• Journal of the Korea Furniture Society
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• v.23 no.4
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• pp.467-478
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• 2012

Interurban railway is expanded by transportation that population can be centralism and cope efficiently in traffic jam by augmentation of vehicles in the city. Railroad chair departs in space of an existent simple rest and products that can raise human body standard and size by powerful engineering vicinity, quality of the material, psychological satisfaction etc. of users are developed steadily. Specially, study of space that can use various materials efficiently with bodily size government official because various of man and woman old and the young etc.. are using in occasion of the train is urgently required. Specially, railroad chair is a product that material and standard, structure, human body engineering, a finish etc. is studied variously. In the case of advanced nation, in case of design a chair operating time of railroad, is placed as all standards orderly and harmonious because is considered exercise dynamics etc.. and designs. The other side, in the case of our country, study expert of chair is short real and is depending on technological data of overseas railway vehicle. Chair for railroad must consider removal of short-range, long distance. That have to be consider to a lot of uncomfortable such as psychological satisfaction of user because the domestic Motor Companies are defining in fair development study and many researchers but the railroad chair company are not accumulated professional manpower and technological know-how. Railway vehicle can recognize that overseas visitors as well as native is important element as space that space is exposed internationally by product that Public personality which used cultural value is strong. Therefore, wish to plan valid spec relationship presentation of various design and specification, function etc.. and contribute to railroad chair development design process lists and analyzes van instances of railway vehicle chair of inside and outside of the country through this study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.57-62
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• 2014

Recently, research activities to develop services for providing safety information to the drivers in fast moving vehicles based on various wireless network technologies such as DSRC (Dedicated Short Range Communication), IEEE 802.11p WAVE (Wireless Access for Vehicular Environment) are widely being carried out. This paper presents a proof-of-concept model based on a robot-car for Dilemma Zone Decision Assistant Service using the wireless LAN technology. The proposed model system consists of a robot-car based on an embedded Linux OS equipped with a WiFi interface and an on-board unit emulator, an Android-based remote controller to model a human driver interface, a laptop computer to run a model traffic signal controller and signal lights, and a WiFi access point to model a road-side unit.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.215-223
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• 2019

Driving after drinking is highly likely to cause accidents due to lack of responsibility and poor judgment. South Korea is seeking a sentence for accidental drinking accidents. The suspended jail term for drunk drivers accounts for more than 70 percent of the total. However, those who come to the probation office during the probation period are involuntary, subject to little motivation for change, and the large-scale, collective, and short-term education offered to them is limited in preventing re-off. In addition to small group and long-term education, we conducted intensive short-term interventions to see changes in drinking habits in three months. In the long run, the effectiveness of drinking control will be demonsstrated, making a difference in improving the programs offered to drunk drivers in the future. Drunk driving accouts for a very high percentage of the causes of traffic accidents, which, like many countries around the world, should make efforts to prevent and punish drunk driving.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.777-781
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• 2004

This paper presents an algorithm for multichannel slotted-ring topology medium access protocol (MAC) using in wavelength division multiplexing (WDM) networks. In multichannel ring, there are two main previously proposed architectures: Tunable Transmitter - Fixed Receiver (TTFR) and Fixed Transmitter - Tunable Receivers (FTTR). With TTFR, nodes can only receive packets on a fixed wavelength and can send packets on any wavelengths related to destination of packets. Disadvantage of this architecture is required as many wavelengths as there are nodes in the network. This is clearly a scalability limitation. In contrast, FTTR architecture has advantage that the number of nodes can be much larger than the number of wavelength. Source nodes send packet on a fixed channel (or wavelength) and destination nodes can received packets on any wavelength. If there are fewer wavelengths than there are nodes in the network, the nodes will also have to share all the wavelengths available for transmission. However the fixed wavelength approach of TTFR and FTTR bring low network utilization. Because source node with waiting data have to wait for an incoming empty slot on corresponding wavelength. Therefore this paper presents Tunable Transmitter - Tunable Receiver (TTTR) approach, in which the transmitting node can send a packet over any wavelengths and the receiving node can receive a packet from any wavelengths. Moreover, the self-similar distributed input traffic is used for evaluation of the performance of the proposed algorithm. The self-similar traffic performs better performance over long duration than short duration of the Poison distribution. In order to increase bandwidth efficiency, the Destination Stripping approach is used to mark the slot which has already reached the desired destination as an empty slot immediately at the destination node, so the slot does not need to go back to the source node to be marked as an empty slot as in the Source Stripping approach. MATLAB simulator is used to evaluate performance of FTTR, TTFR, and TTTR over 4 and 16 nodes ring network. From the simulation result, it is clear that the proposed algorithm overcomes higher network utilization and average throughput per node, and reduces the average queuing delay. With future works, mathematical analysis of those algorithms will be the main research topic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.151-157
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• 2018

Among the various sensors for measuring traffic, PVDF (polyvinylidene fluoride) piezoelectric sensors are used to classify vehicles because they can detect the axle of the vehicle. Piezoelectric sensors are embedded in road pavements and are always exposed to traffic loads and environmental loads. Therefore, the life expectancy is very short, less than 6 years. Traffic control is essential for reinstallation and data collection is interrupted during the failure period. The lifespan will increase if the sensor installation depth is increased. In this study, the sensor signal output was analyzed with a variable depth of sensor installation to verify the possibility of deeper installation. Furthermore, various parameters, such as the weight and speed, were analyzed. The wheel load is applied using APT. As a result, the MSI BL sensor output signal is higher than 100mV when installed at 3cm, which is reliable. If the location of the sensor is deeper in the pavement, the expected lifetime of the sensor is also increased. On the other hand, the MSI cable was found to be less than 100mV at the shallowest depth of 1cm, making it impossible for field applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.47-60
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• 2017

Car sharing, which is considered as a good example of innovation in the transportation sector, is a type of use in which a plurality of people share a single vehicle for a short period of time. It is divided into various types, NFP (Not For Profit) operated by a non-profit organization, B2C (Business to Customer), which is operated by a company, and Peer to Peer (P2P), which is directly connected to an individual. Among them, P2P car sharing is a method of sharing personal vehicles owned by individuals. It has the merit of reducing traffic congestion and providing for efficient traffic demand management by reducing the purchase rate of additional vehicles and minimizing the number of idle ones. This study examines the introduction of P2P car sharing in order to develop a traffic demand management policy and facilitate the transition to a sustainable transportation system. The spatial extent of the study consisted of apartment complexes in Seoul. In apartment complexes, it is possible to minimize the level of expenditure, such as operating expenses, by utilizing the APT management office and there is no difficulty in securing the necessary parking space. Therefore, apartment complexes were selected as the spatial range.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.249-257
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• 2017

Filtering the data for travel time records obtained from DSRC probes is essential for a better estimation of the link travel time. This study addresses the major deficiency in the performance of TRANSGUIDE in removing anomalous data. This algorithm is unable to handle unstable traffic flow conditions for certain time intervals, where fluctuations are observed. In this regard, this study proposes an algorithm that is capable of overcoming the weaknesses of TRANSGUIDE. If TRANSGUIDE fails to validate sufficient number of observations inside one time interval, another process specifies a new validity range based on the median absolute deviation (MAD), a common statistical approach. The proposed algorithm suggests the parameters, ${\alpha}$ and ${\beta}$, to consider the maximum allowed outlier within a one-time interval to respond to certain traffic flow conditions. The parameter estimation relies on historical data because it needs to be updated frequently. To test the proposed algorithm, the DSRC probe travel time data were collected from a multilane highway road section. Calibration of the model was performed by statistical data analysis through using cumulative relative frequency. The qualitative evaluation shows satisfactory performance. The proposed model overcomes the deficiency associated with the rapid change in travel time.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.348-359
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• 2017

Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1873-1879
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• 2014

There are various methodologies to forecast the travel time using real-time data but the K-nearest neighborhood (KNN) method in general is regarded as the most one in forecasting when there are enough historical data. The objective of this study is to evaluate applicability of KNN method. In this study, real-time and historical data of toll collection system (TCS) traffic flow and the dedicated short range communication (DSRC) link travel time, and the historical path travel time data are used as input data for KNN approach. The proposed method investigates the path travel time which is the nearest to TCS traffic flow and DSRC link travel time from real-time and historical data, then it calculates the predicted path travel time using weight average method. The results show that accuracy increased when weighted value of DSRC link travel time increases. Moreover the trend of forecasted and real travel times are similar. In addition, the error in forecasted travel time could be further reduced when more historical data could be available in the future database.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).