• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.152-161
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• 2014

The total boarding and alighting time models have been developed by applying the multiple regression analysis with three variables; numbers of boarding or alighting passengers, non-sitting passengers, and the step-height from the ground. Such variables have influenced to the total boarding time model with the most influential in the numbers of boarding or alighting passengers and the least in the step-height. On the total alighting time model, the numbers of alighting passengers are the most strongest while the step-heights the least. The total boarding and alighting time models can be used in practices for the prediction of current and future bus stops' capacities in TOD-based towns.

• Journal of the Korean Society for Railway
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• v.16 no.2
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• pp.144-150
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• 2013

This paper presents an analysis of door design for the Ui LRT based on experiments to predict doorway flow-time and their analyses results. A similar railway vehicle (from Gimhae LRT) and operational conditions are utilized to assess the doorway flow-time through repetitive experiments. Design of the experiments consists of four scenarios, and the experiments are repeated 39 times in total. We use the results of the experiments to verify the design of doors of Ui LRT (e.g. the required number of doors and their width). Various statistical analyses are carried out for the flow-time with respect to the number of boarding/alighting passengers. We make three category levels of boarding/alighting passengers, and analyze the mean and variance for each category, and then carry out One-Way ANOVA to analyze how the number of boarding/alighting and onboard passengers impact flow-time. The results of this paper can be used for making decisions about doors of the LRT vehicle.

• Journal of the Korean Society for Railway
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• v.17 no.3
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• pp.210-215
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• 2014

Various methods have been developed in an effort to increase the scheduled speeds of the urban railways. Reducing the train dwell times by extending door widths is one such method. However, there is thus far no domestic model of boarding and alighting that is appropriate to lead to boarding and alighting time reductions if the door width is extended. Foreign models are not suitable because human behaviors, which are important factors when assessing boarding and alighting times, differ from country to country. In this study, a boarding and alighting model for domestic urban railways is proposed and related equations and parameters are derived from measured and experimental data. The model can be employed to assess time reductions in Korean railroad system if the door widths are extended.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.6
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• pp.62-70
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• 2016

Increased bus traffic leads inefficiency at near-side bus stops in median exclusive bus lane because buses are waiting for a signal does not have a vehicle arrived. This study suggests a method for estimating a proper spacing between bus stops and signalized intersection to prevent the inefficiency. We modified the Poisson model for a proper spacing by using both dwell time and waiting time of signal instead of using dwell time only. The waiting time of signal changes by spacing and it was measured using micro simulation program. The iterative algorithm using the change of waiting time of signal was also suggested. By applying the proposed method, measure waiting time by simulation and iterative algorithm, the spacing of near-side bus stops, proper spacing is suggested according to flow rate level.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.527-528
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• 2016

현대인들이 가장 많이 사용하는 대중교통은 버스이다. 이에 DB를 통해 많은 이용횟수를 보이는 출, 퇴근시간대에 급행버스를 배치하여 보다 편리한 생활을 위해 다음과 같은 프로젝트를 진행하게 되었다. 서울시를 중심으로 이용객의 시간별, 일별, 월별 승하차 인원을 조사하고 지역인구와 승하차 인원의 연관관계를 탐색하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.13-18
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• 2018

In this study, we classify Seoul metro stations according to boarding and alighting patterns using machine earning technique. The target data is the number of boarding and alighting passengers per hour every day at 233 subway stations from 2008 to 2017 provided by the public data portal. Gaussian mixture model (GMM) and K-means clustering are used as machine learning techniques in order to classify subway stations. The distribution of the boarding time and the alighting time of the passengers can be modeled by the Gaussian mixture model. K-means clustering algorithm is used for unsupervised learning based on the data obtained by GMM modeling. As a result of the research, Seoul metro stations are classified into four groups according to boarding and alighting patterns. The results of this study can be utilized as a basic knowledge for analyzing the characteristics of Seoul subway stations and analyzing it economically, socially and culturally. The method of this research can be applied to public data and big data in areas requiring clustering.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1612-1627
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• 2008

Dwelling time is one of the factors that influence in rail. Current research in dwelling time has been focusing on railways, the state of the research in high-speed rail's dwelling time is not complete. Dwelling time is consisted of time to open door, time to get into and out of vehicle and time of the departure it takes after the passenger's door was closed, it was affected by various factors such as congestion's degree in vehicle, the number of persons that get into and out of vehicle, congestion's degree in station. In order to analyze theses, we need data analysis such as the number of persons that get into and out of vehicle, congestion's degree in station, congestion's degree in vehicle, but the congestion's degree and passenger's distribution chart in vehicle is excluded in this research due to difficulty of gathering data, and thus we will develop forecasting models through high-speed rail's demand most affected by the dwelling time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.731-738
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• 2006

Facing congestion, people want to know traffic information about their routes, especially real-time link travel time (LTT). In this paper, as a sequel paper of the previous non-taxi based LTT generating study by Choi et al. (1998), taxi based GPS probes have been tried to produce LTT for urban arterials. Taxis in itself are good deployment mode of GPS probes although it by nature experiences boarding and alighting time noises which should be accounted. A heuristic real-time dynamic outlier filter algorithm for taxi GPS probe has been developed focusing on urban arterials. An actual traffic survey for dynamic link travel times has been conducted using license plate method for the test arterials of Seoul city transportation network. With the algorithm, it is estimated that 70% of outliers have been filtered and the relative error has been improved by 73.7%. The filtering algorithm developed here would be expected to be in use for other spatial sites with some calibration efforts. Some limitations and future research agenda have also been discussed.

• Journal of Korean Society of Transportation
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• v.24 no.1 s.87
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• pp.97-108
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• 2006

At present, Korean many cities depend on subjective judgements of experts to estimate the number of bus-stops and inter-stop space. To get reliable results by using more objective procedure, we search for old studies and models, but they don't concern alighting demands and a random demand distributions. Our study recognize and overcome these limitation. We devide the demand into boarding and alighting demands, and define the model that can estimate flexibly optimal number of bus-stop and inter-stop space on each segment by the demand distribution. Also we apply this new model to a simple example route having various demand distributions As a result, the number of bus-stop on each segment can be estimate flexibly in proportion to boarding or alighting demand by using this model.

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.85-97
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• 2010

The present study aims at estimating the model of bus bunching duration along with its influential determinants in an attempt to understand the status of bus bunching being created by buses from various routes converging into one bus stop. To do so, the duration analysis, a well-known survival analysis, was adopted in order to capture the distribution of duration time as for 8 base lines, accordingly developing the model best fit for weibull distribution. Key attention to draw out the duration time model for bus bunching phenomena was laid, by analyzing 18 impact factors, on such criterion variables as number of berth, number of bus line in each berth and maximum capacity of on-and-off passengers in each line. Comparison in two typical types of bus lane was made between bus-only center lane(Dobong Mia-ro) and normal street-side lane(Tongil Euiju-ro). In this regard, the study, based on the model as above, suggested appropriate alternatives to improve the bus operation by effectively controlling bus bunching.