• International Journal of Highway Engineering
• /
• v.17 no.1
• /
• pp.91-98
• /
• 2015

PURPOSES : Although signalized intersections have been considered the best way to control traffic volume in urban areas for several decades, roundabouts are currently being discussed as an alternative way to control traffic volume, especially when traffic is light. Because a roundabout's efficiency depends on the load geometry as well as the traffic volume, design guidelines for roundabouts are recommended only if the incoming traffic volume is very low. It is rare to substitute a roundabout for an existing signalized intersection in urban areas. This study aims to estimate the benefits from the transformation of an existing signalized intersection into a roundabout in an urban area. When there is a more moderate volume of traffic, roundabouts can be effectively used by optimizing signals located at an approaching roadway. METHODS : The methodologies of this paper are as follows: First, a signalized intersection was analyzed to determine the traffic characteristics. Second, the signalized intersection was transformed into a roundabout using VISSIM microscopic traffic simulation. Then, we estimated and analyzed the effects and the performance of the roundabout. In addition, we adjusted a method to improve the benefits of the transformation via the optimization of signals located at an approaching road to control the incoming traffic volume. RESULTS : The results of this research are as follows: The signal-optimized roundabout improved delays compared with the signalized intersection during the morning peak hour, non-peak hour, and evening peak hour by 1.78%, 12.45%, and 12.72%, respectively. CONCLUSIONS : According to the simulation results of each scenarios, the signal-optimized roundabout had less delay time than the signalized intersection. If optimized signal control algorithms are installed in roundabouts in the future, this will lead to more efficient traffic management.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.2D
• /
• pp.155-162
• /
• 2008

This study shows how to estimate the design hour factor when the counting stations don't have all of the hourly volumes such as in a coverage survey. A coverage survey records traffic volume from 1 to 5 times in a year so it lacks the detailed information to calculate the design hour factor. This study used the traffic volumes of permanent surveys to estimate the design hour factor in coverage surveys using correlation and regression analysis. A total 7 independent variables are used : the coefficient of variance of hourly volume, standard deviation of hourly volume, peak hour volume, AADT, heavy traffic volume proprotion, day time traffic volume proportion and D factor. All of variables are plotted on a curve, so it must use non-linear regression to analyze the data. As a result the coefficient of determination and MAE are good at logarith model using AADT.

• Proceedings of the Korea Society for Simulation Conference
• /
• 1999.10a
• /
• pp.93-99
• /
• 1999

The service level of Metro stations is affected by the design criteria such as platform area, width and length of stairways, and the number of turnstile, etc. The Korean station design process utilizes peak-hour volume as design volume. Hourly volume, however, can not explicitly account for the variation of traffic and consequently the variation of the level of service within the one-hour time period. Movements in various areas inside of stations are simulated with a queueing network under various operating conditions. A discrete simulation tool called SIMUL8 was utilized. Based on the results, peak volume for 15 minutes period was recommended as a preferred base volume over the peak-hour volume for station design purpose to realistically account for user delays under. Simulation runs also confirmed that escalators have positive effects on passenger processing capacity and on securing stability of passenger flows in stations.

• International Journal of Highway Engineering
• /
• v.14 no.2
• /
• pp.93-99
• /
• 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
• /
• v.12 no.2
• /
• pp.52-62
• /
• 2013

Design Hourly Factor(DHF) is defined as the ratio of design hourly volume(DHV) to Average Annual Daily Traffic(AADT). Generally DHV used the 30th rank hourly volume. But this case DHV is affected by holiday volumes so the road is at risk for overdesigning. Computing K factor is available for counting 8,760 hour traffic volume, but it is impossible except permanent traffic counts. This study applied three method to make DHF, using 30th rank hourly volume to make DHF(method 1), using peak hour volume to make DHF(method 2). Another way to make DHF, rank hourly volumes ordered descending connect a curve smoothly to find the point which changes drastic(method 3). That point is design hour, thus design hourly factor is able to be computed. In addition road classified 3 type for national highway using factor analysis and cluster analysis, so we can analyze the characteristic of DHF by road type. DHF which was used method 1 is the largest at any other method. There is no difference in DHF by road type at method 2. This result shows for this reason because peak hour is hard to describe the characteristic of hourly volume change. DHF which was used method 3 is similar to HCM except recreation road but 118th rank hourly volume is appropriate.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.6D
• /
• pp.759-766
• /
• 2008

Road classification system is the first step for determining the road function and design standards. Currently, roads are classified by various indices such as road location and function. In this study, we classify road using various traffic indices as well as to identify traffic characteristics for each type of road. To accomplish the objectives, mixture model was applied for classifying road and analyzing traffic characteristics using traffic data that observed at permanent traffic count stations. A total of 8 variables were applied: annual average daily traffic(AADT), $K_{30}$ coefficient, heavy vehicle proportion, day volume proportion, peak hour volume proportion, sunday coefficient, vacation coefficient, and coefficient of variation(COV). A total of 350 permanent traffic count points were categorized into three groups : Group I (Urban road), Group II (Rural road), and Group III (Recreational road). AADT were 30,000 for urban, 16,000 for rural, and 5,000 for recreational road. Group III was typical recreational road showing higher average daily traffic volume during Sunday and vacational periods. Group I showed AM peak and PM peak, while group II and group III did not show AM peak and PM peak.

• Journal of Urban Science
• /
• v.10 no.1
• /
• pp.49-60
• /
• 2021

The purpose of this study is to analysis the impact on the inside of the new city when an incidents occurs on the Songdo International City connecting road, which has a limited access. The analysis data used KTDB's O/D and network data of the Seoul metropolitan area. In addition, the scenario composition applied a method of reducing the number of lanes on the road according to the situation of incidents, targeting bridges advancing from Songdo International City to the outside in the morning peak hours. The analysis method analyzed the traffic volume, total travel time, total travel kilometer, and route change in the new city based on the results of the traffic allocation model. As a result of the analysis, the range of influence was shown to two types. First, of the seven bridges, Aam 3, Aam 2, and Aam 1 were analyzed to have an impact only in some areas of the northwestern part of the new city. On the other hand, the remaining bridges were analyzed to affect the new city as a whole. The analysis results of this study are expected to be used as basic data to establish the scope of internal road network management when similar cases occur in the future.

• Journal of Korean Society of Transportation
• /
• v.25 no.6
• /
• pp.79-88
• /
• 2007

Design Hourly Volume (DHV) is the hourly volume used for designing a section of road. DHV is also used to estimate the expected number of vehicles to pass or traverse the relevant section of road in a future target year. The Design Hour Factor (DHF) is defined as the ratio of DHV to Average Annual Daily Traffic (AADT). In addition to high precision of predicted traffic volume, in order to design a roadway to be the proper scale, applying appropriate DHFs considering traffic flow characteristics and type of area which surrounds the relevant roadway is important. This study categorizes sections of expressway (Suh Hae An Expressway) according to their area type and estimates DHFs utilizing traffic data obtained from a vehicle detection system (VDS). This study shows that DHFs calculated using VDS data are different from those using traffic data acquired from a coverage survey. While AADTs from both data show similar values, peak hour volumes from both data show significant differences especially for recreational areas. DHFs from the coverage survey are quite different from the values provided by the Korean design guide or previous research results and DHFs for urban areas are higher than recreational areas. However, DHFs from VDS shows similar values to previous research results. The result of this study suggests that using VDS for estimating DHFs is more reliable than using a coverage survey.

• Proceedings of the KSR Conference
• /
• 2007.05a
• /
• pp.1913-1926
• /
• 2007

Number of highway lanes and level of service are based on the 30th, 60th or 100th highest hour volume of the target year. On the other hand, railroad design volume is generally expressed in average annual daily traffic (AADT). This procedure ignores attributes of railroad demand's concentration on peak days and hours. Actual data analysis represents railroad's peaking characteristics very similar to those of highways. Therefore it is necessary to set a procedure in selecting design volume for railroad planning. This study presents a concept and the calculating procedure for railroad design hourly volume to analyze effect of railroad investment.

• International Journal of Highway Engineering
• /
• v.7 no.3 s.25
• /
• pp.43-52
• /
• 2005

Highway classification is an essential part of defining design criteria of roads. This study is to classify highways by factor analysis. To accomplish the objectives, factor analysis is performed for classifying highways using the traffic data observed at the permanent traffic count points in 2004. A total off variables are applied : AADT, K factor, D factor, heavy vehicle proportion, day time traffic volume proportion, peak hour volume proportion, sunday factor, vacation factor and COV(Coefficient of Variation). The results of factor analysis show that variables are divided into two factors, which are the factor related to the fluctuational characteristics of traffic volume and the factor related to heavy vehicle and directional volume characteristics. According to the results of cluster analysis, 353 permanent traffic count points are categorized into such three groups as type I for urban highway, type II for rural highway, type III for recreational highway, respectively.