• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.1-6
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• 2003

Route selecting of the road is a basic and important process. But the route selecting process must consider the technical, traffic and environment factor simultaneously. The optimum route that are able to reduce the construction cost, maximize utility value and investigate several alternative route must be selected. This study presents a reasonable plan for route selecting through subjective evaluation and classify the methods linked GIS from basic design of road construction.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1970-1974
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• 2010

This paper is how to improve contaminated air by the pollutant from vehicles through ventilators and entrance at the underground station. We are looking for the environment management to take care of customers. There is two ventilation systems. One is natural ventilation system, the other is forced ventilation system. Usually, subway ventilators were installed low on the sidewalk. There are lots of craps on the ventilators, so these things interrupt influx of outside air. But the gas from the vehicles comes into the station through entrance. There is lots of noise while ventilations run. So we install the supply air vents away from the road for the customers. If it's difficult, we cover around the ventilator with clear plastic plates more than 2M heights. We also install silencer on the ventilators. We install the air curtains on the entrances to prevent dust from outside. Seoul Metropolitan has a plan to make 60M deep underground road. To improve underground road air quality, ventilators should be installed that consider the above information.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.252-254
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• 2003

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. ill addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.

• KIEAE Journal
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• v.6 no.4
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• pp.69-76
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• 2006

The effects of the additional visual and auditory stimuli on the loudness evaluation of road traffic noise was investigated by the method of magnitude estimation. As a result, it was shown that additional visual stimulus of noise barrier can influence on the loudness perception of road traffic noise. Also, additional auditory stimuli such as green music or sound of flowing water can influence on the loudness perception of road traffic noise, approximately 5~10% lower than the absence of stimuli. But this effect was disappeared in the range of over 65dB(A).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.842-843
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• 2014

When the environmental impact assessment (EIA) of an development project is performed, the noise prediction model is used to evaluate the noise impact and prepare the noise reduction measures according to the implementation of an development project. Especially, the application of a 3-d noise prediction model is increased to describe the complex noise environment including high-rise living accommodations. Therefore, this paper suggests the application plan of a 3-d noise predicton model for the road noise impact assessment of a development project.

• Korean Journal of Remote Sensing
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• v.18 no.5
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• pp.263-271
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• 2002

Recently, spatial information technologies using remotely sensed imagery and functionality of GIS (Geographic Information Systems) have been widely utilized to various types of transportation-related applications. In this study, extraction programs of some practical indices, to be effectively used in transportation reference planning problem, were designed and implemented as prototyped extensions in GIS development environment: traffic flow estimation (TFL/TFB), urban rural index (URI), and accessibility index (AI). In TFL/TFB, user can obtain quantitative results on traffic flow estimation at link/block using high-resolution satellite imagery. Whereas, URI extension provides urban-rural characteristics related to road system, being considered one of important factors in transportation planning. Lastly, AI extension helps to obtain accessibility index between nodes of road segments and surrounding district areas touched or intersected with the road network system, and it also provides useful information for transportation planning problems. This approach is regarded as one of RS-T (Remote Sensing in Transportation), and it is expected to expand as new application of remotely sensed imagery.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.3
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• pp.188-195
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• 2009

Generally. mobile sources of air pollution were classified in on-road and non-road. Due to increased registration number of construction equipment in Korea. updated emission factors for non-road mobile sources, such as construction machinery. should be developed. NONROAD model of U.S. EPA already has introduced transient adjustment factors and sulfur adjustment factors for emission factors of diesel powered engine. In addition to this. European Environment Agency (EEA) has proposed emission factors for off-road machinery including several types of construction equipment. In this study. six types of construction equipment, such as excavator. forklift, loader, crane, roller and bulldozer, were studied to estimate emission factors based on total registration status in Korea. Total 445 construction equipments between 2004 and 2007 model year were tested with KC1-8 mode and air pollutants (CO, THC, $NO_x$, and PM) were measured. After statistical estimation and calculation, emission factors for CO, THC, $NO_x$, and PM for excavator, forklift, loader, crane, roller and bulldozer were provided and compared with previous emission factors. Moreover, updated emission factors for six types of construction equipment in this study were verified after comparison with emission factors of U.S. EPA. Finally, estimated emission amounts of four air pollutants were suggested according to six types of construction equipment.

• Journal of Korean Society of Forest Science
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• v.104 no.4
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• pp.598-606
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• 2015

This study analyzed the characteristics of the disaster occurrence and disaster-prone zones on the 85 forest roads in the Jeollabuk-do area by referring to their forest environment factors. The on-site survey reveal the types of forest road disasters as follows: erosions of cutting slopes 54.1%, erosions of the filling slope 35.3%, collapses of cutting slopes with filling slope 9.4%, and erosions of road surface 1.1%. Disasters most frequently occurred in the vertical location (the hillside) and the horizontal location (the slope), and the forest factors affecting the road disasters were degree of cutting slopes in $31^{\circ}{\sim}40^{\circ}$, degree of filling slopes in $21^{\circ}{\sim}30^{\circ}$, and the soil texture of SiL. The most significant factors on the most frequent occurrence of forest road disasters were forest type of coniferous, slope aspect northeast, forest age of plantation and felling area, and rainfall in 1601~1700. An analysis of the occurrence of the forest road disasters in the Jeollabuk-do area showed a positive correlative relationship with the following factors of the forest environment within a 1% error: degree of cutting slope in $31^{\circ}{\sim}40^{\circ}$, annual accumulation rainfall in 1601~1700. and showed a positive correlative relationship with the following factors of the forest environment within a 5% error: horizontal location of valley, forest type of coniferous, length of slope more than 20 m, forest age of plantation and felling area, soil texture of SiL.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1837-1845
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• 2015

The longitudinal velocity is necessary for accurate vehicular positioning in urban environment. The pitch angle, which is a road slope, should be calculated to acquire the longitudinal velocity. However, it is impossible to consider very accurate pitch, when using a sensor and an algorithm. That's why process noise and positioning stimation error of IMU should be adjusted to the driving environment and fuse GPS, OBD data with ACF which consist of AKF, CF in this paper. Then, final pitch angle which is appropriate for driving environment is estimated by IMMKF in order to optimize the system model according to road slope models.