• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.109-116
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• 2020

With the advent of high-speed railways, railways are one of the most frequently used means of transportation at home and abroad. In addition, in terms of environment, carbon dioxide emissions are lower and energy efficiency is higher than other transportation. As the interest in railways increases, the issue related to railway safety is one of the important concerns. Among them, visual abnormalities occur when various obstacles such as animals and people suddenly appear in front of the railroad. To prevent these accidents, detecting rail tracks is one of the areas that must basically be detected. Images can be collected through cameras installed on railways, and the method of detecting railway rails has a traditional method and a method using deep learning algorithm. The traditional method is difficult to detect accurately due to the various noise around the rail, and using the deep learning algorithm, it can detect accurately, and it combines the two algorithms to detect the exact rail. The proposed algorithm determines the accuracy of railway rail detection based on the data collected.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.246-253
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• 2018

The main industries in Chungcheongbuk-do are the smart IT parts; bio-health; and transportation machinery, materials and parts. The transportation, machinery, materials and parts (TMMP) industry is a key source technology for environmentally friendly and intelligent vehicles related to future vehicles in the machinery sector. Business support projects to improve this industry are currently underway. In this study, the number of enterprises, number of employees, amount of shipment, and added value of the enterprises in Chungbuk area were analyzed for the downstream and rear industries of the transportation, machinery, materials, and parts industry to set targets and directions for the technical support projects during the enterprise support. In particular, for the SMEs with less than 50 employees, the changes in the value added per enterprise were analyzed according to the period from the year of foundation. In the case of the rear industry, the value added by companies within three years after foundation has decreased gradually each year, so technical support is needed at the beginning of the startup. Companies between the ages of five and nine years are showing a fluctuating trend each year. Therefore, it is necessary to support business diversification or find new items.

• International Journal of Contents
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• v.14 no.1
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• pp.39-44
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• 2018

Today, cars have developed into intelligent automobiles that combine advanced control equipment and IT technology to provide driving assistance and convenience to users. These vehicles provide infotainment services to the driver, but this does not improve the safety of the driver. Accordingly, V2X communication, which forms a network between a vehicle and a vehicle, between a vehicle and an infrastructure, or between a vehicle and a human, is drawing attention. Therefore, various techniques for improving channel estimation performance without changing the IEEE 802.11p standard have been proposed, but they do not satisfy the packet error rate (PER) performance required by the C-ITS service. In this paper, we analyze existing channel estimation techniques and propose a new channel estimation scheme that achieves better performance than existing techniques. It does this by applying the updated matrix for the data pilot symbol to the construct data pilot (CDP) channel estimation scheme and by further performing the interpolation process in the frequency domain. Finally, through simulations based on the IEEE 802.11p standard, we confirmed the performance of the existing channel estimation schemes and the proposed channel estimation scheme by coded PER.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.51-67
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• 1996

The optimal path-finding problem becomes complicated when multiple variables are simultaneously considered such as physical route length, degree of congestion, traffic capacity of intersections, number of intersections and lanes, and existence of free ways. Therefore, many researchers in various fields (management science, computer science, applied mathematics, production planning, satellite launching) attempted to solve the problem by ignoring many variables for problem simplification, by developing intelligent algorithms, or by developing high-speed hardware. In this research, an integration of expert system technique and case-based reasoning in high level with a conventional algorithms in lower level was attempted to develop an optimal path-finding system. Early application of experienced driver's knowledge and case data accumulated in case base drastically reduces number of possible combinations of optimal paths by generating promising alternatives and by eliminating non-profitable alternatives. Then, employment of a conventional optimization algorithm provides faster search mechanisms than other methods such as bidirectional algorithm and $A^*$ algorithm. The conclusion obtained from repeated laboratory experiments with real traffic data in Seoul metropolitan area shows that the integrated approach to finding optimal paths with consideration of various real world constraints provides reasonable solution in a faster way than others.

• Journal of Korean Society of Transportation
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• v.11 no.1
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• pp.55-66
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• 1993

본 논문에서는 최근에 소개된 일반화중력모형(Generalized Gravity Model)파라메터의 최우추정치(Maximum Likelihood Estimates) 계산을 위한 새로운 알고리즘을 이론적으로 도출하였다. 개발된 알고리즘은 첫째 계산속도, 둘째 정밀도, 셋째 모형변수(예컨데 통행시간, 통행비용 등)들 간에 공선성(multicolinearity)이 존재할 경우의 계산능력, 넷째 대규모 스케일의 기.종점자료(large O-D Matrices)에 적용시의 계산능력, 다섯째 모형변수의 개수에 따른 계산능력의 평가기준에서 그 계산실적이 기존의 알고리즘과 비교 평가 되었다. 제안된 기법중에서 Modified Scoring 기법은 계산속도 및 정밀도등 앞서 나열한 계산능력의 평가기준 중 모든 부문에서 매우 탁월한 계산실적을 보이는 것으로 판명되었다. 따라서 최선의 추정치를 보장하는 최우추정기법이 대규모 스케일의 교통계획 적용에도 큰 비용(시간)부담없이 손쉽게 적용될 수 있게 되었다. 제안된 새로운 알고리즘의 적용시 교통계획분야에 가져올 수 있는 기대효과는 다음과 같다. 첫째, 최우추정법이 대규모 O-D 통행표에 쉽게 적용될 수 있고 또한 PC등 소형 컴퓨터에서도 처리가 쉽다. 둘째, 모형설명변수의 자유로운 선택등 통계적실험(experimentation)을 가능케 한다. 셋째, 중력모형이 내재되어 있는 결합모형(Combined Model)의 정산속도를 높인다. 넷째, IVHS(Intelligent Vehicle and Highway System)와 같은 분야에서 온라인(On-line)모형정산을 가능케 할 수 있다.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.759-762
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• 2005

Recently, Intelligent Transportation System(ITS) has been applied to satisfy increasing traffic demand every year and to solve many traffic problems. Especially, Advanced Traveller Information System(ATIS) is a transportation system to optimize the trip of each other vehicle. It is important to provide the driver with quick and comfortable path from source to destination. However, it is difficult to provide a shortest path in a road network with dynamic cost. Because the existing research has a static cost. Therefore, in this paper we propose an operator for searching traffic dependent shortest path. The proposed operator finds the shortest path from source to destination using a current time cost and a difference cost of past time cost. Such a method can be applied to the road status with time. Also, we can expect a predicted arrival time as well as the shortest path from source to destination. It can be applied to efficiently application service as ITS and have the advantages of using the road efficiently, reducing the distribution cost, preparing an emergency quickly, reducing the trip time, and reducing an environmental pollution owing to the saving the fuel.

• Journal of Korean Society of Transportation
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• v.16 no.2
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• pp.77-90
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• 1998

첨단교통관리시스템, 첨단교통정보시스템, 첨단대중교통시스템, 첨단화물운송시스템, 첨단차량 및 도로시스템으로 구성된 한국형 ITS는 적절히 설계, 전개, 운용된다면, 교통체증, 연료, 비용, 공해, 사고 등의 감소와 함께 운전자의 수행도 및 효율성의 향상을 꾀할 수 있다. 그러나, 이러한 목적을 달성하기 위해서는 운전자 및 조작자의 새로운 기술과 시스템들에 대한 중요성 및 시스템의 통합이 중요한 요건으로 작용되며, 인간공학은 이러한 과정에 있어 운전자 및 시스템조작자의 물리적, 인지적 한계를 규명하고 이를 시스템 설계에 반영하는 설계 가이드라인 작성에서 실제 평가까지의 과정에 중요한 역할을 하고 있다. 즉, TMC, AVHS 등의 시스템과 함께 주요한 차내 정보시스템인 항법장치, HUD, RDS-TMC등에 걸쳐 널리 적용되고 있으며, 인간공학은 각각의 시스템뿐만 아닌 전체 ITS의 성공적인 시장성 확보와 전개에도 중요한 영향을 미치는 중요한 요인으로 간주되고 있다. 또한, ISO TC 204 등에 의해 수용되고 있는 인간공학 표준화작업은 장차 ITS의 국제시장 진출 및 산업화를 위한 중요한 문제로 대두되고 있는 실정이다. 이에 본 연구는 국내외 TS에서의 이간공학 적용 및 표준화 동향을 제시함으로써, 한국형 ITS의 인간공학 적용 및 표준화 연구를 위한 기반적인 내용의 제공과 함께, 국내의 연구 및 표준화 작업을 위한 방안을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1975-1988
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• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.2 s.10
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• pp.44-54
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• 2006

The share rate of bus mode has been decreasing continuously. To solve this problem, the local governments implement Bus Management System(BMS) or Bus Impormation System(BIS). In operating the public transportation preferential policy, it is very important to verify the effectiveness of the projects. This study performed the effectiveness of BMS through before and after study based on the field data. Moreover, this study analyzed the factors affecting to BMS. We anticipate that the results of this study would be useful reference in local government's BMS planning.