• The Journal of the Korea Contents Association
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• v.8 no.10
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• pp.76-87
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• 2008

Prediction of travel time on road network is one of crucial research issue in dynamic route guidance system. A new approach based on Rule-Based classification is proposed for predicting travel time. This approach departs from many existing prediction models in that it explicitly consider traffic patterns during day time as well as week day. We can predict travel time accurately by considering both traffic condition of time range in a day and traffic patterns of vehicles in a week. We compare the proposed method with the existing prediction models like Link-based, Micro-T* and Switching model. It is also revealed that proposed method can reduce MARE (mean absolute relative error) significantly, compared with the existing predictors.

• Proceedings of the KSRS Conference
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• v.1
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• pp.260-262
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• 2006

One of future remote sensing techniques for transportation application is vehicle detection from the space, which could be the basis of measuring traffic volume and recognizing traffic condition in the future. This paper introduces an approach to vehicle detection using image object segmentation approach. The object-oriented image processing is particularly beneficial to high-resolution image classification of urban area, which suffers from noisy components in general. The project site was Dae-Jeon metropolitan area and a set of true color aerial images at 10cm resolution was used for the test. Authors investigated a variety of parameters such as scale, color, and shape and produced a customized solution for vehicle detection, which is based on a knowledge-based hierarchical model in the environment of eCognition. The highest tumbling block of the vehicle detection in the given data sets was to discriminate vehicles in dark color from new black asphalt pavement. Except for the cases, the overall accuracy was over 90%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.128-134
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• 2006

Traffic information protocol in DMB is very different from existing analog broadcasting and wireless communication network. In this paper, we examined whether traffic information protocol of Europe Broadcasting Union(TPEG) is applicable to domestic DMB. Also, we proposed a division of classification on kinds of franc information, and related data that it is required to transmit traffic information of TPEG form. We composed of experiment equipment and studied whether is expressed traffic informations as like accident, event, traffic condition and CCTV image on car navigation system. The results obtained it can be given expression to phrases from TPEG streaming data and to link with electronics map by decoding TPEG straming data. Also it can be expressed CCTV and graphic image which is composed of TPEG form.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1630-1636
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• 2022

The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.

• International Journal of Highway Engineering
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• v.11 no.4
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• pp.1-7
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• 2009

Traffic accident by aging drivers is expected to be on the rise rapidly as the number of aging drivers is rising along with the aging trend being progressed. In this study, traffic accident features depending on the classification of aging population and non aging one was evaluated. As a result of this evaluation, effect factors influencing over the aging population was found to be expressed differently from that of the non aging one. Odds ratio between the aging population and non aging one was evaluated through logit model and a model with potential accident probability of the aged drivers was developed. Accident risk of the aged drivers under the condition of curved road, cutting section and moistured road was revealed to be higher than that of the non aging population.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.175-196
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• 2004

This study focuses on improving the performance of freeway incident detection by introducing some new measures to reduce false alarms in developing a new incident detection model. The model consists of the 5 major components through which a series of decision makings in determining the given traffic flow condition are made. The decision making process was designed such that the causes of traffic congestions can be accurately classified into several types including incidents and bottlenecks according to their unique characteristics. The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of the detection rate and detection time. It should noted that the model produced much less false alarms than most of the existing models. The study results prove that the initial objective of the study was satisfied as it was an experimental trial to improve the false alarm rate for the incident detection model to be more pactically usable for traffic management purposes.

• Journal of KIBIM
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• v.6 no.2
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• pp.29-38
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• 2016

Road bridges are deteriorating gradually, and it is forecasted that the number of road bridges aging over 30 years will increase by more than 3 times of the current number. To maintain road bridges in a safe condition, current safety conditions of the bridges must be estimated for repair or reinforcement. However, budget and professional manpower required to perform in-depth inspections of road bridges are limited. This study proposes an estimation model for safety rating of road bridges by analyzing the data from Facility Management System (FMS) and Yearbook of Road Bridges and Tunnel. These data include basic specifications, year of completion, traffic, safety rating, and others. The distribution of safety rating was imbalanced, indicating 91% of road bridges have safety ratings of A or B. To improve classification performance, five safety ratings were integrated into two classes of G (good, A and B) and P (poor ratings under C). This rearrangement was set because facilities with ratings under C are required to be repaired or reinforced to recover their original functionality. 70% of the original data were used as training data, while the other 30% were used for validation. Data of class P in the training data were oversampled by 3 times, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was used to develop the estimation model. The results of estimation model showed overall accuracy of 84.8%, true positive rate of 67.3%, and 29 classification rule. Year of completion was identified as the most critical factor on affecting lower safety ratings of bridges.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.179-185
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• 2018

Particulate Matter (PM2.5) has various adverse effects on health. Climate and industry activity and traffic volume are the main causes, especially in urban area. In order to construct an effective forecasting system, many measurement systems are required, but it is impossible in reality. Therefore, in this study, we propose a method to infer PM2.5 condition by using rule induction technique. The experimental results showed a classification accuracy of 71%.

• Journal of Korean Foot and Ankle Society
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• v.23 no.4
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• pp.183-188
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• 2019

Purpose: Bone contusion is usually treated with conservative therapy for 3 months. Bone contusion around knee and hip joints has been extensively reported on, but there are scant reports on this condition in foot and ankle joints. This study evaluated the nature, characteristics and location of bone contusion around foot and ankle joints to enlighten clinicians on how to better treat this disease entity. Materials and Methods: We classified bone contusion of the 76 patients into three types (102 sites; 47 ankle sprains, 18 traffic accidents, 11 falls) according to the Costa-Paz system with employing magnetic resonance imaging (MRI), and the study then analyzed the common sites and areas of occurrence according to the mechanism of injury and duration of pain after first conducting conservative therapy. Results: Of the 76 patients (102 sites) on the MRI, 43 case (42.2%) for talus, 19 cases for distal tibia, and 12 cases for calcaneus were involved. The classification, according to the Costa-Paz system, was Type I, 51 cases; Type II, 32 cases; and Type III, 19 cases. The duration of pain after conservative treatment was 12.15±2.17 weeks for Type I, 14.5±2.15 weeks for Type II, and 21.0±3.8 weeks for Type III. Conclusion: The most common location of post-traumatic bone contusion around both the foot and ankle is the talus, distal tibia, and calcaneus. The most common type of injury noted on MRI is a diffuse signal with change of the medullary component (Type I), In cases of bone contusion extending to a subjacent articular surface or disruption or depression of the normal contour of the cortical surface (Types II, III), the patients' pain appears to last longer. Thus, it is necessary to consider a longer period of conservative treatment in cases of Types II and III bone contusion because the patients' pain may last longer than 3 months.