• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.33-42
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• 2000

This Paper is a Part of research Project series to analyze unique traffic characteristics observable within weaving sections on urban freeways. The research objectives were to establish with headway distribution and maximum Passing volume on weaving sections the basis of weaving designs that can promote safety and efficiency. Until now, when one wants to check the maximum Passing volume on weaving sections, it is taken for granted using headway distribution of freeway basic section. However. it was suspected in this research that for weaving sections different form of headway distribution had better be used. To prove this, field surveys were made to count headway intervals which supposedly were influenced not only by freeway basic section flows but also by weaving flows and later on used to develop headway distribution for weaving sections. For validation of the developed headway distribution, $x^2$-test was applied to three different data set of observed headways, currently used headway distribution for basic sections(Pearson Type III distribution) and new headway distribution. The result indicated new headway distribution as the most appropriate distribution form. Also, maximum passing volume within weaving sections was calculated based on new headway distribution and compared with Drew's maximum Passing volume.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.249-255
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• 2018

This paper presents a human-centered control algorithm for personalized autonomous driving based on the integration of inverse time-to-collision and time headway. In order to minimize the sense of difference between driver and autonomous driving, the human-centered control technology is required. Driving characteristics in case that vehicle drives with the preceding vehicle have been analyzed and reflected to the longitudinal control algorithm. The driving characteristics such as acceleration, inverse time-to-collision, time headway have been analyzed for longitudinal control. The control algorithm proposed in this study has been constructed on Matlab/Simulink environment and the performance evaluation has been conducted by using actual driving data.

• 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.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.23-31
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• 2002

The headway of vehicles entering an intersection is closely related with the saturation flow rate and is a basic parameter required for determining the saturation headway and the start-up lost delay. Since such headway value reflects the drivers' behaviors and features of the intersection, all intersections don't have an equal value, but are affected by number and location of their lanes, changing types, local characteristics and time zone. Accordingly, this study attempted to suggest proper values on the basis of data by investigating headway in lanes. Number of exclusive through lanes was divided into single lane, double lanes and triple lanes, the locations of lanes were divided into inside lane, central lane and outside lane. As a result of investigating the headway, single through lane, double through lanes-inside lane, and triple through lane-outside lane showed as 1.73 sec., 1.71 sec. and 1.93sec., respectively. The result of calculating the area factor of business areas by fixing 1.00 for the residental area and applying relation between headway and saturation flow rate was 0.96. In the case of start-up lost delay lead dual left turn and directional separation were 1.41 sec. and 3.27 sec., respectively, showing the great difference. Therefore, different start-up lost delay according to changing type should be applied.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.79-90
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• 2019

This study aims to develop time headway distribution models of bicycle traffic flow in a uninterrupted bikeway. The sample data were collected and classified into two groups of traffic volume levels. The lower level traffic volume is defined to be under 8 bicycles per minute, and the higher one is greater or equal to 8 bicycles per minute. The data aggregation interval size was set to be 0.5-second. Four distribution models including normal distribution, negative exponential distribution, shifted negative exponential distribution, and Pearson III distribution were tested, and Chi-square test results shows that the negative exponential distribution and the shifted negative exponential distribution are well fitted to the sample data. Another test results with different sample data also shows the same conclusion.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.31-39
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• 2002

The primary objective of this study is to develop a reliable method for estimating the left turn capacity at the signalized intersection. This study is performed during periods of congestion. Multi left turn lane(bay lane and exclusive lane) approaches are examined. When more than one left turn lane exists, traffic volumes are not distributed equally over each lane. The fundamental approach taken in this study is measuring headways on left turn lanes with altering the bay length from 20m to 120m. Left turn lane is divided into 3 sub-sections in this study. These are SLP section(start-up lost time Period), SFP section(saturation flow period), LSP section(lane selection period). Saturation flow rates are evaluated for each sub section periods. As a results of analysis, it has been confirmed that the left turn capacity can be estimated by left turn bay length and effective green time for left turn. The left turn bay length adjustment factor is suggested in this study.

• International Journal of Highway Engineering
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• v.14 no.2
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• pp.93-99
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• 2012

This study analyzes traffic data which are collected by VDS(Vehicle Detection System) to research the relationship between spacing distribution and vehicles' relative speed. The collected data are relative speed between preceding and following vehicles, passing time and speed. They are also classified by lane and direction. For the result of the analysis, in the same platoon, we figure out that mean of spacing is 40m, which can be a value to determine section A to D. To compare spacing according to time interval, this study splits time intervals to peak hour and non-peak hour by peak hour traffic volume. In conclusion, vehicles in peak hour are in car following because most drive similar speed as preceding vehicle and they have relatively small spacing. On the other hand, non-peak hour's spacing between vehicles is bigger than that of peak hour. This implies driver's behaviors that the less spacing, the more aggressive and want to reduce their travel time in peak hour, whereas most drive easily in non-peak hour and recreational trip purpose because of less time pressure.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.35-47
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• 2023

Various single probability distributions have been used to represent time headway distributions. However, it has often been difficult to explain the time headway distribution as a single probability distribution on site. This study used the EM algorithm, which is one of the maximum likelihood estimations, for the parameters of combined mixture distributions with a certain relationship between two normal distributions for the time headway of vehicles. The time headway distribution of vehicle arrival is difficult to represent well with previously known single probability distributions. But as a result of this analysis, it can be represented by estimating the parameters of the mixture probability distribution using the EM algorithm. The result of a goodness-of-fit test was statistically significant at a significance level of 1%, which proves the reliability of parameter estimation of the mixture probability distribution using the EM algorithm.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.251-258
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• 2006

This study is to develop an estimation method of freeway design capacity through the analysis of time headway distribution in continuum flow. Traffic flow-speed diagram and time headway distribution plotted from individual vehicle data shows: a) a road capacity is not deterministic but stochastic, b) time headway distribution for each vehicle speed group follows pearson type V distribution. The freeway design capacity estimation model is developed by determining a minimum time headway for capacity with stochastic method. The estimated capacity values for each design speed are lower when design speed ${\leq}80km/h$, and higher when design speed ${\geq}106km/h$ in comparison with HCM(2000)'s values. In addition, The distinguish difference is that this model leads flexible application in planning level by defining the capacity as stochastic distribution. In detail, this model could prevent a disutility to add a lane for only one excess demand in a road planning level.