• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.675-685
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• 2014

This paper develops a differentiated pricing strategy over each segment of expressways based on the second-best pricing method for reducing congestion. To this end, a bi-level problem is proposed, in which the upper level of the model is formulated to determine toll level of each segment for minimizing traffic congestion, whereas the lower level of the model is formulated as a variable demand assignment problem. The sensitivity analysis based algorithm is took placed to find optimal solutions of upper level model. An application of the proposed model uses the modified Sioux-Falls network. The results show that the segment-based differentiated pricing strategy performs better than the existing uniform pricing strategy in reducing traffic congestion. This study can be applied as a demand management method to relieve disutility of excessively congested segments of expressways.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.45-54
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• 2013

Korea Expressway Corporation has been utilizing vehicles equipped with dedicated short range communication (DSRC) based on-board equipment (OBE) for collecting path travel times. A path based method (PBM) estimates the path travel time using probe vehicles traveling whole links on the path, so it is not always possible to obtain sufficient samples for calculating path travel time in the DSRC system. Having this problem in utilizing DSRC for travel time information, this study attempted to estimate path travel time with the help of a link based method (LBM) and examined whether the LBM can be used for obtaining reliable path travel times. Some comparisons were made and identified that the MAPE difference between the LBM and the PBM estimates are less than 3%, signaling that LBM can be used as a proxy for PBM in case of sparse sample conditions. Some limitations and a future research agenda have also been proposed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.33-40
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• 2002

This paper presents the evaluation result of the Namsan 1st tunnel traffic information system, which is providing the real time queue length status and incidence information inside of the Namsan 1st tunnel, 1600meters long in the Seoul metropolitan CBD. This information system plays an important role because of notifying drivers to pay a toll for the tunnel path, the congestion information in advance. According to the analysis, an evident improvement on the tunnel path and its bypass without any conspicious adverse impact. Furthermore, survey analysis found that motorists placed a high value on the provided information. During peak time, the system turned out to help motorists to relax their hasty minds. As well, several performance indices showed the reduction of congestion and travel time variation on both of the tunnel path and its bypass, in spite of no traffic information on the bypass.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.52-57
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• 2002

A simulation tool for an optimal ATIS design and drivers' dynamic route choice behavior analysis is developed, which is applicable to urban networks. Due to the difficulty to make drivers feel the time pressure according to traffic conditions, current SP questionnaire survey type surveys have a limitation to capture correct driver reactions to real-time traffic Information provision. The simulator Is a web-based upgraded version, named WATiSim (Web-based ATIS Simulator), to quickly perform a wide population survey with a minimal cost using INTERNET Furthermore, the time pressure issue is lessened by its interface and simulation modules. After WATiSim mimicked a VMS based ATIS in a partial network of Seoul Metropolitan, reactions of drivers to various traffic conditions were surveyed through INTERNET and analyzed using a logit model. Drivers under the ATIS environment clearly understood the provided traffic information, and their reactions were closely related to traffic conditions, scheduled delay, trip purposes as well as toll charge if any.

• Korean Journal of Construction Engineering and Management
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• v.19 no.1
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• pp.3-11
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• 2018

In order to find methods to encourage active private participation for efficient road maintenance, this study examined the status of the road improvement project and the development of the private capital utilization improvement project. Based on this, the study conducted a case study and a financial feasibility study that applied the new business method and selected the applicable business method. As a result, it is analyzed that the method of using Crowd Funding is more advantageous than the private scheme of the private capital utilization improvement project. This is because it will be highly effective in raising funds from users of highway users, especially users of improvement projects. In addition, it can also mean that the road users who suffer from inconvenience due to the construction can return the toll profit of road construction to the user.

• KDI Journal of Economic Policy
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• v.33 no.4
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• pp.143-177
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• 2011

Upon selecting preferred bidder in Public-Private Partnership projects, multi-dimensional procurement auction, where price factor and non-price factor are evaluated, is used. This paper tries to analyze bidding data in BTO road projects. It is shown that a winner tends to get higher score in bidding evaluation, which is partly due to increase in base score as well as fiercer competition among bidders. It turns out that score margin in non-price factor was determinant in selecting winner. Also, there was no competition when the level of bonus point was set too high. For price factor, it costs 730 million KRW per score in construction subsidy by government, while it costs 2.43 billion KRW per score in toll revenue. For non-price factor, it was estimated to cost 2.30 billion KRW. Based on the results, it was suggested that we should have appropriate level of bonus point for first initiator, change in scoring rule in construction subsidy part, adjustment of base score in evaluation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.269-279
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• 2020

Traffic estimation mainly involves surveying equipment such as automatic vehicle classification, vehicle detection system, toll collection system, and personnel surveys through CCTV (Closed Circuit TeleVision), but this requires a lot of manpower and cost. In this study, we proposed a method of estimating traffic volume using deep learning and stereo CCTV to overcome the limitation of not detecting the entire vehicle in case of single CCTV. COCO (Common Objects in Context) dataset was used to train deep learning models to detect vehicles, and each vehicle was detected in left and right CCTV images in real time. Then, the vehicle that could not be detected from each image was additionally detected by using affine transformation to improve the accuracy of traffic volume. Experiments were conducted separately for the normal road environment and the case of weather conditions with fog. In the normal road environment, vehicle detection improved by 6.75% and 5.92% in left and right images, respectively, than in a single CCTV image. In addition, in the foggy road environment, vehicle detection was improved by 10.79% and 12.88% in the left and right images, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.511-520
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• 2016

Automatic License Plate Detection (ALPD) is a key technology for a efficient traffic control. It is used to improve work efficiency in many applications such as toll payment systems and parking and traffic management. Until recently, the hand-crafted features made for image processing are used to detect license plates in most studies. It has the advantage in speed. but can degrade the detection rate with respect to various environmental changes. In this paper, we propose a way to utilize a Faster Region based Convolutional Neural Networks (Faster R-CNN) and a Conventional Convolutional Neural Networks (CNN), which improves the computational speed and is robust against changed environments. The module based on Faster R-CNN is used to detect license plate candidate regions from images and is followed by the module based on CNN to remove False Positives from the candidates. As a result, we achieved a detection rate of 99.94% from images captured under various environments. In addition, the average operating speed is 80ms/image. We implemented a fast and robust Real-Time License Plate Detection System.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.349-352
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• 2006

To cope with traffic congestion, traffic accidents and lack of parking facilities, caused by dramatic increase in total vehicle number, vigorous researches on managing vehicles efficiently are done, both domestically and internationally. The vehicle license plate recognition makes effective management of traffic possible, with its wide application in many fields, covering from speed enforcement, collecting toll, stolen vehicle detection to parking management. The vehicle license plate recognition system causes high cost for collecting training data. Many researches are done by using the virtual sample method, which can be effective for utilizing limited number of training data by generating virtual sample. This paper investigates techniques to improve the performance of vehicle license plate recognition by using the training data's annexation. Also, popular methods for virtual sample creation used for text recognition algorithm are analyzed and their effectiveness is verified.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.83-95
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• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.