• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.656-659
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• 2017

현대 사회에서는 다양한 이동수단 중 지하철, 버스 등의 대중교통에 대한 수요가 높은 편이다. 본 연구의 배경이 되는 서울특별시의 경우에는 출퇴근 시, 과반 수 이상이 대중교통을 이용한다. 대중교통 이용량에는 날씨, 평일-주말, 연착, 도로현황 등 여러 가지에 원인을 둔다. 본 연구에서는 여러 요인 중에서도 날씨 데이터(기온, 강수량, 미세먼지)에 초점을 두어, 날씨에 따른 대중교통 이용량의 변화양상을 학습하여 예측하는 연구를 진행한다. 서울특별시 25개 자치구마다의 날씨 데이터와 대중교통 이용 데이터를 이용하여 Regression을 통한 데이터 학습을 진행하였으며, 학습된 모델을 통한 날씨에 따른 서울특별시 대중교통 이용량 예측에 따른 평균 오차율은 15.49%로 낮은 오차율을 가진다. 본 연구 결과는 날씨에 따른 버스와 지하철의 배차 간격 조절 등의 대중교통 배치 판단 결정에 기초자료로 사용될 것으로 기대된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.247-255
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• 2021

Individual passenger transfer information is not included in Seoul metropolitan subway Automatic Fare Collection (AFC) data. Currently, basic data such as travel time and distance are allocated based on the TagIn terminal ID data records of AFC data. As such, knowledge of the actual path taken by passengers is constrained by the fact that transfers are not applied, resulting in overestimation of the transport index. This research proposes a method by which a transit path that connects the TagIn and TagOut terminal IDs in AFC data is determined and applied to the transit index. The method embodies the concept that a passenger's line of travel also accounts for transfers, and can be applied to the transit index. The path selection model for the passenger calculates the line of transit based on travel time minimization, with in-vehicle time, transfer walking time, and vehicle intervals all incorporated into the travel time. Since the proposed method can take into account estimated passenger movement trajectories, transport-related data of each subway organization included in the trajectories can be accurately explained. The research results in a calculation of 1.47 times the values recorded, and this can be evaluated directly in its ability to better represent the transportation policy index.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.52-64
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• 2023

This paper concentrated on estimating the travel time value of individual regional bus passengers in various in-vehicle crowding conditions. In the analysis model, the traffic-selection data of individual transportation passengers based on smart-card data were used. Variables which reflect the level of in-vehicle crowding and the variables of in-vehicle travel time that reflect the level of in-vehicle crowding were included in the model using Box-Cox transformation. The result of this paper indicates that the travel time value experienced by individual users would increase as the in-vehicle crowding level increases. The smart card data used in this paper is considered to have significant implications in terms of conducting more sophisticated and realistic qualitative research to reflect the values of variables for in-vehicle traffic hours and in-vehicle crowding levels, which previously had limitations in observation and quantification. It is expected that the effects of improvement measures for reducing congestion on regional buses can be considered quantitatively by applying the estimation results of crowding multiplier.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.114-125
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• 2006

Since wireless Internet services and small mobile communication devices come into wide use as well as the use of GPS is rapidly growing, researches on moving object, whose location information shifts sequently in accordance with time interval, are being carried out actively. Especially, the researches on vehicle moving object are applied to Advanced traveler information system, vehicle tracking system, and distribution transport system. These systems are very useful in searching previous positions, predicted future positions, the optimum course, and the shortest course of a vehicle by managing historical data of the vehicle movement. In addition, vehicle historical data are used for distribution transport plan and vehicle allocation. Vehicle historical data are stored at regular intervals, which can have a pattern. For example, a vehicle going repeatedly around a specific section follows a route very similar to another. If historical data of the vehicle with a repeated route course are stored at regular intervals, many redundant data occur, which result in much waste of storage. Therefore this thesis suggest a vehicle historical data store scheme for vehicles with a repeated route course using similar trajectory which efficiently store vehicle historical data.

• Proceedings of the CALSEC Conference
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• 1997.11a
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• pp.53-58
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• 1997

본 논문에서는 통합 Logistics Center라는 새로운 조직을 통해서, 생산, 영업 및 물류를 총괄하여, 전체 공장과 전체 DC/SB의 재고량을 줄이고. 결품현상 방지, 수송비용 절감을 도모할 수 있는 통합 물류 시스템을 구축하는데 필요한 각 Process 및 이를 수행할 수 있는 발견적 해법을 개발하는 것을 연구한다. 생산(공장)에는 가장 정확한 각 DC/SB의 수요 예측량을 토대로 수립된 발주량과, 이 발주량을 통해서 결정된 생산의뢰 계획을 제공한다. 이 생산 계획은 제품이 생산되어서 출고될 때, 근거리 지역에 있는 DC/SB에 출고될 수 있도록, 각 공장의 전용라인에 대해서는 최소 수송비용을 고려한다. 제품의 혼합생산 라인의 정보( 생산 Capa.)를 이용하여 나머지 발주량을 생산 의뢰 계획으로 바꾸게 된다. 공장에서는 이 계획을 바탕으로 생산 계획을 수립하여, 그 계획과 생산된 제품을 DC/SB로 출고시킨다. 공 장의 출하 가능량과 각 DC/SB의 2주전의 발주량은 실제 많은 차이가 발생하므로, 이를 재조정하여, 공장의 재고를 최소로 하는 방향으로 공장의 출하 가능량을 조절한다. 조절된 출하 가능량은 2일간의 근거리 수송 원칙에 의해서 입고될 DC/SB를 결정한다. 공장의 출고를 돕기 위해서 배차계획을 수립한다. 본 연구에서는 각각의 Process Engine들의 활동과 정보의 흐름이 서로 상이한 시간 간격으로 발생할 때 각 Process Engine 의 Input/output으로 사용되는 정보의 효율적인 Systematic 동기화 및 Database와 interface 및 Multi-Tasking Database Transaction을 고려할 것이다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.1
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• pp.1-8
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• 2009

This paper aims mainly to reinterpretate Optimal Bus Scheduling Model by applying System Dynamics Perspective. Traditionally, the study regarding Optimal Bus Scheduling Model stems on the linear relationshp. However, this paper attempted to convert linear relationship based Optimal Bus Scheduling Model to causal loop perspective based Model. In result, the paper present Casual Loop Diagram for Optimal Bus Scheduling Model. Furthermore, the paper also ran a simulation based on Stock & Flow Diagram for Optimal Bus Scheduling Model. The outcome was not much different from the linear relationship based Model due to the similarity of the equation applied on two models.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.211-220
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• 2011

TOD(Transit Oriented Development) has recently been active, which presents that TOD planning elements should be comprehensively taken into consideration in order to enhance domestic transit ridership by changing environments in rail station areas and an empirical analysis on the type of rail station areas and transportation demand should be a prerequisite for usage of future development planning. This study aims to grasp a variety of TOD of influence factors in Seoul rail station area and to perform analysis to identify relationship between public transportation demand and these TOD design factors. To make it come true, we gathered data with respect to Density, Diversity, and Accessibility as representative TOD planning elements and carried out factorial and regression analysis. Consequently, we drew 7 influence factors base on factorial analysis: Factor 1(Diversity/ -Use Mix(LUM)), Factor 2(Density/development density), Factor 3(Accessibility/public transportation facility supply), Factor 4(Design/street design), Factor 5(Green/access mode (pedestrian, bike), Factor 6(Design/subway size), Factor 7(Accessibility/Public transit operation) As the result of model development by using factorial and regression analysis, positive influence factors on passenger flow in rail station area are Factor 1(Diversity : Land-Use Mix), Factor 3(Accessibility : public transportation facility supply), Factor 2(Density : development density), Factor 5(Design/ access mode) and Factor 6(subway size) Next, negative influence factor on passenger flow in rail station area shows Factor 7(Accessibility/Public transit operation) as the most influential factor. This is because the growth of service interval of linked subway and bus leads to reduced demand.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.1
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• pp.32-43
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• 2020

The transfer time is an important factor in establishing public transportation planning and policy. Therefore, in this study, the influencing factors of the transfer time for transit users were identified using smart card data, and the estimation results for the transfer time using the deep learning method such as deep neural network models were compared with traditional regression models. First, the intervals and the distance to the bus stop had positive effects on the subway-to-bus transfer time, and the number of bus routes had a negative effect. This also showed that the transfer time is affected by the area in which the subway station exists. Based on the influencing factors of the transfer time, the deep learning models were developed and their estimation results were compared with the regression model. For model performance, the deep learning models were better than those of the regression models. These results can be used as basic data for transfer policies such as the differential application of transit allowance times according to region.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.64-79
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• 2023

This study reviewed the process of applying the Monte Carlo simulation technique to the traffic allocation problem of metropolitan subways. The analysis applied the assumption of a normal distribution in which the travel time information of the inter-station sample is the basis of the probit model. From this, the average and standard deviation are calculated by separating the traffic between stations. A plan was proposed to apply the simulation with the weights of the in-vehicle time of individual links and the walking and dispatch interval of transfer. Long-distance traffic with a low number of samples of 50 or fewer was evaluated as a way to analyze the characteristics of similar traffic. The research results were reviewed in two directions by applying them to the Seoul Metropolitan Subway Network. The travel time between single stations on the Seolleung-Seongsu route was verified by applying random sampling to the in-vehicle time and transfer time. The assumption of a normal distribution was accepted for sample sizes of more than 50 stations according to the inter-station traffic sample of the entire Seoul Metropolitan Subway. For long-distance traffic with samples numbering less than 50, the minimum distance between stations was 122Km. Therefore, it was judged that the sample deviation equality was achieved and the inter-station mean and standard deviation of the transport card data for stations at this distance could be applied.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4D
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• pp.467-472
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• 2009

Even if Seoul City administration improved its public transportation service, transportation model share in seoul has not been increased. Subway user is also decreasing. Therefore, policy transition into TOD(Transit Oriented Development) should be applied in oder to enhance subway modal share. This paper develops a influencing model by using variables of transportation demand and supply. In addition, it provides major influencing factors for users in subway station area and level of transportation supply based on the analysis results. The results show that: first, cluster analysis presents that traffic pattern is proved to be different according to land use characteristics(residence, non-residence); second, main transportation variables such as transferring distance, the number of bus stop, the number of short distant bus lines, and the number of bicycle are more supplied in residential area compared to non-residential areas; third, the number of lines, bus dispatching interval, operating time, and distance between subway stations are more supplied in non-residential areas than residential areas. All in all, the results will be useful for providing priority of considerations in case of decision-making on public transportation policy in subway station area.