• Journal of Information Technology Services
• /
• v.20 no.1
• /
• pp.81-92
• /
• 2021

As the degree of economic development of society increases, the maintenance issues on the existing social overhead capital becomes essential. Accordingly, the adaptation of the concept of Level of service in highway maintenance is indispensable. It is also crucial to manage and perform the service level such as road assets to provide universal services to users. In this regards, the purpose of this study is to improve the maintenance service rating model and to focus on the assessment items and weights among the improvements. Particularly, in determining weights, an Analytic Hierarchy Process (AHP) is performed based on the survey response results. After then, this study conducts unsupervised neural network models such as Self-Organizing Map (SOM) and Davies-Bouldin (DB) Index to divide proper sub-groups and determine priorities. This paper identifies similar cases by grouping the results of the responses based on the similarity of the survey responses. This can effectively support decision making in general situations where many evaluation factors need to be considered at once, resulting in reasonable policy decisions. It is the process of using advanced technology to find optimized management methods for maintenance.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.2
• /
• pp.33-42
• /
• 2022

In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time. In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.

• Journal of the Korean Regional Science Association
• /
• v.34 no.1
• /
• pp.31-47
• /
• 2018

This study examines the locational characteristics and change of business type in the residental areas that have commercial gentrification issues in Seoul, Korea, using the logistic regression model. The analysis results indicate that the gentrification occurrence areas are strongly associated with low-density and old residential areas. In addition, those areas are more likely to have great accessibilities to highway ramp, subway station, colleges, and other facilities that attract people. Regarding the characteristics of the road, gentrification occurrence areas are associated with longer road length, lower rate of road areas, higher local integration of road network, and higher rate of three-way intersections. This finding indicates that low-density and old residential areas with organic road networks have strong links with commercial gentrification. This study also finds that the business type has been substantially changed from 2006 to 2014 in the commercially gentrified residential areas. While the coffee shops and drinking places have been increased, but neighborhood-living facilities have been decreased. This study also shows that the business life-cycles of drinking places or Korean restaurant are getting short. Finally, this study discusses the commercial gentrification issues and policy implications in the residential districts in Seoul, Korea.

• Journal of Communications and Networks
• /
• v.3 no.4
• /
• pp.383-395
• /
• 2001

Recent measurement and simulation studies have revealed that wide area network traffic displays complex statistical characteristics-possibly multifractal scaling-on fine timescales, in addition to the well-known properly of self-similar scaling on coarser timescales. In this paper we investigate the performance and network engineering significance of these fine timescale features using measured TCP anti MPEG2 video traces, queueing simulations and analytical arguments. We demonstrate that the fine timescale features can affect performance substantially at low and intermediate utilizations, while the longer timescale self-similarity is important at intermediate and high utilizations. We relate the fine timescale structure in the measured TCP traces to flow controls, and show that UDP traffic-which is not flow controlled-lacks such fine timescale structure. Likewise we relate the fine timescale structure in video MPEG2 traces to sub-frame encoding. We show that it is possibly to construct a relatively parsimonious multi-fractal cascade model of fine timescale features that matches the queueing performance of both the TCP and video traces. We outline an analytical method ta estimate performance for traffic that is self-similar on coarse timescales and multi-fractal on fine timescales, and show that the engineering problem of setting safe operating points for planning or admission controls can be significantly influenced by fine timescale fluctuations in network traffic. The work reported here can be used to model the relevant characteristics of wide area traffic across a full range of engineering timescales, and can be the basis of more accurate network performance analysis and engineering.

• International Journal of Computer Science & Network Security
• /
• v.24 no.8
• /
• pp.14-20
• /
• 2024

Traffic sign recognition is an essential feature of intelligent transportation systems and Advanced Driver Assistance Systems (ADAS), which are necessary for improving road safety and advancing the development of autonomous cars. This research investigates the incorporation of the YOLOv9 model into traffic sign recognition systems, utilizing its sophisticated functionalities such as Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) to tackle enduring difficulties in object detection. We employed a publically accessible dataset obtained from Roboflow, which consisted of 3130 images classified into five distinct categories: speed_40, speed_60, stop, green, and red. The dataset was separated into training (68%), validation (21%), and testing (12%) subsets in a methodical manner to ensure a thorough examination. Our comprehensive trials have shown that YOLOv9 obtains a mean Average Precision (mAP@0.5) of 0.959, suggesting exceptional precision and recall for the majority of traffic sign classes. However, there is still potential for improvement specifically in the red traffic sign class. An analysis was conducted on the distribution of instances among different traffic sign categories and the differences in size within the dataset. This analysis aimed to guarantee that the model would perform well in real-world circumstances. The findings validate that YOLOv9 substantially improves the precision and dependability of traffic sign identification, establishing it as a dependable option for implementation in intelligent transportation systems and ADAS. The incorporation of YOLOv9 in real-world traffic sign recognition and classification tasks demonstrates its promise in making roadways safer and more efficient.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.14 no.6
• /
• pp.37-49
• /
• 2015

In Space Syntax, the greater the degree of integration between separate links, the greater the links' accessibility from the target network. As such, planning pedestrian walks so that links with high degrees of integration are connected, or else inducing high integration value land use are both valid options. The travel distribution model reflects how walking demand, or more specifically, the pedestrian, partakes in route choosing behavior that minimizes select criteria, notably level of discomfort, as measured using travel distance and time. The model thus demonstrates travel patterns associated with demand pertaining to minimization of discomfort experienced by the pedestrian. This research introduces a method that integrates Space Syntax and the pedestrian travel distribution model. The integrated model will determine whether regions with high degrees of integration are actually being used as pivots for pedestrian demand movement, as well as to explain whether the degree of integration is sustained at an appropriate level while considering actual movement demand. As a means to develop the integrated model, a method that combines display of the visibility of the space syntax network and road-divided links is proposed. The pedestrian travel distribution model also includes an alternative path finding mechanism between origin and destination, which allows for uniform allocation of demand.

• Geomechanics and Engineering
• /
• v.28 no.1
• /
• pp.65-75
• /
• 2022

Time and cost of construction are key factors in decision-making during a tunnel project's planning and design phase. Estimations of time and cost of tunnel construction projects are subject to significant uncertainties caused by uncertain geotechnical and geological conditions. The Gaussian Process Regression (GPR) technique for predicting ground condition and construction time and cost of mountain tunnel projects is used in this work. The GPR model is trained with data from past mountain tunnel projects. The model is applied to a case study in which the predicted time and cost of tunnel construction using the GPR model are compared with the actual construction time and cost for model validation and reducing the uncertainty for the future projects. In addition, the results obtained from the GPR have been compared with to other models of artificial neural network (ANN) and support vector regression (SVR) that the GPR model provides more accurate results.

• Journal of ILASS-Korea
• /
• v.27 no.3
• /
• pp.155-160
• /
• 2022

Recently, major developed countries have strengthened automobile fuel efficiency regulations and carbon dioxide emission allowance standards to curb climate change caused by global warming worldwide. Accordingly, research and manufacturing on electric vehicles that do not emit pollutants during actual driving on the road are being conducted. Several automobile companies are producing and testing electric vehicles to commercialize them, but it takes a lot of manpower and time to test and evaluate mass-produced electric vehicles with driving mileage of more than 300km on a per-charge. Therefore, in order to reduce this, a simulation model was developed in this study. This study used vehicle information and MCT speed profile of small electric vehicle as basic data. It was developed by applying Simulink, which models the system in a block diagram method using MATLAB software. Based on the vehicle dynamics, the simulation model consisted of major components of electric vehicles such as motor, battery, wheel/tire, brake, and acceleration. Through the development model, the amount of change in battery SOC and the mileage during driving were calculated. For verification, battery SOC data and vehicle speed data were compared and analyzed using CAN communication during the chassis dynamometer test. In addition, the reliability of the simulation model was confirmed through an analysis of the correlation between the result data and the data acquired through CAN communication.

• The Korean Journal of Applied Statistics
• /
• v.23 no.6
• /
• pp.1179-1190
• /
• 2010

Management of the existing traffic network and understanding current traffic conditions is the most important and effective way to solve traffic congestion. This research investigates the status of Korea expressway through the Speed-Flow-Density diagram and finds the best suitable queueing model for each area. Dynamic structure in the queueing model enables us to reflect the structural change of the road in case of traffic congestion. To find the best model and estimate the parameters, we use the Newton-Raphson method. Finally, we examine the road efficiency in view of the optimal speed and density using the conditional distribution of speed and density of a S-F-D diagram.

• International Journal of Highway Engineering
• /
• v.13 no.2
• /
• pp.195-202
• /
• 2011

This study develops a day-to-day dynamics modeling framework, incorporating a consistent drivers' travel time perception behavior and traffic information provision. Descriptive traffic information is updated and provided to the subscribers making a final decision on route choice. Nonsubscribers(not equipped any information devices) are assumed to obtain daily traffic information from their experience or friends or other public agencies. Drivers' route choice behavior is modeled based on boundedly-rational behavior rules. A microscopic traffic simulation model is adopted to evaluate the network system performance. Numerical experiments on a real world network have demonstrated the convergent property of the proposed model and the effectiveness of the consistent perception model.