• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.559-564
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• 2006

The purpose of this study is to minimize negative effect of VMS travel time information service by sensitivity analysis, which forecasts the change in link traffic volume. As a result, strategies for providing travel information that can change driving patterns for minimizing travel time were found. The framework for analysis is recently expanded with the application of game theory. According to the experiment, the algorithm generated for travel time information service reduces total travel time and yields travel patterns that is very close to the system optimization. Also, this study found that the route the travel time service information is provided about could play the important role.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.41-55
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• 1995

In this paper, an optimal idenfication method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together wlth optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzzy-neural networks(FNNs) are tuned automatically using improved modified complex method and modified learning algorithm. For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activateti sluge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.175-184
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• 2008

Uncertainties are unavoidable in engineering applications. In this paper we propose an alpha reliable multi-variable network design problem under demand uncertainty. In order to decide the optimal capacity enhancement, three performance measures based on 3E(Efficiency, Equity, and Environmental) are considered. The objective is to minimize the total budget required to satisfy alpha reliability constraint of total travel time, equity ratio, and total emission, while considering the route choice behavior of network users. The problem is formulated as the chance-constrained model for application of alpha confidence level and solved as a lexicographic optimization problem to consider the multi-variable. A simulation-based genetic algorithm procedure is developed to solve this complex network design problem(NDP). A simple numerical example ispresented to illustrate the features of the proposed NDP model.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.4
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• pp.117-122
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• 2021

In recent years, human damage and loss of money due to various disasters such as typhoons, earthquakes, forest fires, landslides, and wars are steadily occurring, and a lot of manpower and funds are required to prevent and recover them. In this paper, we designed and developed a disaster drone system based on artificial intelligence in order to monitor these various disaster situations in advance and to quickly recognize and respond to disaster occurrence. In this study, multiple disaster drones are used in areas where it is difficult for humans to monitor, and each drone performs an efficient search with an optimal path by applying a deep learning-based optimal path algorithm. In addition, in order to solve the problem of insufficient battery capacity, which is a fundamental problem of drones, the optimal route of each drone is determined using Ant Colony Optimization (ACO) technology. In order to implement the proposed system, it was applied to a forest fire situation among various disaster situations, and a forest fire map was created based on the transmitted data, and a forest fire map was visually shown to the fire fighters dispatched by a drone equipped with a beam projector. In the proposed system, multiple drones can detect a disaster situation in a short time by simultaneously performing optimal path search and object recognition. Based on this research, it can be used to build disaster drone infrastructure, search for victims (sea, mountain, jungle), self-extinguishing fire using drones, and security drones.

• Journal of Korean Society of Disaster and Security
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• v.13 no.2
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• pp.53-63
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• 2020

In this study, the two-dimensional flow analysis model Hydro_AS-2D model was used to simulate the situation of flooding in Seongsangu and Uichang-gu in Changwon in the event of rising sea levels and extreme flooding, and the results were expressed on three-dimensional topography and the optimal evacuation path was derived using BIM technology. Climate change significantly affects two factors in terms of flood damage: rising sea levels and increasing extreme rainfall ideas. The rise in sea level itself can not only have the effect of flooding coastal areas and causing flooding, but it also raises the base flood level of the stream, causing the rise of the flood level throughout the stream. In this study, the rise of sea level by climate change, the rise of sea level by storm tidal wave by typhoon, and the extreme rainfall by typhoon were set as simulated conditions. The three-dimensional spatial information of the entire basin was constructed using the information of topographical space in Changwon and the information of the river crossing in the basic plan for river refurbishment. Using BIM technology, the target area was constructed as a three-dimensional urban information model that had information such as the building's height and location of the shelter on top of the three-dimensional topographical information, and the results of the numerical model were expressed on this model and used for analysis for evacuation planning. In the event of flooding, the escape route is determined by an algorithm that sets the path to the shelter according to changes in the inundation range over time, and the set path is expressed on intuitive three-dimensional spatial information and provided to the user.

• Journal of Korean Society of Transportation
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• v.30 no.1
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• pp.125-133
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• 2012

This paper deals with the passenger transfer trips counted from smart bus-card data from Seoul transit network to understand the current operational condition of the system. Objective of this study is to relocate the location of the transit transfer centers. It delivers a bi-level programing model. The upper model is a linear 0-1 binary integer program having the objective of total travel cost minimization constrained by the number of transfer centers and the total construction budget. The lower model is an user equilibrium assignment model determining the passengers' route choice according to the transfer center locations. The proposed bi-level programming model was tested in an example network. The result showed that the proposed was able to find the optimal solution.

• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.25-33
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• 2007

The local governments of major cities in Korea are giving focus on public transportation to reduce congestion and improve accessibility in city areas. In this regards, the proper measurement of accessibility is now a key policy requirement for reorganizing the public transport network. Public transport routing problems, however, are considered to be highly complicated since a multi-mode travel generates different combinations of accessibility. While most of the previous research efforts on measuring transport accessibility are found at zone-levels, an alternative approach at a finer scale such as bus links and stops is presented in this study. We proposes a method to compute the optimal route choice of origin-destination pairs and measure the accessibility of the chosen modes combination based on topological configuration. The genetic algorithm is used for the computation of the journey paths, whereas the space syntax theory is used for the accessibility. This study used node-link data in GIS instead of axial lines which are manually drawn in space syntax. The resulting accessibilities of bus stops are calibrated by O-D survey data and the proposed process is tested on a CBD of Seoul.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.430-448
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• 2001

In navigation system, a primary task is to compute the minimum cost route from the current location to the destination. One of major problems for navigation systems is that a significant amount of computation time is required when the digital road map is large. Since navigation systems are real time systems, it is critical that the path be computed while satisfying a time constraint. In this paper, we have developed a HiTi(Hierarchical MulTi) graph model for hierarchically structuring large digital road maps to speedup the minimum cost path computation. We propose a new shortest path algorithm named SPAH, which utilizes HiTi graph model of a digital road map for its computation. We prove that the shortest path computed by SPAH is the optimal. Our performance analysis of SPAH also showed that it significantly reduces the computation time over exiting methods. We present an in-depth experimental analysis of HiTi graph method by comparing it with other similar works.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.269-276
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• 2021

Recently, smart sensors are used in various environments, and the implementation of ad-hoc sensor networks (ASNs) is a hot research topic. Unfortunately, traditional sensor network routing algorithms focus on specific control issues, and they can't be directly applied to the ASN operation. In this paper, we propose a new routing protocol by using the Q-learning technology, Main challenge of proposed approach is to extend the life of ASNs through efficient energy allocation while obtaining the balanced system performance. The proposed method enhances the Q-learning effect by considering various environmental factors. When a transmission fails, node penalty is accumulated to increase the successful communication probability. Especially, each node stores the Q value of the adjacent node in its own Q table. Every time a data transfer is executed, the Q values are updated and accumulated to learn to select the optimal routing route. Simulation results confirm that the proposed method can choose an energy-efficient routing path, and gets an excellent network performance compared with the existing ASN routing protocols.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.44-49
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• 2020

Recently, one of the major issues of shipbuilding and marine is the reduction of air and marine pollution emission to ships. In response, the International Maritime Organization (IMO) has concluded an international convention (MARPOL) to prevent pollution from ships. A Annex Six of The Convention restricts and regulates air and marine pollution of ship from exhausting gases. To this end, it is required to apply EEDI (Energy Efficiency Design Indicators) to the construction of new ships, and to minimize the emission of environmental pollutants by recommending the application of EEOI (Energy Efficiency Operation Indicators) to operational ships. Therefore, in this study, we propose to calculate the grade of operating efficiency (EG) of ships based on actual operational data for transport ships and to provide energy-efficient optimal path search information through analysis of marine environment data.