• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.1
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• pp.1-15
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• 2011

The RLC systems is one of the intelligent transportation systems that has gained a nation-wide support for last decades and being installed to discourage motorists from running the red lights at signalized intersections. It is taken for granted that the RLC will provide motorists with increased safety, so that their installments are always justifiable. However, in order to acquire more efficiency and wider supports from the general public in future RLC installments, an improved methodology for analyzing the effects of the RLC systems is required. In order to satisfy this requirement, this research performed the following tasks. First, the number of signal violations after the RLC systems were investigated in order to check its resulting effects. Second, the number of crashes after the RLC systems were collected and compared with the number of signal violations. Third, a statistical analysis was carried out to develop the relationships between the signal violations and the crashes based on negative binomial distribution. The analysis revealed that the number of crashes has a close relationship with the RLC placement, traffic volume, vehicle speed, the number of phases, and the number of lanes for major approaches. Finally, based on the results found in this analysis, this research presents a methodology for analyzing the safety effects of placing the RLC that should be of service when investigating the economic consequences of the RLC systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.15-23
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• 2011

Driving directly in front of the driver of a vehicle driving in front of the vehicle and it is commonly known is affected. Responding to the car in front of the driver and the vehicle in front, and these follow the model is known as Three-Car-Following Model. Platoon vehicles to follow behind the driver's visibility is affected by the a tinted vehicle, and Parameters of the model is estimated to be affected also. In this study, in Three-Car-Followng Model parameters were estimated. and the parameter values differ about whether and how analysis was performed by the level of Visible Light Transmission Percentage. RTK GPS receiving data through field experiment analyzed based on sensitivity of three car by Visible Light Transmission Percentage and ${\gamma}$. And With statistical verification of driving directly in front of the driver in front of the vehicle and that the moving vehicle is influenced also confirmed. Also Visible Light Transmission Percentage is lowered, the vehicle in front of the driver's behavior showed sensitive reactions. In the further need to research for influence analysis of traffic flow capacity by the level of VLT.

• Smart Structures and Systems
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• v.6 no.3
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• pp.309-333
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• 2010

Civil infrastructure, in both its construction and maintenance, represents the largest societal investment in this country, outside of the health care industry. Despite being the lifeline of US commerce, civil infrastructure has scarcely benefited from the latest sensor technological advances. Our future should focus on harnessing these technologies to enhance the robustness, longevity and economic viability of this vast, societal investment, in light of inherent uncertainties and their exposure to service and even extreme loadings. One of the principal means of insuring the robustness and longevity of infrastructure is to strategically deploy smart sensors in them. Therefore, the objective is to develop novel, durable, smart sensors that are especially applicable to major infrastructure and the facilities to validate their reliability and long-term functionality. In some cases, this implies the development of new sensing elements themselves, while in other cases involves innovative packaging and use of existing sensor technologies. In either case, a parallel focus will be the integration and networking of these smart sensing elements for reliable data acquisition, transmission, and fusion, within a decision-making framework targeting efficient management and maintenance of infrastructure systems. In this paper, prudent and viable sensor and health monitoring technologies have been developed and used in several large structural systems. Discussion will also include several practical bridge health monitoring applications including their design, construction, and operation of the systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.7-14
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• 2012

We frequently confront with cars interfering into our lane on the ramp. We suffered from serious traffic congestion due to the interfering cars. But the police enforcement has not done actively because it's hard to enforce. In this study, we have evaluated the systems to enforce cutting-in cars through the field test. Generally, the image processing method depends on the weather. To overcome this limitation we proposed a new algorithm combined with section detection method. In the filed test we concluded the results as follows. Whereas the violation detection rate of the general image processing was 58.2%, a new algorithm proposed by this study was 74.5%. And, an error rate enforcing vehicles that do not violate was 0.0%. Also, we can use the existing facilities, such as street light because of compact and lightweight systems which are integrated camera with controller. Therefore, we concluded that it is possible to enforce the interfering Cars using vehicle enforcement systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.32-39
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• 2011

Even if the traffic light is green, if vehicles enter a jammed intersection, they are violation of the law. The police is enforcing law as a part of a nation wide campaign. Because, using the camcorder, the police can not do enforcement the offending vehicle, there are other techniques. Our research group proposed automated photographic equipment enable to enforce moving violation in intersection. Using new license plate recognition technology and backtracking technology to trace the offending vehicle, once the system detects a violator, it records 8 wide pictures and 1picture from the front vehicle, showing it enter and proceed through the intersection. Field experimental results obtained in the system, the following conclusions. The three measures of effectiveness investigated were recognition rate 83.5, mis-match recognition rate 1.5%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.31-37
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• 2024

In this study, we propose a machine vision-based pedestrian signal detection algorithm that operates efficiently even in computing resource-constrained environments. This algorithm demonstrates high efficiency within limited resources and is designed to minimize the impact of ambient lighting by sequentially applying HSV color space-based image processing, binarization, morphological operations, labeling, and other steps to address issues such as light glare. Particularly, this algorithm is structured in a relatively simple form to ensure smooth operation within embedded system environments, considering the limitations of computing resources. Consequently, it possesses a structure that operates reliably even in environments with low computing resources. Moreover, the proposed pedestrian signal system not only includes pedestrian signal detection capabilities but also incorporates IoT functionality, allowing wireless integration with a web server. This integration enables users to conveniently monitor and control the status of the signal system through the web server. Additionally, successful implementation has been achieved for effectively controlling 50W LED pedestrian signals. This proposed system aims to provide a rapid and efficient pedestrian signal detection and control system within resource-constrained environments, contemplating its potential applicability in real-world road scenarios. Anticipated contributions include fostering the establishment of safer and more intelligent traffic systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.182-199
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• 2023

Since 2021, government departments have been promoting Automated Driving Technology Development and Innovation Project as national research and development(R&D) project. The automated vehicles and service technologies developed as part of these projects are planned to be subsequently provided to the public at the selected Living Lab City. Therefore, it is important to determine a spatial area and operation section that enables safe and stable automated driving, depending on the purpose and characteristics of the target service. In this study, the static Operational Design Domain(ODD) elements for Level 4 automated driving services were reclassified by reviewing previously published papers and related literature surveys and investigating field data. Spatial analysis techniques were used to consider the reclassified ODD elements for level 4 in the real area of level 3 automated driving services because it is important to reflect the spatial factors affecting safety related to real automated driving technologies and services. Consequently, a total of six driving mode changes(disengagement) were derived through spatial information analysis techniques, and the factors affecting the safety of automated driving were crosswalk, traffic light, intersection, bicycle road, pocket lane, caution sign, and median strip. This spatial factor analysis method is expected to be useful for determining special areas for the automated driving service.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.43-61
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• 2011

In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.