• Journal of Korean Society of Transportation
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• v.12 no.1
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• pp.97-116
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• 1994

Today advanced traffic management systems are required because of a high increase in traffic demand. Accordingly, the objective of this study is to take advantage of image processing systems and present image processing methods available for collection of the data on traffic characteristics, and then to investigate the possibility of traffic flow analysis by means of comparison and analysis of measured traffic flow. Data were collected at two places of Daegu city and Kyongbu expressway by using VTR. Rear view (down stream) and frontal view (up stream) methods were employed to compare and analyze traffic characteristics including traffic volume, speed, time-headway, time-occupancy, and vehicle-length, by analysis of measured traffic flow and image processing respectively. Judging from the results obtained by this study, image processing techniques are sufficient for the analysis of traffic volume, but a frame grabber equipped with high speed processor is necessary as well, with low level system judged to be sufficient for traffic volume analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12A
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• pp.1246-1252
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• 2006

In this paper, a collision probability, processing time and traffic capacity analysis algorithm for RFID system using random FHSS and synchronous FHSS is proposed. Service time, duty cycle, traffic intensity and additional delay time required for re-transmission due to collision are considered and the processing delay and frequency channel capacity are analyzed for the steady state operation of the system. The simulation results which show maximum capacity of the system and explain the accuracy of the algorithm are provided.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.951-969
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• 2017

In this paper, we newly propose a traffic information service model that collects traffic information sensed by an individual vehicle in real time by using a smart device, and which enables drivers to share traffic information on all roads in real time using an application installed on a smart device. In particular, when the driver requests traffic information for a specific area, the proposed driver-personalized service model provides him/her with traffic information on the driving directions in advance by predicting the driving directions of the vehicle based on the learning of the driving records of each driver. To do this, we propose a traffic information management model to process and manage in real time a large amount of online-generated traffic information and traffic information requests generated by each vehicle. We also propose a road node-based indexing technique to efficiently store and manage location-based traffic information provided by each vehicle. Finally, we propose a driving learning and prediction model based on the hidden Markov model to predict the driving directions of each driver based on the driver's driving records. We analyze the traffic information processing performance of the proposed model and the accuracy of the driving prediction model using traffic information collected from actual driving vehicles for the entire area of Seoul, as well as driving records and experimental data.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.737-740
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• 1999

This paper deals with a real time optimization algorithm within real time for DRGS(Dynamic Route Guidance System) and evaluate the algorithm. A pre-developed system offers the optimal route in using only static traffic information. In using real-time traffic information, Dynamic route guidance algorithm is needed. The serious problem in implementing it is processing time increase as nodes increase and then the real time processing is impossible. Thus, in this paper we propose the optimal route algorithm with window mechanism for the real-time processing and then evaluate the algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12A
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• pp.1910-1916
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• 1999

This paper suggested to solve ATM switch performance and service rate which was input buffer managed scheme in ATM network with burst traffic characteristics, For this purpose, ATM multiplexer is prepared before sending for handling burst random input traffic to multiplex and then sort based on cell inter-arrival time and cell processing due time which had been marked after that. The server looks for cell header with the most shortest due time and sends it, thus it is satisfied that real time traffic for instance CBR and rt-VBR was guaranteed cell processing time to send fast than non real time traffic. For analysis of ATM switch performance with cell processing due time and priority, each output port has divided into four different virtual buffer and each buffer has assigned different cell inter-arrival time and processing due time according to ATM Forum for example CBT/rt-VBR, nrt-VBR, ABR and UBR and showed it’s optimal service parameters then analyzed service rate behaviors according to each traffic characteristics.

• Journal of Information Processing Systems
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• v.7 no.1
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• pp.1-16
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• 2011

With the increasing number of vehicles in use in our daily life and the rise of traffic congestion problems, many methods and models have been developed for real time optimisation of traffic lights. Nevertheless, most methods which consider real time physical queue sizes of vehicles waiting for green lights overestimate the optimal cycle length for such real traffic control. This paper deals with the development of a generic hybrid model describing both physical traffic flows and control of signalised intersections. The firing times assigned to the transitions of the control part are considered dynamic and are calculated by a simplified optimisation method. This method is based on splitting green times proportionally to the predicted queue sizes through input links for each new cycle time. The proposed model can be easily translated into a control code for implementation in a real time control system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.1
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• pp.100-108
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• 2016

In this paper, We propose a method which prevents severe traffic jam by controlling traffic signal by itself based on image-processed information and fuzzy logic. The detailed idea of this method is first to let a closed monitoring camera gather the number of cars which show the flow of traffic the designated roads which are commonly considered to have traffic. After executing the image processing method on each image gathered from the monitoring camera, this system determines the changing timing of traffic signal based on fuzzy logic. Also, this image processing method shows good performance in real road environment because the setup background image which used in this system is designed to be updated in real time. All of good points mentioned above would lead driver and users to cost efficient and time efficient results by preventing the increase of the number of traffic on road in advance with the automatic traffic signal controlling algorithm based on the fuzzy logic.

• The Transactions of the Korea Information Processing Society
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• v.7 no.10
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• pp.3195-3200
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• 2000

this paper is a stucy onthe preductionof multi-media traffic flow for the realizationof optimum ATM congestion control. In ATM network it is expected that the characteristic of multi-media traffic flow is varied slowly with a time. Fjor the simulation, time-variable multi-media traffic is penerated using possion distribution(connect calls per process time).\, gamma distribution(transmission rate per a call) and exponential distribution(holding time per a call). And using back-propagation neural netwok and proposed tripple neural network, the simulation to predict generaed traffic is executed. From the result,it's capability is shown that the proposed neural network model can be used in the predictionof ATM traffic flow.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.498-506
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• 2003

A computer vision system applied to an intelligent safety vehicle has been required to be worked on a small sized real time special purposed hardware not on a general purposed computer. In addition, the system should have a high reliability even under the adverse road traffic environment. This paper presents a design and an implementation of an onboard hardware system taking into account for high speed image processing to analyze a road traffic scene. The system is mainly composed of two parts: an early processing module of FPGA and a postprocessing module of DSP. The early processing module is designed to extract several image primitives such as the intensity of a gray level image and edge attributes in a real-time Especially, the module is optimized for the Sobel edge operation. The postprocessing module of DSP utilizes the image features from the early processing module for making image understanding or image analysis of a road traffic scene. The performance of the proposed system is evaluated by an experiment of a lane-related information extraction. The experiment shows the successful results of image processing speed of twenty-five frames of 320$\times$240 pixels per second.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.362-364
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• 2018

Predicting network traffic volume has become a popular topic recently due to its support in many situations such as detecting abnormal network activities and provisioning network services. Especially, predicting the volume of the next upcoming traffic from the series of observed recent traffic volume is an interesting and challenging problem. In past, various techniques are researched by using time series forecasting methods such as moving averaging and exponential smoothing. In this paper, we propose a long short-term memory neural network (LSTM) based network traffic volume prediction method. The proposed method employs the changing rate of observed traffic volume, the corresponding time window index, and a seasonality factor indicating the changing trend as input features, and predicts the upcoming network traffic. The experiment results with real datasets proves that our proposed method works better than other time series forecasting methods in predicting upcoming network traffic.