• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1445-1447
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• 2000

본 논문에서는 철도 수요예측 문제의 유형을 목적에 따라 3가지로 분류하였고, 최근 철도자원을 재고관리 차원에서 접근하고자 하는 시각에 따라 분배모형으로써 적응필터를 사용하는 방법의 타당성에 대해 설명하였다. 또 철도 승객수요의 주요 특징을 분석하였으며, 철도 승객수요 예측의 요구사항 및 방법론을 대규모 재고관리 시스템의 일반적 요구사항에 따라 정리하였다. 영향인자에 대한 분석으로 요일별 계절변동 지수를 정량적으로 산정하였다. 적응필터를 이용한 철도 승객수요 예측의 예제를 제시하였으며, 예측에의 정확성에 대한 비교를 제시하였다.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.401-409
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• 2014

This study deals with capacity expansion planning of airport infrastructure in view of economic validation that reflect construction costs and social benefits according to the reduction of passengers' delay time. We first forecast the airport peak-demand which has a seasonal and cyclical feature with ARIMA model that has been one of the most widely used linear models in time series forecasting. A discrete event simulation model is built for estimating actual delay time of passengers that consider the passenger's dynamic flow within airport infrastructure after arriving at the airport. With the trade-off relationship between cost and benefit, we determine an economic quantity of conveyor that will be expanded. Through the experiment performed with the case study of Incheon international airport, we demonstrate that our approach can be an effective method to solve the airport expansion problem with seasonal passenger arrival and dynamic operational aspects in airport infrastructure.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.81-90
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• 2008

Bus passengers need some time to adapt to the changed bus route or free bus transfer system which is part of the public transportation system restructuring plan. This research is focused on the characteristics of monthly patterns of bus passengers. The period of stabilization of bus passenger demand after the rearrangement of bus route system by a time series were analysed. In order to look into the characteristics of bus passenger demand by month, data on the number of monthly bus passengers of recent five years in metropolitan cities across the nation was collected. Kendall's coefficient of concordance is used to test whether the cities showed concordance with respect to the number of monthly bus passengers during a period of five years. The study collected and performed a time series analysis of data on the number of monthly bus passengers during the past ten years in Daegu metropolitan area which carried out a new bus route plan in February 2006. The number of monthly bus passengers in 2006 was estimated using the time series analysis. The city of Daegu found that after six months the estimated and actual values displayed a similar pattern. This result can be applied to other cities in estimating the passenger demands in the future.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.1
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• pp.99-106
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• 2023

글로벌 팬데믹 사태는 항공 수요에 부정적인 영향을 끼치는 요소 중 하나다. 글로벌 팬데믹으로 인해 한국은 2020년과 2021년의 항공 승객 수가 2019년 대비 각각 68.1%와 47% 감소했다. 본 연구는 지난 20여년 동안 발생한 4대 팬데믹 특성을 분석, 전염병의 영향을 연구하는 것을 목표로 한다. SARS, H1N1, MERS 및 COVID-19의 발생기간 동안 한국의 항공 여객 및 화물 수요에 대한 실증 데이터를 활용하여 영향력을 분석한다. 또한 머신러닝 회귀 모델을 구축하여 향후 발생할 다른 전염병 대한 항공 수요를 예측하고자 한다. 연구 결과, 전염병이 항공 운항편수와 승객에 부정적인 영향을 미친다는 사실을 발견하였다. 반면화물 수송에는 긍정적인 영향을 미친다는 분석 결과를 도출하였다. 본 분석에 활용되는 회귀 모델은 팬데믹 기간 동안 항공수요를 예측하는 데 평균 86.8%의 기능을 보였다. 또한 본 연구는 특정 국가의 팬데믹 상황보다 전 세계적인 팬데믹 상황이 항공 운송 수요에 더 많은 영향을 미친다는 것을 보여준다.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.472-479
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• 2024

Half of the world's total population lives in cities, continuous urbanization is progressing, and the urban population is expected to exceed two-thirds of the total population by 2050. To resolve this phenomenon, the Korean government is focusing on building a new urban air mobility (UAM) industrial ecosystem. Airlines are also part of the UAM industry ecosystem and are preparing to improve efficiency in safe operations, passenger safety, aircraft operation efficiency, and punctuality. This study performs demand forecasting using time series data on the number of daily passengers on Korean Air's Gimpo to Jeju route from 2019 to 2023. For this purpose, statistical and machine learning models such as SARIMA, Prophet, CatBoost, and Random Forest are applied. Methods for effectively capturing passenger demand patterns were evaluated through various models, and the machine learning-based Random Forest model showed the best prediction results. The research results will present an optimal model for accurate demand forecasting in the aviation industry and provide basic information needed for operational planning and resource allocation.

• Spatial Information Research
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• v.19 no.4
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• pp.11-20
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• 2011

In accordance with low carbon and green growth paradigm, a subway is one of major public transit systems for resolving traffic congestion and decreasing traffic accidents. In addition, as subway networks expand, passengers' travel pattern in the subway network change and consequently affect the urban structure. Generally, new subway route has been planned and developed, mainly considering a travel demand forecast. However, it is desired to conduct an empirical analysis on the forecast model regarding change of travel accessibility and passenger demand pattern according to new subway line. Therefore, in this paper, an alternative method, developed based upon a spatial syntax model, is proposed for evaluating new subway route in terms of passenger's mobility and network accessibility. In a case study, we constructed subway network data, mainly targeting the no 9 subway line opened in 2009. With an axial-map analysis, we calculated spatial characteristics to describe topological movement interface. We then analyzed actual modal shift and change on demand of passengers through the number of subway passenger between subway stations and the number of passenger according to comparative bus line from Smart Card to validate suggested methods. Results show that the proposed method provides quantitative means of visualizing passenger flow in subway route planning and of analyzing the time-space characteristics of network. Also, it is expected that the proposed method can be utilized for predicting a passengers' pattern and its impact on public transportation.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.181-190
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• 2005

The time required for airport process is considered more important as the airports are becoming bigger. International Civil Aviation Organization mattes this international standards and recommends not to exceed it. The passenger forecasting model is developed to predict the number of passengers at the check-in counter, and the area of formalities for departure and entry. In case of forecasting the number of outbound-passengers. the patterns of show-up lead time(SLT) at the check-in counter and lag time from check-in counter to the area of departure formalities are modeled in terms of time. On the other hand, the matter of the choice of check-in counters and areas of departure formalities are modeled in terms of space. In case of forecasting the number of inbound-passengers and transfer passengers, the time of airplane movement from arrival to block on at the gate and the time of passengers required from gate to the area of formalities for entry are modeled in terms of time. While the matter of the choice of gates and the areas of formalities for entry are modeled in terms of space. The average error of forecasting outbound-passengers and inbound-passengers is respectively 15% and 10%, which are considered excellent with the 5% error of passenger reservation information as input data. Through the development of passenger forecasting models, we assure we could provide passengers with valuable service because we allocate resource such as employees and equipments according to the degree of concentration of passengers.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.111-128
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• 2019

Subways are eco-friendly public transportation that can transport large numbers of passengers safely and quickly. It is necessary to predict the accurate number of passengers in order to increase public interest in subway. This study groups stations on Lines 1 to 9 of the Seoul Metropolitan Subway using clustering analysis. We propose one final prediction model for all stations and three optimal prediction models for each cluster. We found three groups of stations out of 294 total subway stations. The Group 1 area is industrial and commercial, the Group 2 ares is residential and commercial, and the Group 3 area is residential districts. Various data mining techniques were conducted for each group, as well as driving some influential factors on demand prediction. We use our model to predict the number of passengers for 8 new stations which are part of the 3rd extension plan of Seoul metro line 9 opened in October 2018. The estimated average number of passengers per hour is from 241 to 452 and the estimated maximum number of passengers per hour is from 969 to 1515. We believe our analysis can help improve the efficiency of public transportation policy.

• Journal of the Korean Society for Railway
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• v.20 no.1
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• pp.156-163
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• 2017

In the major operation sections of the urban railway, there has been habitual delay, and delay propagation; another problem is the increase of crowds and of inconvenience to passengers. The urban railway has different characteristics from rural railways, such as uncertainty of demand and irregularity of train operation. In urban railways, recently, operators manage quality indicators of service using operation results, such as the delay of train operation and the congestion of trains. However, because the urban railway has characteristics in which demand, passenger behavior, and train operation mutually affect each other, it is difficult to express the quality of service that passengers actually feel. In this paper, we suggest a quality indicator of service from the viewpoint of passengers, and present a demand responsive multi-train simulation method to predict dynamic dwell time and train operation status; we also use simulation results to consider changes in the quality indicator of service.

• 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.