• Journal of Information Technology Services
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• v.17 no.4
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• pp.85-100
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• 2018

In this study, we formulated rational criteria to efficiently provide traffic information services via a crafted approach. By utilizing this, we presented a detailed traffic information service providing method that can overcome the limitations of existing link unit information provision system. Three methodologies such as user survey, data mining, and KHCM (Korea Highway Capacity Manual) utilization method were applied to formulate a rational expression standard for traffic information service. Each method was designed to establish a quantitative criterion for various traffic conditions and to enable user-oriented traffic information service in consideration of the traffic principal/compatibility. Considering the results of each methodological analysis in a comprehensive manner, the basic expression standards for traffic information service was formulated. Then we presented improvements such as traffic condition step by road, speed range of traffic condition, expression term of traffic condition and so on. In order to complement the problems of the information provision system of the existing link unit based on the derived improvement criterion, we presented the detailed traffic information service provision method by using the traffic speed data of the second order. And we applied this to the two links of Daegu city. The method presented in this research can improve the quality of traffic information service. Not only it can be used for various fields such as optimal route search, traffic safety service and so on.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6D
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• pp.685-691
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• 2009

The purpose of this paper is to classify the traffic condition in freeway by the impacts of road weather. The factor analysis showed that weather factors, which are considered as influential, are identified as weather condition (rain or clear), temperature and sight distance with RWIS and VDS data in Seohae bridge used. The result of ANOVA shows that weather is dividedinto clear and rainy; temperature into below and equal or above $5^{\circ}C$ and sight distance into below or equal or above 10km. Based on those factors, the freeway traffic condition has been classified as five different types. The flow-speed model for each traffic conditions was proposed, which was not significant due to the lack of smaple data. Although not sufficient, the methodology to categorize traffic situation model presented in this paper may shed light on the idea for the future and can be used for proper traffic management for each weather condition.

• Smart Structures and Systems
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• v.24 no.6
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• Journal of the Korean Institute of Navigation
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• v.20 no.2
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• pp.51-58
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• 1996

For the smooth flow of traffic vehicles and its effective management, it is necessary to have an exact information on traffic condition, i.e., the volume of traffic, velocity, occupancy and classification of vehicles. In particular, for classification of vehicles, there has been only image processing method using camera, where the method can obtain much information but rather expensive. In this paper, an algorithm for the measurement of velocity and total length of vehicles has been proposed to develop a general traffic management system, which is necessary to discriminate the class of vehicles. In order to realize the proposed algorithm, we have developed an ultrasonic spatial filtering method, which has better performance than that of using the traditional vehicle detector. To have this system to be constructed, we have introduced three sets of ultrasonic devices where each has one transmitter and two receivers which are arranged to obtain the spatial difference of objects. The velocity of vehicles can be measured by analyzing the occurrence time of pulses and their time differences. The total length of vehicles can be given by multiplying velocity with time interval of pulses sequence. To confirm the effectiveness of this measuring system, the experiment by the spatial filtering method using the ultrasonic sensors has been carried out. As the results, it is found that the proposed method can be used as one of measurement tools in the general traffic management system.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.39-49
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• 2010

Although many studies have been carried out on the pattern of behavior of drivers which result from the provision of traffic information, there have been few detailed studies on the composition of message, method for message expression, timing of provision, point of provision, media for provision, changes by traffic condition, etc. This study was intended to provide an insight into the changes in the characteristics related to the provision of information by analyzing how the patterns of information utilization change depending on the traffic condition and reclassifying such patterns according to the characteristics of media. Unlike the existing studies, this study adopted the traffic condition, using rate of information media, and the correlation coefficient label as the basis for information media classification, and categorized them into passive utilization media, active utilization media, and past experience in order to ensure the statistical reasonability. The categorized using rate of information media and traffic condition was found to have a positive(+) correlation with the travel speed in the case of passive utilization media during both consecutive holidays(Korea's traditional Thanksgiving day) and weekends, but had a negative(-) correlation with the positive utilization media and past experience. The rate of decision to take a detour based on the past experience was high at the condition of congestion or slow during both consecutive holidays and weekends, but the rate of decision to take a detour through passive utilization media was high in a smooth traffic. In other words, if the traffic condition worsens, using rate of passive utilization media would be low while the diversion rate would be high which uses the active utilization media and past experience. Therefore, it should be established to suit the traffic condition and media characteristics for strategies of traffic distribution through drivers' diversion behavior on weekends and consecutive holidays.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.23-29
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• 2014

The advanced traffic management system of intelligent transport systems automates the related traffic tasks such as vehicle speed, traffic volume and traffic incidents through the improved infrastructures like high definition cameras, high-performance radar sensors. For the safety of road users, especially, the automated incident detection and secondary accident prevention system is required. Normally, CCTV based image object detection and radar based object detection is used in this system. In this paper, we proposed the algorithm for real time highway incident detection system using multi surveillance cameras to mosaic video and track accurately the moving object that taken from different angles by background modeling. We confirmed through experiments that the video detection can supplement the short-range shaded area and the long-range detection limit of radar. In addition, the video detection has better classification features in daytime detection excluding the bad weather condition.

• Journal of Korean Society for Geospatial Information Science
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• v.5 no.2 s.10
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• pp.131-144
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• 1997

Traffic jam densified day by day is phenomenon to occur lack of the road capacity in comparison with traffic density, but lack of the road cannot be concluded by main cause of traffic ism. Because the central function of a city would be concentrated upon the downtown and traffic demand would not be evenly distributed by the classification of an hour. Therefore, this study based on the fact that each driver will select the route generating traffic delay very low when path choice from origin to destination in travel plan estimating the quality of passage could be maintained the speed he want will approach to a characteristic grasp of a road, traffic, driver changing every moment by traffic-demand of road increased as a geometrical series with analysis a classification of a street, a intersection along the path on traffic density and highway capacity analysis the path using GIS techniques about complex street network, also will get the path of actual optimum for traffic delay trend creating under various condition the classification per a hour, a day of week and an incident through network such as analysis for traffic generation zone adjacent about street, intersection, afterward will expect the result increasing efficiency of the road-use through a good distribution of traffic by optimum-path choice, accordingly will prepare the scientific, objective, appropriate basis to decide the reasonable time of a road-widen and expansion through section analysis along a rate of traffic volume vs. road capacity.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.3
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• pp.257-263
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• 2013

The classification of risk factors and the identification of risk acceptance criteria are core works to assess risk levels with high enough confidential level in the field of marine traffic environment. In the previous study work, the twenty kinds of risk factors and its assessment criteria for the domestic marine traffic environment were proposed. In this paper, with these previous studying results, the relative importance of the risk factors were analyzed by questionnaire survey of marine traffic experts using the analytic hierarchy process. The analysis results showed that the relative importance of the visibility restriction is the highest among the twenty kinds of risk factors, and the relative importance of the traffic condition is the highest among the five kinds of risk categories. As results from analysis, it is expected that the approaching method on the relative importance is to be one of basic techniques for the development of risk assessment models in the domestic marine traffic environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.1-12
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• 2012

This paper aims at developing an active expressway entrance control effectiveness analysis tool which operators can utilize and manage traffic based on current traffic condition. For this, after identifying the current problems of tollgate-based entrance policy being used, a new set of decision element such as congestion index, decision criteria for congestion, and congestion management unit has been proposed together with the procedure of newly developed tollgate control policy. Three key parts developed are traffic condition identification module, tollgate metering module, and travel speed calculation module. Some measures of effectiveness were also identified and the newly developed effectiveness analysis tool produced better result. According to classification of traffic condition by reference speed as 80km/h, the improved tollgate entrance procedure increased 21.5% in average travel speed compared with Do-Nothing case and also increased 8.8% compared with current entrance control method.