• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.454-456
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• 2010

The demands for (a) individual vehicle has been gradually increasing recently due to increase of personal income and spare time. In 2009, the quantities of registered vehicles exceeds over 17,325,210 millions pieces, and the risks of traffic accidents and traffic jam are increasing days by days. It has some limitations to solve the problem of traffic jam by transportation facilities and causes lots of time and costs. For a possible solution, ITS(Intelligent Transport System) has been introduced, but it is an insufficient way for abrupt incidents or risks on roads. The riskiest matter on driving a vehicle is unforeseen situation. In this paper, the most efficient and economical system that communicates with a driver about unexpected accident by sensor network and GPS information, is introduced rather than a traditional method associated with lots of time and costs.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.220-233
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• 2017

Traffic volumes are fundamental data widely used in various traffic analysis, such as origin-and-destination establishment, total traveled kilometer distance calculation, congestion evaluation, and so on. The low number of links collecting the traffic-volume data in a large urban highway network has weakened the quality of the analyses in practice. This study proposes a method to estimate the traffic volume data on a highway link where no collection device is available by introducing a spatial statistic technique with (1) the traffic-volume data from TOPIS, and National Transport Information Center in the Ministry of Land, Infrastructure, and (2) the navigation data from private navigation. Two different component models were prepared for the interrupted and the uninterrupted flows respectively, due to their different traffic-flow characteristics: the piecewise constant function and the regression kriging. The comparison of the traffic volumes estimated by the proposed method against the ones counted in the field showed that the level of error includes 6.26% in MAPE and 5,410 in RMSE, and thus the prediction error is 20.3% in MAPE.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.8
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• pp.900-908
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• 2012

Owing to the proliferation of smart phone users, a proactive spectrum policy is needed in order to deal with increasing data traffic. Therefore, the prediction of frequency requirements for future wireless local area network (WLAN) as well as a licensed cellular communication is necessary. In this paper, we proposed a new prediction method for WLAN spectrum requirements. This method includes both a traditional prediction method and an offloading percentage from cellular network, Also, it can consider a frequency interference between access points using a statistical approach. Based on these approaches, we can predict the spectrum requirements of future domestic WLAN services considering the frequency interference. Finally, we suggest the spectrum policy for WLAN which can prevent spectrum shortage of future WLAN services.

• Journal of Korean Society of Transportation
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• v.14 no.3
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• pp.7-26
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• 1996

The estimation of real-time Origin-Destination(O-D) parameters, which gives travel demand between combinations of origin and destination points on a urban freeway network, from on-line surveillance traffic data is essential in developing an efficient ATMS strategy. On this need a real-time O-D parameter estimation model is formulated as a parameter adaptive filtering model based on the extended Kalman Filter. A Monte Carlo test have shown that the estimation of time-varying O-D parameter is possible using only traffic counts. Tests with field data produced the interesting finding that off-ramp volume predictions generated using a constant freeway O-D matrix was replaced by real-time estimates generated using the parameter adaptive filter.

• Industrial Engineering and Management Systems
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• v.15 no.3
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• pp.259-267
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• 2016

In Wireless Sensor Network, various routing protocols were employed by our Research and Development community to improve the energy efficiency of a network as well as to control the traffic by considering the terms, i.e. Packet delivery rate, the average end-to-end delay, network routing load, average throughput, and total energy consumption. While maintaining network connectivity for a long-term duration, it's necessary that routing protocol must perform in an efficient way. As we discussed Optimized Link State Routing protocol between all of them, we find out that this protocol performs well in the large and dense networks, but with the decrease in network size then scalability of the network decreases. Whenever a link breakage is encountered, OLSR is not able to periodically update its routing table which may create a redundancy problem. To resolve this issue in the OLSR problem of redundancy and predict link breakage, an enhanced protocol, i.e. S-OLSR (More Scalable OLSR) protocol has been proposed. At the end, a comparison among different existing protocols, i.e. DSR, AODV, OLSR with the proposed protocol, i.e. S-OLSR is drawn by using the NS-2 simulator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5028-5057
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• 2015

In this paper, a new mathematical scheme of Macro Handover Management (MHM) in F-HMIPv6 networks based on Location Based Services (LBS) is proposed. Previous schemes based on F-HMIPv6 protocol usually suffer from three major drawbacks: First, They don't exploit the information about the user mobility behavior in order to reduce handover effects. Second, they only focus on the micro mobility level. Third, they don't consider the quality of service (QoS) of the traffic. The proposed MHM scheme avoids these drawbacks using the available information about Mobile Node (MN) such as user mobility patterns and MN's velocity to predict handover and improve network's QoS. It also takes the traffic type in consideration since it presents a major factor in locating QoS for the user. MHM is analyzed and compared with the F-HMIPv6. The results show that MHM improves the performance in terms of packet delivery cost, location update cost, and handover latency. The design of MHM comprises software package in the MN in addition to a hardware part in the network side. It has implications for communication, design, and pricing of mobile services.

• Journal of KIISE:Information Networking
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• v.29 no.2
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• pp.174-180
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• 2002

With the invention of MPLS and the Improvement in traffic engineering, multipath packet forwarding and dynamic load control has become a reality. A dynamic load control, while it can improve network efficiency by controlling loads between paths according to the network state, can lead to unstable and oscillating state because of the staleness of the state information. In this paper, we propose a efficient load control scheme which remains stable. The proposed scheme introduces prediction to reduce the staleness of state message and to prevent oscillation.

• KNOM Review
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• v.23 no.1
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• pp.34-42
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• 2020

Network Function Virtualization (NFV) environment can deal with dynamic changes in traffic status with appropriate deployment and scaling of Virtualized Network Function (VNF). However, determining and applying the optimal VNF deployment is a complicated and difficult task. In particular, it is necessary to predict the situation at a future point because it takes for the process to be applied and the deployment decision to the actual NFV environment. In this paper, we randomly generate service requests in Multiaccess Edge Computing (MEC) topology, then obtain training data for machine learning model from an Integer Linear Programming (ILP) solution. We use the simulation data to train the machine learning model which predicts the optimal VNF deployment in a predefined future point. The prediction model shows the accuracy over 90% compared to the ILP solution in a 5-minute future time point.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.42-48
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• 2011

This paper presents a bridge road surface frost prediction and monitoring system. The node sensing hardware comprises microprocessor, temperature sensors, humidity sensors and Zigbee wireless communication. A software interface is implemented the control center to monitor and acquire the temperature and humidity data of bridge road surface. A bridge road surface frost occurs when the bridge deck temperature drops below the dew point and the freezing point. Measurement data was used for prediction of road surface frost occurrences. The actual alert is performed at least 30 minutes in advance the road surface frost. The road surface frost occurrences data are sent to nearby drivers for traffic accidents prevention purposes.