하였다. 모형에 적용되는 함수식은 실제 지하철역별 수요와 가장 유사한 형태를 보이고 있는 로지스틱 함수식을 이용하였다. 또한 각각의 지하철역별로 나타나는 상이한 특성은 카테고리로 분류하여 모형에 반영하였다. 카테고리는 토지이용도, 사회경제활동의 규모, 그리고 지하철역의 특성에 따라 분류하였다. 각 카테고리별 특성을 대표하는 독립 변수로 인구 종사자수, 학생수와 개통 후 경과 연도 등을 선정하였다. 그 결과 카테고리별로 추정된 지하철역별 수요는 통계적으로 매우 유의한 것으로 나타났다. 본 연구는 지하철역별로 승차하는 순수한 수요를 보다 정확하게 추정하기 위한 모형을 개발하는 것이 주된 목적이다. 반면에 본 모형을 이용하여 지하철역별 하차 수요 및 횐승 수요를 추정하는 것은 어렵다. 따라서 기존에 지하철 수요를 추정하는 데에 가장 많이 사용된 4단계 모형과 접목하여야 하며, 이에 대한 방안도 본 연구에서 제시하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.51-63
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• 2000

Dynamic traffic assignment(DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvement, especially regarding its formulation and solution algerian Recently, with its promise for In(Intelligent Transportation System) and GIS(Geographic Information System) applications, DTA have received increasing attention. This potential also implies higher requirement for DTA modeling, especially regarding its solution efficiency for real-time implementation. But DTA have many mathematical difficulties in searching process due to the complexity of spatial and temporal variables. Although many solution algorithms have been studied, conventional methods cannot iud the solution in case that objective function or constraints is not convex. In this paper, the genetic algorithm to find the solution of DTA is applied and the Merchant-Nemhauser model is used as DTA model because it has a nonconvex constraint set. To handle the nonconvex constraint set the GENOCOP III system which is a kind of the genetic algorithm is used in this study. Results for the sample network have been compared with the results of conventional method.

• Proceedings of the KOR-KST Conference
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• 1998.10b
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• pp.297-297
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• 1998

고속도로의 교통혼잡을 관리하기 위해서는 근본적으로 혼잡지점 상류부의 진입교통량을 제어해야 한다. 이를 위한 효과적인 램프미터링 운영전략이나 고속도로 교통정보제공방안을 수립하기 위해서는 혼잡영향권(대기행렬길이)에 관한 신뢰성 있는 데이터가 반드시 필요하다. 고속도로의 대기행렬길이를 산정하기 위해 일반적으로 충격파이론과 Queueing이론을 제시하고 있다. 그러나, 기존의 충격파 이론을 포물선형의 교통량-밀도관계식을 근거로 하고 있어 충격파간에 발생하는 부수적인 충격파를 해석하는 과정이 수학적으로 불가능하여 실질적인 목적으로 사용할 수 없음은 이미 잘 알고 있는 사실이다. 최근에 이러한 한계를 극복할 수 있는 새로운 방법으로 교통량 밀도간의 관계식을 삼각형으로 가정하고 교통량 대신에 누적교통량을 사용하는 Simplified Theory of Kinematic Waves In Highway Traffic이 개발(Newell, 1993)되었지만, 이 방법을 적용하기 위해서는 기본적으로 대상 고속도로 구간의 교통량-밀도관계식을 규명해야 하는 어려움이 있다.(사실 실시간으로 밀도데이터를 수집하기란 불가능하다.) Queueing이론에서 제시하는 대기행렬은 모두 대기차량이 병목지점에 수직으로 정렬하여 도로를 점유하지 않는 Point Queue(혹은 Vertical stack Queue)로서 실제로 도로상에 정렬된 대기행렬(Real Physical Queue)과는 전혀 다르다. 이미 입증된 바 있어, Queueing이론을 이용함은 타당성이 없다. 이러한 사실에 근거하여 본 연구는 고속도로 대기행렬길이를 산정할 수 있는 모형개발을 위한 기초연구로서 혼잡상태의 연속류 특성을 분석하는데 목적이 있다. 이를 위해, 본 연구에서는 서울시 도시고속도로에서 수집한 실제 데이터를 이용하여 진입램프지점의 혼잡상태에서 대기행렬의 증가 또는 감소하는 과정을 분석하였다. 주요 분석결과는 다음과 같다. 1. 혼잡초기의 대기행렬은 다른 혼잡시기에 비해 상대적으로 급속한 속도로 증가함. 2. 혼잡초기의 대기행렬의 밀도는 다른 혼잡시기에 비해 비교적 낮음. 3. 위의 두 결과는 서로 관계가 있으며, 혼잡시 운전자의 행태(차두간격)과 혼잡기간중에도 변화함을 의미함. 4. 교통변수 중에서 대기행렬길이를 산정하는데 적합한 교통변수를 교통량과 밀도로 판단됨. 5. Queueing이론에서 제시하는 대리행렬길이 산정방법인 대기차량대수$\times$평균차두간격은 대기행렬내 밀도가 일정하지 않아 부적합함을 재확인함. 6. 혼잡초기를 제외한 혼잡기간 중 대기행렬길이는 밀도데이터 없이도 혼잡 상류부의 도착교통량과 병목지점 본선통과교통량만을 이용하여 추정이 가능함. 7. 이상에 연구한 결과를 토대로, 고속도로 대기행렬길이를 산정할 수 있는 기초적인 도형을 제시함.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.493-498
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• 2017

Recently, traffic data is analyzed for efficiency of bus operation, D2D(: Door to Door) service, and self-driving of public transportation. However, various studies have been carried out to predict the delay time of public transportation, especially buses, but the research to date has been insufficient due to limitations of simple analysis and data acquisition. In this study, delay time estimation is performed by collecting and processing data such as day of the week, weather, and time of day based on bus operation information. The proposed method in this paper can be applied to autonomous public transport and public traffic control system by improving the accuracy by adding variables in the future.