• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.91-99
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• 2015

This study intends to build a traffic accident predictive model considering road geometrics, traffic and enviromental characteristics and identify the relationship of 4-legs intersection accidents in Seoul and Busan metropolitan area. The RPNB(Random Parameter Negative Binomial) model shows improvement over the fixed NB(Negative Binomial) and out of 53 variables, 10 variables (main road number of lane, main road vehicle traffic volume(left), minor road vehicle traffic volume(right), main road drive restriction, minor road sight distance, minor road median strip, minor road speed limit, minor road speed restriction) showed to have significant variables affecting traffic accident occurrences in 4-legs signilized intersections. Also, among 10 significant variables, 2 variables(minor road sight distance, minor road speed restriction) found to be random parameters.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.117-133
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• 1996

In the paper a superior performance algorithm compared to the existing vehicle actuated controller and time fixed controller and the additional controller is described through realization of fuzzy traffic phase controller. Fuzzy theory is encouraging since the application is similar to human's decision ability that is approately coped with uncertain conditions. The paper presents that selection of the phase adequated the variable traffic conditions through the fuzzy theory algorithm and decision of optimal cycle time approated the uncertain traffic volume are predominant in traffic jam solution compared to the existing Webster's cycle time decision method and the sequential traffic phase design method and dual-ring phase operation system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.57-66
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• 2012

This study intended to evaluate the operational performance change from the introduction of the bus signal priority system using the field data. To complete the objective, travel time and volume data were collected from the before and after study, then the distribution of individual vehicle's travel time and the difference of travel time and traffic volume were compared respectively. Analysis results showed that no significant volume change was observed from both passenger vehicle and bus for the major and cross streets. It was identified that the quality of travel time distributions of passenger vehicle and bus was improved after introducing the bus signal priority system. In terms of average speed, passenger car in a major direction increased by 6.5% and bus increased by 10.5% in general. Statistical tests showed that those speed differences were statistically significant at the 95% of confidence level. The results of this paper will be a good source for further research in the area of bus signal priority control.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.183-191
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• 2018

In this study a traffic volume dependent displacement estimation model for Gwangan Bridge was developed using bridge monitoring big data. Traffic volume data for four different vehicle types and the vertical displacement data in the central position of the Gwangan Bridge were used to develop and validate the estimation model. Two statistical estimation models were developed using multiple regression analysis (MRA) and principal component analysis (PCA). Estimation performance of those two models were compared with actual values. The results show that both the MRA and the PCA based models are successfully estimating the vertical displacement of Gwangan Bridge. Based on the results, it is concluded that the developed model can effectively be used to predict the traffic volume dependent displacement behavior of Gwangan Bridge.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.107-116
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• 2005

The information on travel time in providing the information of traffic to drivers is one of the most important data to control a traffic congestion efficiently. Especially, this information is the major element of route choice of drivers, and based on the premise that it has the high degree of confidence in real situation. This study developed a vehicle arrival time prediction algorithm called as "VAT-DV" for 6 corridors in total 6.1Km of "Nam-san area trffic information system" in order to give an information of congestion to drivers using VMS, ARS, and WEB. The spatial scope of this study is 2.5km~3km sections of each corridor, but there are various situations of traffic flow in a short period because they have signalized intersections in a departure point and an arrival point of each corridor, so they have almost characteristics of interrupted and uninterrupted traffic flow. The algorithm uses the information on a demand volume and a queue length. The demand volume is estimated from density of each points based on the Greenburg model, and the queue length is from the density and speed of each point. In order to settle the variation of the unit time, the result of this algorithm is strategically regulated by importing the AVI(Automatic Vehicle Identification), one of the number plate matching methods. In this study, the AVI travel time information is composed by Hybrid Model in order to use it as the basic parameter to make one travel time in a day using ILD to classify the characteristics of the traffic flow along the queue length. According to the result of this study, in congestion situation, this algorithm has about more than 84% degree of accuracy. Specially, the result of providing the information of "Nam-san area traffic information system" shows that 72.6% of drivers are available.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.33-40
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• 2004

This study aims to provide guidelines for the selection of on-street parking spot on local streets considering conditions of surrounding area and characteristics of traffic generation. This guideline provides the method which determine required roadway width for planning and design of local streets. It is necessary to identify factors for the location selection analysis. This research team selects 12 case study areas to investigate traffic environment on the sites for this analysis. Most of factors which influence on-street parking are found to have a qualitative data format except traffic volume and pedestrian movement data. Quantification theory II which is known to be suitable for qualitative analysis has been applied to identify the meaningful variables for dependent variable. In addition, discriminant analysis has performed to verify the correlation for each variable with hit ratio. Road width, traffic volume, street traders and their heavy packages, and illegally parked vehicle are found to be most significant factors for selection of on-street parking location. Therefore, it is necessary to consider traffic volume generated from massive residential complex and traffic volume for outside and above-mentioned factors for installation of on-street parking facility in the case of new road construction or road width widen.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.19 no.2
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• pp.37-44
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• 2011

This paper summarizes the HVO concept and procedures, presents a summary of the research and results, and outlines areas where future HVO is required. This concept enables people to get their destinations through shortest paths with advanced air traffic control system and equipments. The concept's key feature is that pilots maintain their own separation from other aircraft using air-to-air datalink and on-board software which are needed for supporting flight information present on the high Way in the sky display and airborne internet. By assigning Self-Controlled Area which assume pilot have separation responsibility, controllers evaluated SATS HVO concept as a successful method on the view of reduced workload and increased traffic level on high volume operation.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.79-88
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• 2009

The objective of this study is to develop the new vehicle classification algorithm and minimize classification errors. The existing vehicle classification algorithm collects data from loop and piezo sensors according to the specification("Vehicle classification guide for traffic volume survey" 2006) given by the Ministry of Land, Transport and Maritime Affairs. The new vehicle classification system collects the vehicle length, distance between axles, axle type, wheel-base and tire type to minimize classification error. The main difference of new system is the "Wandering" sensor which is capable of measuring the wheel-base and tire type(single or dual). The wandering sensor obtains the wheel-base and tire type by detecting both left and right tire imprint. Verification tests were completed with the total traffic volume of 762,420 vehicles in a month for the new vehicle classification algorithm. Among them, 47 vehicles(0.006%) were not classified within 12 vehicle types. This results proves very high level of classification accuracy for the new system. Using the new vehicle classification algorithm will improve the accuracy and it can be broadly applicable to the road planning, design, and management. It can also upgrade the level of traffic research for the road and transportation infrastructure.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.2
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• pp.9-21
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• 2011

The ramp metering strategy is one of the effective ways to solve the freeway traffic congestion in peak time periods. The study was initiated with assurance that the traffic conditions of ramp and mainline that mitigate the congestion would exist. Under the moderate traffic volume levels, ramp metering is expected to improve the quality of freeway operation. To derive a range of traffic condition, three operation strategies(Do nothing, ramp metering, minimum ramp control) were set up and the ALINEA algorithm was implemented with microscopic traffic simulator "VISSIM". The volumes of mainline and ramp are key parameters for the simulation scenarios. Measures of effectiveness for the study include mainline density and average vehicle speed. Operation boundaries in terms of traffic volume were proposed for operating ramp metering strategy. The results show that under the proposed traffic conditions the ramp metering was more successful to increase average vehicle speeds. Traffic controls under the operation boundaries of traffic levels give significant effects for density and average vehicle speed. The outcomes of this study would be useful to improve the performance of ramp metering strategies.