• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.103-108
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• 2004

The motivation of this study comes from the problem recognition that the headway of passing vehicle in signalized intersections can not be merely determined by departing sequence. Traffic speed and headway data of passing vehicles in signalized intersection have been obtained by using magnetic detectors(NC 97) and detecting program, and the data was analyzed. Without special treatment, the model established on passing behavior of vehicles was meaningless from statistical view point. Hence, special treatments such as filtering (upper 85% offensive driver driven vehicle's) and log scaling of data were carried on. With this new data, meaningful model (where coefficient of determination is 0.91) was established. This model explained the fact that vehicle headway in signalized intersection is affected by speed and headway of previous vehicle and speed of itself.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.57-65
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• 2011

After heated discussion, National Police Agency decided not to install Red Arrow signal at such major intersections as Gwanghwamoon, Sejongro. The major issues can be summarized in the following reasons. The one is the conflict of color and symbol (red means STOP and arrow means PROGRESS), and it would confuse drivers and may cause traffic accident. The other includes high replacement cost. This paper delivered how much red arrow signal would affect (1) drivers start up delay time, (2) saturation flow rate and (3) vehicle headway. The result showed that there was no statistical difference in those even when a red arrow signal is placed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.9 no.1
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• pp.1-10
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• 1989

The stress history of a bridge is different depending on the characteristic of traffic flow. Because the flow is varied with vehicle type, weight and headway time etc., statistical analysis in bridges is necessary to estimate the history by traffic flow. By applying the statistical analyses in fracture mechanics, the remaining service life of the structure can be estimated. In this paper, 1)the statistical analysis of vehicle type, weight and headway time etc. to analysis randomness of traffic flow, 2)measuring and analysis of stress history of a real bridge, 3)reappearance of stress history by Monte-Carlo Simulation using constitution ratio of vehicle type, weight and headway time as probabilitic variable, 4)comparision of the calculated and modelled stress history, 5)calculation of reduction factor, 6)comparision of frequency of stress range depending on span length etc. were performed. From the results, the basic modelled stress history which is necessary for the method of estimation of the remaining service life of the structure could be suggested.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.139-147
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• 2007

In military transportation, the use of wide trailer for transporting the large and heavy weight equipments such as tank, armoured vehicle, and mobile gunnery is quite common. So, the vulnerability of causing traffic accidents for these wide military trailer to bump or collide with another car in adjacent lane is very high due to its broad width in excess of its own lane's width. Also, the possibility of these strayed accidents can be increased especially by the careless driver. In this paper, the recognition system of lane and vehicle headway direction is developed to detect the possible collision and warn the driver to prevent the fatal accident. In the system development, Kalman filtering is used first to extract the border of driving lane from the video images supplied by the CCD camera attached to the vehicle and the driving lane detection is completed with regression analysis. Next, the vehicle headway direction is recognized by using neural network scheme with the extracted parameters of the detected driving lane feature. The practical experiments for the developed system are also carried out in the real traffic road of Seoul city area and the results show us the more than 90% accuracy in recognizing the driving lane and vehicle headway direction.

• Journal of KIBIM
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• v.8 no.3
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• pp.12-19
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• 2018

The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens' convenience by responding to urban conditions.

• Journal of Navigation and Port Research
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• v.31 no.1 s.117
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• pp.107-113
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• 2007

This research addressed the problem of describing how the operating characteristics of passenger car and large vehicle differ qualitatively and quantitatively through the analysis of field survey data. A formulation that estimates passenger car equivalents used in this paper is derived by microscopic headway method. Regression analysis was used to focus on the effect of vehicle type on intervehicular spacings and the modeling technique for the statistical analysis was detailed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1999-2007
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• 2013

The key characteristics of two-lane-and-two-way traffic flow are platoon and overtaking caused by low-speed vehicle such as truck. In order to develop two-way traffic flow model comprised of CF(car-following) and overtaking model, it is essential to develop a car-following model which is suitable to two-way traffic flow. Short distance between vehicles is caused when a high-speed vehicle tailgates and overtakes foregoing low-speed vehicle on two-way road system. And a vehicle following low-speed vehicle decides to overtake the front low-speed vehicle using suitable space within the headway distribution of opposite traffic flow. For this reason, a two-way CF model should describes not only running within short gap but also headway distribution. Additionally considering domestic two-way-road size, there is a on-going need for large-network simulation, but there are few studies for two-way CF model. In this paper, a two-way CA model is developed, which explains two-way CF behavior more realistic and can be applied for large road network. The experimental results show that the developed model mimics stop-and-go phenomenon, one of features of congested traffic flow, and efficiently generates the distribution of headway. When the CF model is integrated with overtaking model, it is, therefore, expected that two-way traffic flow can be explained more realistically than before.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.137-144
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• 2007

This study involves an analytical approach to determine transit dispatching schedules (headways) Determining a time schedule is an important process in transit system planning. In general, the transit headway should be shorter during the peak hour than at non-peak hours for demand-responsive service. It allows passengers to minimize their waiting time under inelastic, fixed demand conditions. The transit headway should be longer as operating costs increase, and shorter as demand and waiting time increase. Optimal headway depends on the amount of ridership. and each individual vehicle dispatching time depends on the distribution of the ridership. This study provides a theoretical foundation for the dispatching scheme consistent with common sense. Previous research suggested a dispatching scheme with even headway. However, according to this research, that is valid for a specific case when the demand pattern is uniform. This study is a general analysis expanding that previous research. This study suggests an easy method to set a time table without a complex and difficult calculation. Further. if the time axis is changed to the space axis instead, this study could be expanded to address the spacing problems of some facilities such as roads. stations, routes and others.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.119-127
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• 2009

It has been known that bus route and company related characteristics have influences on punctuality, but fewer research have been conducted. Independent variables used in this study were selected using correlation analysis, and OLS(Ordinary Least Square) and HLM(Hierarchical Linear Model) were employed to identify factors affecting bus punctuality(headway deviation). The results showed that ICC(intraclass Correlation Coefficient) is 0.10, indicating that hierarchical linear models are more adequate for these data because there is effective variation in the subjects between companies. Punctuality was found to be negatively associated with the number of vehicles, the number of persons per vehicle, and total travel time. On the other hand, average headway and company size have a positive relationship with punctuality. Therefore, the number of vehicles per route, average headway, and the number of vehicles managed by a company should be considered for more accurately evaluating the management of piunctuality.

• Spatial Information Research
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• v.22 no.5
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• pp.65-75
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• 2014

Bus headway plays an important role not only in determining the passenger waiting time and bus service quality, but also in influencing the bus operation cost and passenger demand. Previous research on headway control has considered only an hourly difference in the distribution of ridership between peak and non-peak hours. However, this approach is too simple to help manage ridership demand fluctuations in a short time scale; thus passengers' waiting cost will be generated when ridership demand exceeds the supply of bus services. Moreover, bus ridership demand varies by station location and traffic situation. To address this concern, we propose a headway control algorithm for minimizing the waiting time cost by using Smart Card data. We also provide proof of the convergence of the algorithm to the desired headway allocation using a set of preconditions of political waiting time guarantees and available fleet constraints. For model verification, the data from the No. 143 bus line in Seoul were used. The results show that the total savings in cost totaled approximately 600,000 won per day when we apply the time-value cost of waiting time. Thus, we can expect that cost savings will be more pronounced when the algorithm is applied to larger systems.