• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.39-47
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• 2020

In this study, crash situations of representative bus crash types were elicited by analyzing a total of 1,416 bus repair record which were collected in 2018~2019. K-means clustering was used as a methodology for this study. Bus repair record contain the information of repair term, type of bus operation, responsibility of accident, weather condition, road surface condition, type of accident, other party, type of road and type of location for each data. Also, by checking collision parts of each bus repair record, each record was classified by types of collision regions. From this, 760 record are classified to frontal type, 363 record are classified to middle-frontal type, 374 record are classified to middle-rear type and 331 record are classified to rear type. As mentioned, k-means clustering was performed on each type of collision parts. As a result, this study analyzed the severity of bus crash based on actual bus accident data which are based on bus repair record not the crash data from the TAAS. Also, this study presented crash situation of representative bus crash types. It is expected that this study can be expanded to analyzing hydrogen bus crash and defining indicators of hydrogen bus safety.

• 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 Environmental Impact Assessment
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• v.33 no.2
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• pp.74-83
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• 2024

This study aims to estimate roadkill occurrences and investigate influential factors in Chungcheongnam-do, contributing to the establishment of roadkill prevention measures. By comprehensively considering weather, road, and environmental information, machine learning was utilized to estimate roadkill incidents and analyze the importance of each variable, deriving primary influencing factors. The Gradient Boosting Machine (GBM) exhibited the best performance, achieving an accuracy of 92.0%, a recall of 84.6%, an F1-score of 89.2%, and an AUC of 0.907. The key factors affecting roadkill included average local atmospheric pressure (hPa), average ground temperature (℃), month, average dew point temperature (℃), presence of median barriers, and average wind speed (m/s). These findings are anticipated to contribute to roadkill prevention strategies and enhance traffic safety, playing a crucial role in maintaining a balance between ecosystems and road development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.341-363
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• 2016

Haze or fog is a common natural phenomenon. In foggy weather, the captured pictures are difficult to be applied to computer vision system, such as road traffic detection, target tracking, etc. Therefore, the image dehazing technique has become a hotspot in the field of image processing. This paper presents an overview of the existing achievements on the image dehazing technique. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on two main works, that is, image dehazing scheme based on atmospheric veil and image dehazing scheme based on dark channel prior. After the overview and a comparative study, we propose an improved image dehazing method, which is based on two image dehazing schemes mentioned above. Our image dehazing method can obtain the fog-free images by proposing a more desirable atmospheric veil and estimating atmospheric light more accurately. In addition, we adjust the transmission of the sky regions and conduct tone mapping for the obtained images. Compared with other state of the art algorithms, experiment results show that images recovered by our algorithm are clearer and more natural, especially at distant scene and places where scene depth jumps abruptly.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.150-151
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• 2008

With the proliferation of vehicles and advancement of Information Technology, the technology of Telematics, which provides valuable services to people by collecting and analyzing the information from drivers, vehicles and Telematics environments (e.g. traffic information, road condition, weather information, etc.), has been a hot research area in IT and automotive recently. As the information technology revolution brings more and more assistance for driver information processing becomes increasing important. Therefore, drivers' workload is very essential factor for safety driving in Telematics environment. For managing drivers' workload effectively, ETRI haven been developing vehicle and driver management system which can collect data from drivers and vehicle in realtime and analyze the data to manage drivers' and vehcles' status since 2007. This technology will apply to commercial vehicle telematics such as texi or truck management system in the future for increasing driving safety. In this presentation, I would like to explain what we had developed so far and discuss future direction.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.110-116
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• 2016

Autonomous vehicles must obey the traffic laws in order to drive actual roads. Traffic signs erected at the side of roads explain the road traffic information or regulations. Therefore, traffic sign recognition is necessary for the autonomous vehicles. In this paper, color characteristics are first considered to detect traffic sign candidates. Subsequently, we establish HOG (Histogram of Oriented Gradients) features from the detected candidate and recognize the traffic sign through a SVM (Support Vector Machine). However, owing to various circumstances, such as changes in weather and lighting, it is difficult to recognize the traffic signs robustly using only SVM. In order to solve this problem, we propose a tracking algorithm with RANSAC-based motion estimation. Using two-point motion estimation, inlier feature points within the traffic sign are selected and then the optimal motion is calculated with the inliers through a bundle adjustment. This approach greatly enhances the traffic sign recognition performance.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.53-56
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• 2001

This paper has been developed a millimeter-wave radar to prevent car collision. This system needs to progress the problem as follows; (1) Increase of traffic accidents causing damage and injuries due to the increased number of motor vehicles and long distance driving, (2) Need for a device to help drivers who are in trouble due to bad weather conditions. (3) Need for a millimeter-wave radar as obstacles which need to be detected are small. This system is composited with some major technologies, Narrow beams to recognize obstacles or other objects, One-side circuit technology to prevent interference between electric waves, and Parts designed for radar products which are able to transmit millimeter - waves. The system has a various a application Field, Car distance auto-control system, prevent bump collision due to unexpected stoppage of the front car or careless driving, obstacle warning system, Car following system, and industrial and military purposes system. We have a looking forward to propose to develop field tests under various road conditions and hybrid car sensor by combining with other sensors

• International Journal of Highway Engineering
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• v.19 no.2
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• pp.1-6
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• 2017

PURPOSES : Pavement textures can be categorized into four according to wavelength: microtexture, macrotexture, megatexture (roads), and roughness. Pavement surface texture influences a number of aspects of tire-pavement interaction such as wet-weather friction, tire-pavement noise, splash, spray, tire-wear, and rolling resistance. In particular, macrotexture is the pavement surface characteristic that considerably impacts tire-pavement noise. In general, it can be demonstrated that tire-pavement noise increases with the increase of texture depth and wavelength. Recently, mean profile depth (MPD) and wavelength have been used to evaluate tire-pavement noise. This study aimed to identify the relationship between mean profile depth and average wavelength for asphalt pavement based on the information obtained on a number of asphalt pavement sections. METHODS : Profile data were collected from a number of expressway sections in Korea. In addition, mean profile depth and average wavelength were calculated by using this profile data. Statistical analysis was performed to determine the correlationship between mean profile depth and average wavelength. RESULTS:This study demonstrates a linear relationship between mean profile depth and average wavelength for asphalt concrete pavement. CONCLUSIONS :The strong relationship between mean profile depth and average wavelength of asphalt pavement was determined in this study.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.37-37
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• 2003

Rice is one of the most important crop in the world and is a major export of Thailand. Optical sensors are not useful for rice monitoring, because most cultivated areas are often obscured by cloud during the growing period, especially in South East Asia. Spaceborne Synthetic Aperture Radar (SAR) such as RADARSAT, can see through regardless of weather condition which make it possible to monitor rice growth and to retrieve rice acreage, using the unique temporal signature of rice fields. This paper presents the result of a study of examining the backscatter behavior of rice using multi-temporal RADARSAT dataset. Ground measurements of paddy parameters and water and soil condition were collected. The ground truth information was also used to identify mature rice crops, orchard, road, residence, and aquaculture ponds. Land use class distributions from the RADARSAT image were analyzed. Comparison of the mean DB of each land use class indicated significant differences. Schematic representation of temporal backscatter of rice crop were plotted. Based on the study carried out in Pathum Thani Province test site, the results showed variation of sigma naught from first tillering vegatative phase until ripenning phase. It is suggested that at least, three radar data acquisitions taken at 3 stages of rice growth circle namely; those are at the beginning of rice growth when the field is still covered with water, in the ear differentiation period, and at the beginning of the harvest season, are required for rice monitoring. This pilot project was an experimental one aiming at future operational rice monitoring and potential yield predicttion.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.13-20
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• 2022

Road conditions and weather conditions are very important factors in the case of traffic accident fatalities in fog and ice sections that occur on roads in winter. In this paper, a simulation was performed to estimate the traffic accident risk rate assuming traffic accident prediction data. In addition, in this paper, in order to reduce traffic accidents and prevent traffic accidents, factor analysis and traffic accident fatality rates were predicted using the WEKA data mining technique and TENSOR FLOW open source data on traffic accident fatalities provided by the Korea Transportation Corporation.