• International Journal of Highway Engineering
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• v.15 no.1
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• pp.163-169
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• 2013

PURPOSES: The urban traffic problems can be defined as general problems for smooth traffic flow including maintenance of mass transportation system according to suddenly increased population, traffic regulations for vehicles and pollution problem. As a method for solving traffic jams, one of the traffic problems of late, interest in Intelligent Traffic System(ITS) is increasing sharply, which is a system managing traffic demand by providing passers with information on traffic state of path and road conditions before they pass the road through ATIS, a field of ITS. METHODS: Variable message signs(VMS) is used on the roads as a method for providing information to promote smooth traffic flow and safety and prevent traffic accidents in advance by providing drivers with various information while driving. RESULTS: Recently, as ITS industry has been vitalized and technical factors of VMS have developed, various kinds of information is provided but the effect of VMS has not been maximized due to its limited type. CONCLUSIONS: Therefore, this study intends to provide methods for effective information transfer by analyzing driver's visual behavior characteristics for VMS and presenting a basis for maximizing VMS effect after considering read by expression type.

• International Journal of Highway Engineering
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• v.17 no.4
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• pp.107-115
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• 2015

PURPOSES: This study analyzes the characteristics of traffic flow on freeway sections under daytime and nighttime conditions to improve traffic safety, and suggests a method to estimate an accurate freeway capacity value that reflects these characteristics. METHODS : The trends and differences in capacity were investigated using comprehensive field data collected under both daytime and nighttime conditions on freeway sections with designated speeds of 80, 100, and 120 kph. RESULTS : The capacity values under nighttime driving conditions were reduced by 3.3%, 6.9%, and 8.8% at 80, 100, and 120 kph, respectively. Several nighttime adjustment factors were deduced for each designated speed category from the analysis results. CONCLUSIONS : It is expected that more accurate capacity values can be estimated for freeway sections under nighttime conditions by applying the resulting adjustment factors. In addition, traffic safety will be improved through the increased efficiency of traffic management on these freeway sections.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.1 s.6
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• pp.43-56
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• 2005

This research has examined a time series analysis(TSA) of an every hour traffic information such as occupancy, a traffic flow, and a speed, a statistical model of a surveyed data on the traffic fundamental diagram and an expand aspect of a traffic jam by many Parts of the traffic flow. Based on the detected data from traffic accidents on the Cheonan-Nonsan high way and events when the road volume decreases dramatically like traffic accidents it can be estimated from the change of occupancy right after accidents. When it comes to a traffic jam like events the changing gap of the occupancy and the mean speed is gentle, in addition to a quickness and an accuracy of a detection by the time series analyse of simple traffic index is weak. When it is a stable flow a relationship between the occupancy and a flow is a linear, which explain a very high reliability. In contrast, a platoon form presented by a wide deviation about an ideal speed of drivers is difficult to express by a statical model in a relationship between the speed and occupancy, In this case the speed drops shifty at 6$\~$8$\%$ occupancy. In case of an unstable flow, it is difficult to adopt a statistical model because the formation-clearance Process of a traffic jam is analyzed in each parts. Taken the formation-clearance process of a traffic jam by 2 parts division into consideration the flow having an accident is transferred to a stopped flow and the occupancy increases dramatically. When the flow recovers from a sloped flow to a free flow the occupancy which has increased dramatically decrease gradually and then traffic flow increases according as the result analyzed traffic flow by the multi regime as time series. When it is on the traffic jam the traffic flow transfers from an impeded free flow to a congested flow and then a jammed flow which is complicated more than on the accidents and the gap of traffic volume in each traffic conditions about a same occupancy is generated huge. This research presents a need of a multi-regime division when analyzing a traffic flow and for the future it needs a fixed quantity division and model about each traffic regimes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1218-1230
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• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2D
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• pp.105-111
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• 2010

Operating speed is one of key elements in the design and operation of freeway. Few studies have been carried out macroscopically to find characteristics of free-flow speed (FFS) using small sample size and not considering the conditions of traffic volume and the effects of hour periods. As an attempt to find out the characteristics of FFS, this study microscopically analysed both the distributions of freeway FFS and percentile speeds according to traffic volume and daily hour periods using huge sample size. It was found out that speed distributions are changed and showed different behaviors under the conditions of traffic volumes(1-5 veh/30sec) and daily hour periods(0-5, 6-8, 9-11, 12-19, 20-23). V85(85th percentile speed) at morning, night, and midnight period decreases according to increase in traffic volume respectively, whereas V85 at day time does not show considerable differences. So the behaviors of V85 according to hour periods dramatically changed according to the increase in traffic volume.

• 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 Korean Port Research
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• v.13 no.1
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• pp.113-122
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• 1999

Congestion and traffic accidents occur on the merge and diverge sections in the interchange of the freeway. Studies have been conducted to reduce the traffic delay and accidents on the merge section in the freeway since 1960s. but a study was not conducted to estimate the speed variation on the merge section construct models estimated for the speed variation and suggest the appropriate measures. The purpose of this study was to identify the traffic flow characteristics on the merge section in the freeway construct the models estimated for the speed variation on the merge section in the freeway and finally establish the appropriate measure for reduction of traffic delay and accidents on the merge section in the freeway. The following results were obtained: I) Speed variations in the urban freeway appeared to be about 3.2mph, 6.5mph and 7.4mph based on the morning peak period, afternoon peak period and 24-hours period but those in the suburban freeway appeared to be about 8.0mph, 11.1mph and 10.1mph based on the same periods respectively. So different speed reduction signs need be installed to reduce delay and accidents on the merge section in the freeway based on the areas and periods as the freeway traffic management system(FTMS). ii) These models estimated for speed variation need to be studied with the changeable message sign(CMS) technique based on the real-time data so that the traffic flow could be maximized and the traffic delay and accidents be on the merge section in the freeway as more efficient freeway traffic management system(FTMS) in the near future.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.189-198
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• 2015

Despite the fact that traffic engineering techniques have been comprehensively utilized in the past to enhance the performance of communication networks, the distinctive characteristics of Software Defined Networking (SDN) demand new traffic engineering techniques for better traffic control and management. Considering the behavior of traffic, large flows normally carry out transfers of large blocks of data and are naturally packet latency insensitive. However, small flows are often latency-sensitive. Without intelligent traffic engineering, these small flows may be blocked in the same queue behind megabytes of file transfer traffic. So it is very important to identify large flows for different applications. In the scope of this paper, we present an approach to detect large flows in real-time without even a short delay. After the detection of large flows, the next problem is how to control these large flows effectively and prevent network jam. In order to address this issue, we propose an approach in which when the controller is enabled, the large flow is mitigated the moment it hits the predefined threshold value in the control application. This real-time detection, marking, and controlling of large flows will assure an optimize usage of an overall network.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2005.10a
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• pp.111-116
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• 2005

The approaching waters of Po-hang port will be increased the traffic volume and passage of larger vessel according to the opening of Young-il new port in 2006. But the adjacent area of Po-hang port still exist the danger elements of safe navigation, the disordered navigation and traffic congestion. Therefore the safety of traffic must be improved in Po-hang vicinity area. This paper describes the status of marine traffic flow and navigational characteristics based on the marine traffic survey using the exclusive software, and the results of marine traffic survey classify into the ship's types and tonnage and track history of passing ships through the statistical method. Finally the examinations of main traffic route, traffic flow and navigational characteristics are discussed, and these results can be utilized the best design of ship's routing at the Po-hang approaching water.