• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.180-183
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• 2008

This research addresses the problem of estimating Origin-Destination (O-D) trip matrices from link volume counts, a set of unobserved link volumes and information of user equilibrium flows in transportation networks. A heuristic algorithm for estimating unobserved link flows is derived, which provides volume estimates that are approximately consistent with both observed flows and an assumption of user equilibrium conditions. These estimated link volumes improve the constraints associated with the synthetic OD estimation model, providing improved solution search procedure. Model performance is tracked in terms of the root mean square errors (RMSE) in predicted travel demands, and where appropriate, predicted linked volumes. These results indicate that the new model substantially outperforms existing approaches to estimating user-equilibrium based synthetic O-D matrices.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.144-152
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• 2018

This study focuses on collecting traffic data using drones to compensate for limitation of the data collected by the existing traffic data collection devices. Feasibility analysis was performed to verify the traffic data extracted from drone videos and optimal methodology for extracting data was established through analysis of various data reduction scenarios. It was found from this study that drones are very economical traffic data collection devices and have strength of determining the level-of-service(LOS) for uninterrupted flow condition in a very simple and intuitive way.

• Journal of Advanced Navigation Technology
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• v.12 no.1
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• pp.46-53
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• 2008

The ITS(Intelligent Transport System) enables us to provide solutions on traffic problems, while maximizing safety and efficiency of road and transportation systems, by combining technologies from information and communication, electrical engineering, electronics, mechanics, control and instrumentation with transportation systems. The issues that an integration system for massive traffic data sources must face are due to several factors such as the variety and amount of data available, the representational heterogeneity of the data in the different sources, and the autonomy and differing capabilities of the sources. In this paper, we describe how to extract and load of the heterogeneous massive traffic data from the operational databases, such as FTMS and ARTIS using commercial tools. Also, we experiment on traffic data warehouses with integrated quality management techniques for providing high quality data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.192-202
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• 2021

In the past, various studies have been conducted on safety facility installation standards and road safety indices. But there are limitations in applying them to the field, such as using many survey items and variables that are difficult to use. Therefore, this study attempted to develop road safety judgment indicators considering the applicability of the research results and to prepare criteria for installing safety facilities. As part of the study, data of related systems were reviewed, and the use of variables already in use was figured out. Furthermore, the road safety judgment indicators reflecting traffic, road, and accident factors were developed through correlation and factor analysis. Later, the criteria score for determining the installation of safety facilities was derived through cluster analysis. The analyses suggested, that the installation judgment criterion score at the intersection was lower than that of the single road(crosswalk), and the road risk at the intersection was higher.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.42-52
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• 2022

Bus Information System (BIS) services are expanding nationwide to small and medium-sized cities, including large cities, and user satisfaction is continuously improving. In addition, technology development related to improving reliability of bus arrival time and improvement research to minimize errors continue, and above all, the importance of information accuracy is emerging. In this study, accuracy performance was evaluated using LSTM, a machine learning method, and compared with existing methodologies such as Kalman filter and neural network. As a result of analyzing the standard error for the actual travel time and predicted values, it was analyzed that the LSTM machine learning method has about 1% higher accuracy and the standard error is about 10 seconds lower than the existing algorithm. On the other hand, 109 out of 162 sections (67.3%) were analyzed to be excellent, indicating that the LSTM method was not entirely excellent. It is judged that further improved accuracy prediction will be possible when algorithms are fused through section characteristic analysis.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.155-164
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• 2008

Variable Message Signs (VMS) is one of the major components for Intelligent Transport Systems (ITS) services that provides real-time traffic and incident information to drivers. The objective of this research was to develop a method determining the optimal location of VMS considering safety and driving characteristics of various drivers. A driving simulator was utilized to evaluate how drivers can safely exit to off-ramp depending on various VMS locations while information relating route diversion was provided. The binary logistic regression and factor analysis were applied in developing a probability model that predicts the success of safe off-ramp exiting. Based on the developed probability model, a method to estimate the spacing between VMS and off-ramp is suggested. It is expected that the products of this study would be utilized as a tool in determining VMS locations for ITS planners and designers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.91-104
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• 2022

The operational design domain (ODD) of autonomous vehicles needs to be expanded on highways and urban roads in light of the substantial commercialization of Level 3 autonomous vehicles. Therefore, this study developed a specific infrastructure autonomous vehicle-based cooperative driving service to ensure the driving safety of autonomous vehicles on city roads. The traffic operation efficiency, safety evaluation, and core evaluation indices for each service were selected and analyzed to study the effect of each service. The result of the analysis confirmed that the traffic operation efficiency and safety of autonomous vehicles were improved through the V2X communication-based autonomous cooperative driving service. On the whole, the significance of this study is in deriving the effect of the autonomous cooperative driving service based on V2X communication on urban roads with interrupting traffic flow.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.28-32
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• 2020

Railway transportation is the main land-based transportation in most countries. Accordingly, railway-transportation safety has always been a key issue for many researchers. Railway pedestrian accidents are the main reasons of railway-transportation casualties. In this study, we conduct experiments to determine which of the latest convolutional neural network models and algorithms are appropriate to build pedestrian railroad accident prevention systems. When a drone cruises over a pre-specified path and altitude, the real-time status around the rail is recorded, following which the image information is transmitted back to the server in time. Subsequently, the images are analyzed to determine whether pedestrians are present around the railroads, and a speed-deceleration order is immediately sent to the train driver, resulting in a reduction of the instances of pedestrian railroad accidents. This is the first part of an envisioned drone-based intelligent security system. This system can effectively address the problem of insufficient manual police force.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.1
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• pp.1-10
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• 2015

In urban area, since multipath and signal attenuations frequently occur due to street trees, street lights and buildings, it is difficult to obtain accurate navigation solution using GPS. As these problems also impact negatively on the INS/GPS coupled system, implementing advanced transportation systems such as autonomous navigation system and Intelligent Transportation System (ITS) become quite hard. For this reason, to alleviate deterioration of navigation system performance in urban area, direction information extraction algorithm using vision system is proposed in this paper. 2D Finite Rate of Innovation (FRI) technique is applied to extract lane edges. The proposed technique is simulated using road images and feasibility of proposed technique is analyzed through the simulation results.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.37-46
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• 2021

Government agencies, such as police and local governments, strive to prevent traffic hazards and create a comfortable road environment by pormoting transportation and road facilities. To this end, roads and transportation facilities are enhanced and adjusted, and improvement projects in areas with frequent traffic accidents are carried out. Usually, improvement projects in areas with frequent traffic accidents vary by projects and region. Moreover, these projects are carried out under the supervision of a person in charge and related parties. Hence, civil complaints and subjectivity are reflected in deriving priorities for the improvement projects, limiting the efficiency of the project. To this end, a study was conducted to estimate the direction of improvement of the project target site. This study comprehensively considered road, traffic, and accident conditions of representative projects with high effectiveness in handling traffic accidents. The results of the study state that the accuracy of estimating the improvement project was around 88%. In addition, the study found that there was a strong relationship between traffic volume, accident rate, and accident severity in estimating the improvement direction.