• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.109-120
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• 2013

In this paper we proposed the design of the hybrid road lighting control system using solar-light generation. The proposed hybrid road lighting control system be power offer through hybrid controller using Solar-Light Generation, and it is designed so that it can control lighting up. To control supply of continuous power when during power shortages. And the gateway be transmit control command using zigbee to road lighting to ensure that automatic lighting control on human sensing. In this case, the gateway is apply the lighting control algorithm that decisions to the status of the system by a pre-set time schedule and be able to operate. In this paper, the proposed efficiency analysis results of a hybrid road lighting control system was consumed power of 129.6W per day, 3.8KW per month, 47.3KW per annual. As a result, it were able to increase the energy efficiency than existing lighting control system by reduce power consumption of 76.2% and the electricity prices of 76.8%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.155-163
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• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

• Journal of Korean Society of Forest Science
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• v.99 no.1
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• pp.1-8
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• 2010

The optimum forest road density was calculated with the method which is used in Europe on the investigation site in Korea. The economical optimum forest road density at the minimum total transport cost was 10.51 m/ha. The total transport cost was calculated 235,354 won/ha per year. The forest road construction cost amounted to 99,693 won/ha per year in case of the depreciation period of 30 years and the interest rate of 3%, the forest road maintenance cost amounted to 14,502 won/ha per year, the logging cost amounted to 99,564 won/ha per year, the cost of footpaths amounted to 18,142 won/ha per year, the cost by the loss of the production area amounted to 3,454 won/ha per year.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.1-12
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• 2015

Intrusive Vehicle Detectors have excellent detection performance compared to other types of detector, but disadvantages of high installation and maintenance costs, short life time due to greater damage to roads and paving materials. In contrast, Non-Intrusive Vehicle Detectors attached to the stationary pole have advantages because it does not damage the road surface and easy and less expensive to maintain. Despite these advantages, Non-Intrusive type detectors are still not been widely used in traffic signal control systems because of the low detection performance. In this study, a FMCW(Frequency Modulated Continuous Wave) radar Vehicle Detector was designed as an alternative detector for the signalized intersection, and the performance evaluation was presented by purpose applicability.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.59-74
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• 2021

The domestic road space is reaching the limit of planar space distribution, and Increasingly, the importance of three-dimensional space distribution through the development of underground space. therefore, In this study, a study was conducted on a traffic control method that can safely induce two different traffic flows in the connection between the ground road and the underground road. Through VISSIM, we calculated the appropriate amount of outflow and inflow traffic compared to the capacity of the main line when there is a Merge/Diverge section in the underground road. and Through the analysis of the number of conflicts, the appropriate traffic control level for safety in the underground, A basic study was conducted on the level of risk in the underpass according to the level of delay in the ground part through the analysis of the delay scenario of the ground road.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.106-118
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• 2019

In recent years, research and development on image object recognition using artificial intelligence have been actively carried out, and it is expected to be used for road maintenance. Among them, artificial intelligence models for object detection of road surface are continuously introduced. In order to develop such object recognition algorithms, a backbone network that extracts feature maps is essential. In this paper, we will discuss how to select the appropriate neural network. To accomplish it, we compared with 4 different deep neural networks using 6,000 road surface damage images. Based on three evaluation methods for analyzing characteristics of neural networks, we propose a method to determine optimal neural networks. In addition, we improved the performance through optimal tuning of hyper-parameters, and finally developed a light backbone network that can achieve 85.9% accuracy of road surface damage classification.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.273-286
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• 2022

An HD map is essential in the automated driving of level 4 and above to generate the vehicle's global path since it contains road information and each road's lane information. Therefore, all the road elements in the HD map must be correctly defined to construct the correct road network necessary to generate the global path. But unfortunately, it is not difficult to find various errors even in the most recent HD maps. Hence, a preprocessing algorithm has been developed to detect and correct errors in the HD map. This error detection and correction result in constructing the correct road network for use in global path planning. Furthermore, the algorithm was tested on real roads' HD maps, demonstrating its validity.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.4
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• pp.55-66
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• 2020

This study developed four algorithms and their associated indices that can quantify and qualify road hazards along roadways. Initially, relevant raw data can be collected from commercial vehicles by camera and DTG. Well-processed data, such as potholes, road freezing, and fog, can be generated from the Integrated management system. Road hazard algorithms combine these data with road inventory data in the Data Sharing Platform. Depending on well-processed data, four different road hazard algorithms and their associated indices were developed. To test the algorithms, an experimental plan based on passive DTG attached in probe vehicles was performed at two different test locations. Selection of the test routes was based on historical data. Although there were limitations using random data for commercial vehicles, hazardous roadways sections, such as fog, road freezing, and potholes, were generated based on actual historical data. As a result, no algorithm error was found in the entire test. Because this study provides road hazard information according to a section, not a point, it can be practically helpful to road users as well as road agencies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.74-88
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• 2020

A Cooperative Intelligent Transportation System (CITS) provides useful information on upcoming hazards in order to prevent vehicle collisions. In addition, the availability of individual vehicle travel information obtained from the CITS infrastructure allows us to identify the level of road safety in real time and based on analysis of the indicators representing the crash potential. This study proposes a methodology to derive road safety content, and presents evaluation results for its applicability in practice, based on simulation experiments. Both jerk and Stopping Distance Index (SDI) were adopted as safety indicators and were further applied to derive road section safety information. Microscopic simulation results with VISSIM show that 5% and 20% samples of jerk and SDI are sufficient to represent road safety characteristics for all vehicles. It is expected that the outcome of this study will be fundamental to developing a novel and valuable system to monitor the level of road safety in real time.