• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.2057-2067
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• 2013

Traffic demand forecasting for pavement management in the present can be estimated using the past trends or subjective judgement of experts instead of objective methods. Also future road plans and local development plans of a target region, for example new road constructions and detour plans cannot be considered for the estimate of future traffic demands. This study, which is the fundamental research for developing objective and accurate decision-making support system of maintenance management for the national highway, proposed the methodology to predict future traffic demands according to 4-step traffic forecasting method using EMME in order to examine significance of future traffic demands affecting pavement deterioration trends and compare existing traffic demand forecasting methods. For the case study, this study conducted the comparison of traffic demand forecasting methods targeting Daejeon Regional Construction and Management Administration. Therefore, this study figured out that the differences of traffic demands and the level of agent costs as well as user costs between existing traffic demand forecasting methods and proposed traffic demand forecasting method with considering future road plans and local development plan.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.1-14
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• 2013

Annual average daily traffic (AADT) serves the important basic data in transportation sector. Despite of its importance, AADT is estimated through permanent traffic counts (PTC) at limited locations because of constraints in budget and so on. At most of locations, AADT is estimated using short-term traffic counts (STC). Though many studies have been carried out at home and abroad in an effort to enhance the accuracy of AADT estimate, the method to simplify average STC data has been adopted because of application difficulty. A typical model for estimating AADT is an adjustment factor application model which applies the monthly or weekly adjustment factors at PTC points (or group) with similar traffic pattern. But this model has the limit in determining the PTC points (or group) with similar traffic pattern with STC. Because STC represents usually 24-hour or 48-hour data, it's difficult to forecast a 365-day traffic variation. In order to improve the accuracy of traffic volume prediction, this study used the geostatistical approach called co-kriging and according to their reports. To compare results, using 3 methods : using adjustment factor in same section(method 1), using grouping method to apply adjustment factor(method 2), cokriging model using previous year's traffic data which is in a high spatial correlation with traffic volume data as a secondary variable. This study deals with estimating AADT considering time and space so AADT estimation is more reliable comparing other research.

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.53-63
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• 2003

The Origin-Destination(OD) matrix is very important in describing transport movements in a region. The OD matrix can be estimated using traffic counts on links in the transport network and other available information. This information on the travel is often contained in a target OD matrix and traffic counts in links. To estimate an OD matrix from traffic counts, they are the major input data which obviously affects the accuracy of the OD matrix estimated, Generally, the quality of an estimated OD matrix depends much on the reliability of the input data, and the number and locations of traffic counting points in the network. Any Process regarding the traffic counts such as the amount and their location has to be carefully studied. The objective of this study is to select of the optimal location of traffic counting points for the OD matrix estimation. The model was tested in nationwide network. The network consists of 224 zones, 3,125 nodes and 6,725 links except to inner city road links. The OD matrix applied for selection of traffic counting points was estimated to 3-constrained entropy maximizing model. The results of this study follow that : the selected alternative to the best optimal counting points of six alternatives is the alternative using common links of OD matrix and vehicle-km and traffic density(13.0% of 6,725 links), however the worst alternative is alternative of all available traffic counting points(44.9% of 6,725 links) in the network. Finally, it should be concluded that the accuracy of reproduced OD matrix using traffic counts related much to the number of traffic counting points and locations.

• Journal of the Korean Society of Hazard Mitigation
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• v.6 no.1 s.20
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• pp.17-27
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• 2006

To estimate the retained strength of reinforced concrete structure after fire is very difficult because the complex behavior of structure is hard to understand during course of a fire. However, the damages which is caused by fire of the traffic facility infrastructure are enormous. Therefore the security against fire is important element that must not be overlooked. For this reason, an exact estimate method of the fire endurance is highly demanded. In this study, the validity of the nonlinear finite element method approach for the fire endurance of reinforced concrete structure is verified. The results of fire endurance estimate of underground road way by nonlinear finite element method approach are compared with those by ACI 216R-89.

• Journal of Korean Society of Transportation
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• v.30 no.5
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• pp.71-82
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• 2012

Some previous studies adopted a method statistically based on the observed traffic volumes and travel times to estimate the parameters. Others tried to find an optimal set of parameters to minimize the gap between the observed and estimated traffic volumes using, for instance, a combined optimization model with a traffic assignment model. The latter is frequently used in a large-scale network that has a capability to find a set of optimal parameter values, but its appropriateness has never been demonstrated. Thus, we developed a methodology to estimate a set of parameter values of BPR(Bureau of Public Road) function using Harmony Search (HS) method. HS was developed in early 2000, and is a global search method proven to be superior to other global search methods (e.g. Genetic Algorithm or Tabu search). However, it has rarely been adopted in transportation research arena yet. The HS based transportation network calibration algorithm developed in this study is tested using a grid network, and its outcomes are compared to those from incremental method (Incre) and Golden Section (GS) method. It is found that the HS algorithm outperforms Incre and GS for copying the given observed link traffic counts, and it is also pointed out that the popular optimal network calibration techniques based on an objective function of traffic volume replication are lacking the capability to find appropriate free flow travel speed and ${\alpha}$ value.

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.103-113
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• 2003

This paper shows alternative route search and preference route search for the traffic route search, and proposes the use of the fuzzy non-additive controller by the application of AHP(analytic hierarchy process). It is different from classical route search and notices thinking method of human. Appraisal element, weight of route is extracted from basic of the opinion gathering for the driving expert and example of route model was used for the finding of practice utility. Model evaluation was performed attribute membership function making of estimate element, estimate value setting, weight define by the AHP, non additive presentation of weight according to $\lambda$-fuzzy measure and Choquet fuzzy integral. Finally, alternative route search was possible to real time traffic route search for the well variable traffic environment, and preference route search showed reflection of traffic route search disposition for the driver individual. This paper has five important meaning. (1)The approach is similar to the driver's route selection decision process. (2)The approach is able to control of route appraisal criteria for the multiple attribute. (3)The approach makes subjective judgement objective by a non additive. (4)The approach shows dynamic route search for the alternative route search. (5)The approach is able to consider characteristics of individual drivers attributed for the preference route search.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.25-33
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• 2014

This study investigates the reliability of DSRC-based traffic information system on the typical urban arterial with the minimum sample size method. VISSIM has been employed to calculate the required sample size. After comparing the number of hi-pass vehicles recorded from DSRC and the required sample size, this study found that the interrupted traffic state tends to generate more outliers than the uninterrupted one, the lack of the number of vehicles completely passing links with multiple driveways makes it difficult to estimate the reliable traffic information, the traffic information during peak hour is relatively more reliable than that during off-peak hour, and the reliability of DSRC-based traffic information system depends on the significance level in calculating the sample size. The driveway density and traffic signal operation due to the individual link length significantly affects the required sample size, resulting in determining the reliability of the DSRC-based traffic information system.

• Journal of Navigation and Port Research
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• v.31 no.6
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• pp.447-454
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• 2007

To assess the port development and maritime traffic environment, the future traffic volume has been estimated using the number of inbound and outbound vessel for a specific port. The estimation of future traffic volume should be considered as an important factor to establish the degree of fairway congestion, the determination of fairway width and the operational role. Until now, the number of in and out vessel for the port has been only estimated mainly, but the type and size of inbound and outbound ships are different depending on the port's characteristics. So, it is difficult to estimate the future traffic volume using the change of only one item. This paper calculates the future traffic volume using the marine traffic characteristic factors as the number of coastal ship and ocean-going ship, the size of ship and the change of cargo volume per a ship etc. And it compared with the results of Artificial Neural Network(ANN) for accurate identification of nonlinear system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.22-32
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• 2013

Since the percentage of vehicles equipped with Hi-pass, an electronic toll collecting device, has increased rapidly, it is very crucial to determine the optimal location of Hi-pass lanes at a toll plaza in terms of traffic control and operation. In this study, the appropriateness of existing Hi-pass lanes of a toll plaza is evaluated considering its physical geometry and traffic characteristics. A new evaluating criterion called "traffic dispersion rate" is developed in order to measure the level of traffic spreading across the toll booth lanes at a toll plaza. Logistic regression models are constructed to estimate the relationship between the traffic dispersion rate and its affecting variables. The model estimation results show that several variables including Hi-pass lane traffic volume, length of toll plaza, entering/exiting taper lengths, and locations of Hi-pass lanes. The results also suggest that traffic dispersion rate can be increased by adjusting the location of Hi-pass lanes. The study enables us to quantify traffic dispersion rate which can be used to optimize the location and operation of Hi-pass lanes at toll plazas.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.353-361
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• 2020

Due to the rapid urbanization, various traffic problems such as traffic jams during commute and regular traffic jams are occurring. In order to solve these traffic problems, it is necessary to quickly and accurately estimate and analyze traffic volume. ITS (Intelligent Transportation System) is a system that performs optimal traffic management by utilizing the latest ICT (Information and Communications Technology) technologies, and research has been conducted to analyze fast and accurate traffic volume through various techniques. In this study, we proposed a deep learning-based vehicle detection method using UAV (Unmanned Aerial Vehicle) video for real-time traffic analysis with high accuracy. The UAV was used to photograph orthogonal videos necessary for training and verification at intersections where various vehicles pass and trained vehicles by classifying them into sedan, truck, and bus. The experiment on UAV dataset was carried out using YOLOv3 (You Only Look Once V3), a deep learning-based object detection technique, and the experiments achieved the overall object detection rate of 90.21%, precision of 95.10% and the recall of 85.79%.