• Journal of Korean Society of Transportation
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• v.17 no.2
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• pp.179-191
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• 1999

To device effective freeway traffic flow control strategies and freeway traffic information dissemination strategies, it is very important to estimate real physical queue length on the freeway. Shock wave theory and queueing theory have limitation to be used to estimate the queue length. The primary objective of this study is to develop a reliable method for estimating the physical queue length and level of congestion. Queueing propagation processes were analysed by using such traffic data as main line traffic volume, ramp volume, density. speed, and physical queue length collected by video photographing on Olympic Freeway. As a result of analysis, it has been confirmed that the real queue length can be estimated by using the traffic counts arriving the congested region and passing a bottleneck location. Further more, a reliable method for estimating the level of congestion could be developed on the basis of real-time traffic counts.

• Journal of Korean Society for Atmospheric Environment
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• v.10 no.3
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• pp.197-202
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• 1994

The research described in this paper is conducted to estimate the short-term concentrations of nonreactive pollutants such as CO and TSP from vehicle emissions near Kyungbu Highway. An emphasis is placed on the development of a model for a hourly traffic volume for each vehicle type, which is based on real traffic data. By using the model and the calculated emission factor due to vehicle speed for each vehicle type, the emission rate of CO and TSP for each traffic line is computed. The hourly emission rate and meteorological data are used to simulate by HIWAY-2 for the distance of 5m and 10m from the downwind edge of Kyungbu Highway located in relatively uncomplicated terrain.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.7-29
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• 1996

Traffic flow characteristics is analysed on eight multi-lane roads which are unsignalized in urban areas. Data of traffic flow rates by classification and average speed were gathered every ten minutes interval for twenty-four hours. Machine (NC-90A) was used to acquire the field data. The major purpose of this study is to build up speed-density models on urban arterial roads. Five different kinds of models were tested. Those models are Greenshields' model, Greenberg's model, modified Greenberg's model, Underwood's model and Drake's model. The modified Greenberg's model fits best at six points and the Greenshield's model fits best two points out of eight points. The breakpoint(Kb) of modified Greenberg's model is between 10 and 32 pcphpl. Capacity drawn from speed-volume relationships were appeared to be arround 2,000 and 2,200 pcphpl at the Hannam Bridge and the Hannam Overpass and 1,100 and 1,700 pcphpl at Namsan Tunnel(No1) and the beginning point of Gyeong-Bu Expressway.

• Journal of Environmental Health Sciences
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• v.30 no.3
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• pp.293-299
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• 2004

Traffic represents one of the largest sources of primary air pollutants in urban area. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentration of pollutants. A characteristics of most of the these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emission inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for vehicle types. The majority of inventories are compiled using passive data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. The study of current trends is towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this study, a model of vehicle emission calculation by using real-time traffic data was studied. Traffic data, which are required on a street-by-street basis, is obtained from induction loops of traffic control system. It is possible that characteristics of hourly air pollutants emission rates is obtained from hourly traffic volume and speed. An emission rates model is allocated with a high resolution space by using geographic information system (GIS). Vehicle emission model was developed with a high resolution spatial, gridded and hourly emission rates.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.125-136
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• 2005

The current traffic accident reduction procedure in economic feasibility study does not consider the characteristics of road and V/C ratio. For solving this problem, this paper suggests methods to be able to evaluate safety of each road in construction and improvement through developing accident Prediction model in reflecting V/C ratio Per road types and traffic characters. In this paper as primary process, model is made by tke object of urban roads. Most of all, factor effecting on accident relying on road types is selected. At this point, selecting criteria chooses data obtained from road planning procedure, traffic volume, existence or non-existence of median barrier, and the number of crossing point, of connecting road. and of traffic signals. As a result of analyzing between each factor and accident. all appear to have relatives at a significant level of statistics. In this research, models are classified as 4-categorized classes according to roads and V/C ratio and each of models draws accident predicting model through Poisson regression along with verifying real situation data. The results of verifying models come out relatively satisfactory estimation against real traffic data. In this paper, traffic accident prediction is possible caused by road's physical characters by developing accident predicting model per road types resulted in V/C ratio and this result is inferred to be used on predicting accident cost when road construction and improvement are performed. Because data using this paper are limited in only province of Jeollabuk-Do, this paper has a limitation of revealing standards of all regions (nation).

• Journal of the Korean Society for Aviation and Aeronautics
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• v.25 no.3
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• pp.89-100
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• 2017

In order for the international aviation community to efficiently and safely manage the gradual increase of air passenger demand, direction suggestions of airport traffic prediction based on future airport capacity requirements, airport design and infrastructure establishment is utilized by airport traffic data that is m comparable internationally. It is a global trend to pursue more efficient airport operating system structure to accept air passenger demand through more realistic comparable data in order to escape from the structure of reckless airport establishment and infrastructure composition based on passenger demand predictions referring to simple statistical data that has existed in the past. This study aimed to seek effective operational measures for the New Jeju airport scheduled to be opened in 2025 by time-series analysis. This study also analysed airport operation strategies, air traffic distribution strategies, cargo volume increase rates and its effectiveness of airports adopting the multi-airport system that have similar operational practices and geographical conditions. This study sought the most appropriate multi airport system application measures for New Jeju airport to promote efficiency and international competitiveness.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.116-126
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• 2013

Weather factor to affect driver's driving environment are due to changes in weather conditions is caused rainfall, snowfall, fog etc and the reducing of road capacity and because deteriorating weather could be affect the headway and vehicle speed and the decrease in lane caused by an accident occurs that was analyzed that bad weather occur congestion greater than on the clear day. Bad weather to reduce of the driver's visibility was analysed the sensitive to changes in travel speed and traffic accident to appears high characteristics according to weather conditions on the expressway(higher speed) than general road. As a result, visibility have been determined to be important factor in changes of the highway traffic flow characteristics. Therefore, in this study, we selected traffic volume and travle speed that have a major impact on high-speed expressway basic segments as importance effect factor and we selected to subject of analysis among the Metropolitan Expressway that it could be obtained certain level of traffic volume data and we studied the changing of expressway traffic flow due to change visibility after the collecting of meteorological and traffic data. In order to perform this study, data collection and analysis methods were established through the existing literature and we had selected level of visibility of the expressway and we had performed the statistical verification. Finally, we had calculated the rate of change of expressway traffic flow characteristics due to visibility and we had come up with a way to apply at capacity and service level analysis.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.51 no.3
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• pp.461-473
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• 2015

Jinhae Bay located in the southern of Korean Peninsular is an important spawning area in Korea. By some preliminary studies it was measured several times that adult Pacific codes (Gadus microcephalus) were passed (swimming layer: 15 to 18 m) over a submerged sea tunnel (sea bottom: about 30 m) rather than another immigration route when the Pacific codes were tagged surgically with an acoustic transmitters and released inside of the Bay. There is a possibility that the Pacific codes and the other fishes use the route on the sea tunnel as an immigration route are affected by a human-generated underwater noise around the sea tunnel due to the sea tunnel traffic. On this study the 25-hour measurements of the underwater noise level by water layer were conducted with a hydrophone attached on a portable CTD and an underwater noise level meter during four seasons, and the acoustical characteristics of the underwater noise was analyzed. The mean traffic volume for one hour at the sea tunnel on the spring was shown the largest value of 1,408 [standard deviation (SD): 855] vehicles among four seasons measurement. The next one was ordered on the autumn [1,145 (SD: 764)], winter [947 (SD: 598)] and summer [931 (SD: 558)] vehicles. Small size vehicle was formed 84.3% of the traffic volume, and ultra-small size, medium size, large size and extra-large size of the vehicle were taken possession of 8.7%, 3.2%, 2.0% and 1.8%, respectively. On the daily change of the noise level in vertical during four seasons the noise level of 5 m-layer was shown the highest value of 121.2 (SD: 3.6) dB (re $1{\mu}Pa$), the next one was 10 m-layer [120.7 (SD: 3.5)], 2 m- and 15 m-layer [120.3 (SD: 3.5 to 3.7)] and 1 m-layer [119.2 (SD: 3.6)] dB (re $1{\mu}Pa$). In relation with the seasonal change of the noise level the average noise level measured during autumn was shown the highest value of 123.9 (SD: 2.6) dB (re $1{\mu}Pa$), the next was during summer [121.4 (SD: 3.2)], spring [118.0 (SD: 3.4)] and winter [116.5 (SD: 5.1)] dB (re $1{\mu}Pa$). In results of eigenray computation when the real bathymetry data (complicate shape of sea bed) was applied the average number of eigenray was 2.68 times (eigenrays: 11.03 rays) higher than those of model bathymetry (flat and slightly sloped sea bottom). When the real bathymetric data toward inside (water depth becomes shallow according to a distance between the source of noise and hydrophone) of the Bay was applied on the eigenrays calculation the number of the eigenray was 1.31 times (eigenrays: 12.49 rays) larger than the real bathymetric data toward outside (water depth becomes deep with respect to the distance). But when the model bathymetric data toward inside of the Bay was applied the number of the eigenray was 1.05 times (eigenrays: 4.21 rays) larger than the model bathymetric data toward outside.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4717-4737
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• 2017

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.618-619
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• 2015

The costs due to the maintenance and its method of national road have increased every year, and the maintenance costs have become higher due to the maintenance method without considering the characteristics of each region. This study is a preliminary study regarding the determination of maintenance method according to the characteristics of each region by applying variables (e.g. serviced time, traffic volume, average temperature, maximum temperature, minimum temperature, rainfall, and snowfall) to decide the maintenance and its method through the correlation analysis of road maintenance and repair period for each regional agency using the maintenance history data of national road management agency.