• The Korean Journal of Archival Studies
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• no.11
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• pp.181-214
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• 2005

Metropolitan rapid transit system plays an essential role in the public transportation system of any large city, and its managing agency is usually charged with the responsibility of storing and managing the design drawings of the system. The drawings are important and historically valuable documents that must be kept permanently because they contain comprehensive data that is used to manage and maintain the system. However, no study has been performed in Korea on how well agencies are preserving and managing these records. Seoul Metropolitan Rapid Transit Corporation(SMRT) is the managing agency established by the city of Seoul to operate subway lines 5, 6, 7, and 8 more efficiently to serve its citizens. By the Act on Records Management in Public Institutions(ARMPI), SMRT should establish a records center to manage its records. Furthermore, all drawings produced by SMRT and other third party entities should be in compliance with the Act. However, SMRT, as a form of local public corporation, can establish a records center by its own way. Accordingly, the National Archives & Records Service(NARS) has very little control over SMRT. Therefore, the purpose of this study is to research and analyze the present state of storage and management of the drawings of metropolitan rapid transit in SMRT and is to find a desirable method of preservation and management for drawings of metropolitan rapid transit. In the process of the study, it was found that a records center is being considered to manage only general official documents and not to manage the drawings as required by ARMPI. SMRT does not have a records center, and the environment of management on the drawings is very poor. Although there is a plan to develop a new management system for the drawings, it will be non-compliant of ARMPI. What's happening at SMRT does not reflect the state of all other cities' metropolitan rapid transit records management systems, but the state of creation of records center of local public corporation is the almost same state as SMRT. There should be continuous education and many studies conducted in order to manage the drawings of metropolitan rapid transit efficiently by records management system. This study proposes a records center based on both professional records centers and union records centers. Although metropolitan rapid transit is constructed and managed by each local public corporation, the overall characteristics and processes of metropolitan rapid transit projects are similar in nature. In consideration of huge quantity, complexity and specialty of drawings produced and used during construction and operation of metropolitan rapid transit, and overlap of each local public corporation's effort and cost of the storage and management of the drawings, they need to be managed in a professional and united way. As an example of professional records center, there is the National Personnel Records Center(NPRC) in St. Louis, Missouri. NPRC is one of the National Archives and Records Administration's largest operations and a central repository of personnel-related records on former and present federal employees and the military. It provides extensive information to government agencies, military veterans, former federal employees, family members, as well as researchers and historians. As an example of union records center, there is the Chinese Union Dangansil. It was established by several institutions and organizations, so united management of records can be performed and human efforts and facilities can be saved. We should establish a professional and united records center which manages drawings of metropolitan rapid transit and provides service to researchers and the public as well as members of the related institutions. This study can be an impetus to improve interest on management of not only drawings of metropolitan rapid transit but also drawings of various public facilities.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.