• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.53-64
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• 2013

The car-following model is one of core models in Advanced Vehicle & Highway Systems (AVHS). The car-following model has been developed in aspects such as human factor and reduction error rates. However, the consideration of safety depending on weather condition has not been completed yet. In this paper, therefore, changes of driving condition for car-following due to different road condition were dealt with, and optimal safety distance corresponding to road condition such as dry, wet and snowy were computed. The GMIT(GM Model with Instantaneous T) model was picked over for simulation of adaptive cruise control applied the suggested optimal safety distance. As the results, the 1.7 times longer safety distance was required for wet road condition than dry road condition, and the 5.6 times longer safety distance was required for snowy road condition.

• Journal of KIBIM
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• v.6 no.2
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• pp.29-38
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• 2016

Road bridges are deteriorating gradually, and it is forecasted that the number of road bridges aging over 30 years will increase by more than 3 times of the current number. To maintain road bridges in a safe condition, current safety conditions of the bridges must be estimated for repair or reinforcement. However, budget and professional manpower required to perform in-depth inspections of road bridges are limited. This study proposes an estimation model for safety rating of road bridges by analyzing the data from Facility Management System (FMS) and Yearbook of Road Bridges and Tunnel. These data include basic specifications, year of completion, traffic, safety rating, and others. The distribution of safety rating was imbalanced, indicating 91% of road bridges have safety ratings of A or B. To improve classification performance, five safety ratings were integrated into two classes of G (good, A and B) and P (poor ratings under C). This rearrangement was set because facilities with ratings under C are required to be repaired or reinforced to recover their original functionality. 70% of the original data were used as training data, while the other 30% were used for validation. Data of class P in the training data were oversampled by 3 times, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was used to develop the estimation model. The results of estimation model showed overall accuracy of 84.8%, true positive rate of 67.3%, and 29 classification rule. Year of completion was identified as the most critical factor on affecting lower safety ratings of bridges.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.5
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• pp.180-187
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• 2007

This paper presents a mathematical vehicle model that is designed to analyze the dynamic performance and to develop various safety control systems. Wheel slip controllers for ABS is also formulated to improve the vehicle response and to increase the safety on slippery road. Validation of the model and controller is performed by comparison with a commercial software package, CarSim. The result shows that performances of developed vehicle model are in good accordance with those of the CarSim on various driving conditions. Developed ABS controller is applied to the vehicle model and CarSim model, and it achieves good control performance. ABS controller improves lateral stability as well as longitudinal one when a vehicle is in turning maneuver on slippery road. A driver model is also designed to control steer angle of the vehicle model. It also shows good performance because the vehicle tracks the desired lane very well.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.57-62
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• 1994

In order to establish a prediction method for road traffic noise generated from actual traffic flow, a new approach is proposed for practical use. One block in urban road is regarded as one box in this study. This prediction method is able to treat any kind of road traffic noise generated from one block. The validity of the proposed prediction method has been experimentally confirmed by applying it to actually observed road traffic noise data. The correlation between observed and predicted noise level is good.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.395-406
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• 2008

An excavation with the depth of 32.7m will be constructed as a ventilation shaft in Shanghai metro Line 9. The excavation induced effect on a nearby undercrossing road in operation must be properly evaluated. A centrifuge model test was conducted to study the impact of deep excavation on this existing undercrossing. Detail simulation works are described in this paper. The excavation steps could be simulated in the no-stop state of centrifuge machine. And induced settlements of the undercrossing road in both parallel and vertical directions were analyzed. Protective partition cement soil piles were also simulated in the tests. Simulation test shows deep excavation has a great influence on undercrossing road and the partition pile can obviously deduce the influence.

• Spatial Information Research
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• v.16 no.1
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• pp.1-10
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• 2008

`Information measure model' and 'Information benefit model' for road plan are developed. In result of application to actual road project, 'Information measure model' showed same result with actual road plan, it can express qualitative element by 'Information' like safety, public discontent, etc. and 'Information benefit model' can evaluate present plan in the side of provider's and demader's 'benefit'.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6D
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• pp.829-837
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• 2011

Construction cost estimation in planning phase which calculates the cost for performing construction tasks is used for various ways. Meanwhile, in the case of road construction, the existing cost estimating method in early phase based on numerical mean value of the past is not accurate to be used. This paper propose neural network model for estimating road construction cost in planning phase to solve the limit of current cost estimating method. The model was designed using past road construction bidding records, and variables of model were optimized through trial and error. The estimation result of the model was compared with regression analysis and government's standard and it was verified that the model is better in accuracy. It is expected that the proposed model will be used for road cost estimation in planning phase.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.133-148
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• 2022

In this study, a road pavement crack deterioration model was developed for a pavement road sections of the Sejong-city. Data required for model development were acquired using a deep learning-based road asset monitoring system. Road pavement monitoring was conducted on the same sections in 2021 and 2022. The developed model was analyzed by dividing it into a method for estimating the annual average amount of deterioration and a method based on Bayesian Markov Mixture Hazard model. As a result of the analysis, it was found that an analysis results similar to the crack deterioration model developed based on the data acquired from the Automatic pavement investigation equipmen was derived. The results of this study are expected to be used as basic data by local governments to establish road management plans.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.52-57
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• 2006

One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.

• Journal of the Ergonomics Society of Korea
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• v.21 no.3
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• pp.25-34
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• 2002

The road sign in dynamic traffic system is an important element which affects on human cognitive performance on driving. Web-based vision system simulator was developed to examine the cognition time of the road sign in dynamic environment. This experiment the cognition time of the road sign in dynamic environment. This experiment was designed in with-subject design with two factors: vehicle speed and the amount of information of the traffic sign. It measured the cognition time of the road sign through two evaluation methods: the subjective test with vision system simulator and computational cognitive model. In these two evaluations of human cognitive performance under the dynamic traffic environment, it demonstrated that subject's cognition time was affected by both the amount of information of traffic sign and driving speed.