• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.85-100
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• 2022

Recently, with the widespread expansion of convergence based on digital connectivity, the transportation and mobility fields are rapidly changing, and research related to this is also diversifying. This study aims to analyze the research trends in the mobility field and identify key research areas and topics. Topic modeling analysis has been proved as a useful approach for analyzing the research trends. The abstracts of 142 research papers concerning mobility from the Korean academic citation index were analyzed, derived 9 research topics and linked to 6 key elements of research framework. The result showed that 'Advanced vehicle and transportaion technology' and 'Linkage and integrated services among means for mobility' were most actively studied research fields. It also found that research on insurance, law, regulation for securing user's safety and conflict-resolving with the existing industry has been conducted.

• Journal of IKEEE
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• v.26 no.2
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• pp.287-291
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• 2022

In general, as the amplitude and phase information of the physical layer channel impulse response change rapidly in time and frequency according to the high-speed movement of the vehicles in V2X communication systems, it is difficult to accurately estimate these channels at the receiving end. In order to effectively overcome this problem, midamble-based channel estimation methods in which mid-ambles are periodically inserted into a packet have been recently considered. However, as the number of midambles increases, we suffer from the spectral efficiency loss. To relieve such a loss, in this paper, we propose a new bidirectional averaging channel estimation method that combines the existing data pilot-based channel estimation methods and the mid-ambles. Finally, through the simulation results, we demonstrate that the proposed method outperforms the existing mid-amble method in terms of packet error rate with fewer number of mid-ambles.

• Journal of IKEEE
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• v.26 no.4
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• pp.614-621
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• 2022

This paper proposes RIDS (Random Forest-Based Intrusion Detection), which is an intrusion detection system to detect hacking attack based on random forest. RIDS detects three typical attacks i.e. DoS (Denial of service) attack, fuzzing attack, and spoofing attack. It detects hacking attack based on four parameters, i.e. time interval between data frames, its deviation, Hamming distance between payloads, and its diviation. RIDS was designed in memory-centric architecture and node information is stored in memories. It was designed in scalable architecture where DoS attack, fuzzing attack, and spoofing attack can be all detected by adjusting number and depth of trees. Simulation results show that RIDS has 0.9835 accuracy and 0.9545 F1 score and it can detect three attack types effectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.183-196
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• 2022

Public transportation accessibility has been evaluated through minimum level of service for public transportation. However it is evaluated based operators rather than users. This study analyzed the users' accessibility(first-mile, last-mile) to public transportation using altteul transport card data. As a result of user's accessibility of public transportation, rural areas was lower than that in the urban areas. This study calssified type 1 and 2 based average approach time, and average approach time of Type 1 and 2 were more than average approach time of total area. We propsed an efficient introduction of autonomous public transportation on-demand service using delphi survey. As a result of delphi survey, experts agreed on 9 items regarding function, service item, route operation, approach distance, route mileage, punctuality.

• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.104-111
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• 2023

Rolling stock maintenance, which focuses on preventive maintenance, is typically implemented considering the potential harm that may be inflicted to passengers in the event of failure. The cost of preventive maintenance throughout the life cycle of a rolling stock is 60%-75% of the initial purchase cost. Therefore, ensuring stability and reducing maintenance costs are essential in terms of economy. In particular, private railroad operators must reduce government support budget by effectively utilizing railroad resources and reducing maintenance costs. Accordingly, this study analyzes the reliability characteristics of components using field data. Moreover, it resolves the problem of determining an economical replacement interval considering the timing of scrapping railroad vehicles. The procedure for determining the optimal replacement interval involves five steps. According to the decision model, the optimal replacement interval for the onboard signal device components of the "A" line train is calculated using field data, such as failure data, preventive maintenance cost, and failure maintenance cost. The field data analysis indicates that the mileage meter is 9 years, which is less than the designed durability of 15 years. Furthermore, a life cycle in which the phase signal has few failures is found to be the same as the actual durability of 15 years.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.321-327
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• 2023

With the development of artificial intelligence, the prediction system has become one of the essential technologies in our lives. Despite the growth of these technologies, traffic congestion at intersections in the 21st century has continued to be a problem. This paper proposes a system that predicts intersection traffic jams using a Convolutional LSTM (Conv-LSTM) algorithm. The proposed system models data obtained by learning traffic information by time zone at the intersection where traffic congestion occurs. Traffic congestion is predicted with traffic volume data recorded over time. Based on the predicted result, the intersection traffic signal is controlled and maintained at a constant traffic volume. Road congestion data was defined using VDS sensors, and each intersection was configured with a Conv-LSTM algorithm-based network system to facilitate traffic.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.302-302
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• 2021

지하차도의 경우 국지 및 돌발홍수가 발생할 경우 대부분 침수됨에도 불구하고 2020년 7월 23일 부산 지역에 밤사이 시간당 80mm가 넘는 폭우가 발생하면서 순식간에 지하차도 천장까지 물이 차면서 선제적인 차량 통제가 우선적으로 수행되지 못하여 미처 대피하지 못한 3명의 운전자 인명사고가 발생하였다. 수재해를 비롯한 재난 관리를 빠르게 수행하기 위해서는 기존의 정부 및 관주도 중심의 단방향의 재난 대응에서 벗어나 정형 데이터와 비정형 데이터를 총칭하는 빅데이터의 통합적 수집 및 분석을 수행이 필요하다. 본 연구에서는 부산지역의 지하차도와 인접한 지하터널 CCTV 자료(센서)를 통한 재난 발생 시 인명피해를 최소화 정보 제공을 위한 Object Detection(객체 탐지)연구를 수행하였다. 지하터널 침수가 발생한 부산지역의 CCTV 영상을 사용하였으며, 영상편집에 사용되는 CCTV 자료의 음성자료를 제거하는 인코딩을 통하여 불러오는 영상파일 용량파일 감소 효과를 볼 수 있었다. 지하차도에 진입하는 물체를 탐지하는 방법으로 YOLO(You Only Look Once)를 사용하였으며, YOLO는 가장 빠른 객체 탐지 알고리즘 중 하나이며 최신 GPU에서 초당 170프레임의 속도로 실행될 수 있는 YOLOv3 방법을 적용하였으며, 분류작업에서 보다 높은 Classification을 가지는 Darknet-53을 적용하였다. YOLOv3 방법은 기존 객체탐지 모델 보다 좀 더 빠르고 정확한 물체 탐지가 가능하며 또한 모델의 크기를 변경하기만 하면 다시 학습시키지 않아도 속도와 정확도를 쉽게 변경가능한 장점이 있다. CCTV에서 오전(일반), 오후(침수발생) 시점을 나눈 후 Car, Bus, Truck, 사람을 분류하는 YOLO 알고리즘을 적용하여 지하터널 인근 Object Detection을 실제 수행 하였으며, CCTV자료를 이용하여 실제 물체 탐지의 정확도가 높은 것을 확인하였다.

• Korean Security Journal
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• no.19
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• pp.67-103
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• 2009

Objectives of this study is develop security services through determinants analysis on the efficiency of security works regarding security and guarding business in Korea because nowadays the modern society like Korea let alone all over the world faces the increase of dangerous factors in every security field of the human societies, and also it is the very present situation that an individual's life even the national security itself can be at the risk without guaranteeing the efficiency of the security services. For this purpose, this study reviewed related documents, surveyed and interviewed security personnels to identify what the potentially influential factors are in both the public and private security organizations regarding the efficiency of present security services and organizations, and what differences are. Also, comparing the public and private security sectors, this study intended to suggest policy agendas how to enhance the efficiency of security services in the future. This study surveyed the 177 agents and former agents of the Presidential Security Service(PSS) for the public security sector, and also surveyed, interviewed, and internet-based polled 821 randomly selected personnels for the private security sector. This research showed that regarding the efficiency of the security services number of independent variables which had positive responses in the public security sector was more than that in the private security sector. Among the 21 questions regarding this issue, there were all of 21 positive responses in the public security sector while there were 18 negative responses in the private security sector. As a result of synthesizing all the answers of the both sides, it is possible to understand that mostly the ratio of the positive response was much higher. In the public security service, statistically significant variables were budget support for events, prior access of information, an integrated teamwork training, organizational atmosphere, morale of organization personnel. However, practical training of the security service and mutual communication showed unexpectedly negative(-) signs. In the private security service, statistically significant variables were budget support for events, integrated teamwork training, socially friendly atmosphere, compensation for the personnels, bullet-proof equipments and vehicles, mood of organization, personnel recruit and disposition, unexpected incidents and basic attitude for security services. In sum, while organizational personnel variables and organizational management variables were significant in the public security service, some organizational management variables and all socio-environment variables were statistically significant at 5% significance level.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.