• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.127-135
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• 2008

Generally, optimum shortest path algorithms adopt single attribute objective among several attributes such as travel time, travel cost, travel fare and travel distance. On the other hand, multi-objective shortest path algorithms find the shortest paths in consideration with multi-objectives. Up to recently, the most of all researches about multi-objective shortest paths are proceeded only in single transportation mode networks. Although, there are some papers about multi-objective shortest paths with multi-modal transportation networks, they did not consider transfer problems in the optimal solution level. In particular, dynamic programming method was not dealt in multi-objective shortest path problems in multi-modal transportation networks. In this study, we propose a multi-objective shortest path algorithm including dynamic programming in order to find optimal solution in multi-modal transportation networks. That algorithm is based on two-objective node-based label correcting algorithm proposed by Skriver and Andersen in 2000 and transfer can be reflected without network expansion in this paper. In addition, we use non-dominated paths and tree sets as labels in order to improve effectiveness of searching non-dominated paths. We also classifies path finding attributes into transfer and link travel attribute in limited transit networks. Lastly, the calculation process of proposed algorithm is checked by computer programming in a small-scaled multi-modal transportation network.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.92-99
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• 2024

To minimize the cumulative radiation dose, various path-finding approaches for single agent have been proposed. However, for emergence situations such as nuclear power plant accident, these methods cannot be effectively utilized for evacuating a large number of workers because no multi-agent method is valid to conduct the mission. In this study, a novel algorithm for solving the multi-agent path-finding problem is proposed using the conflict-based search approach and the objective function redefined in terms of the cumulative radiation dose. The proposed method can find multi paths that all agents arrive at the destinations with reducing the overall radiation dose. To verify the proposed method, three problems were defined. In the single-agent problem, the objective function proposed in this study reduces the cumulative dose by 82% compared with that of the shortest distance algorithm in experiment environment of this study. It was also verified in the two multi-agent problems that multi paths with minimized the overall radiation dose, in which all agents can reach the destination without collision, can be found. The method proposed in this study will contribute to establishing evacuation plans for improving the safety of workers in radiation-related facilities.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.1-14
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• 2008

Mass transit information should contribute many benefits to users. Especially transportation information technology is developing highly with information technology in Korea recently. Hereafter it is expected to provide customized transportation information to users individually with the advent of ubiquitous age in earnest. This public transportation information service can be realized by path finding algorithms in public transportation networks including travel and transfer attributes. In this research, multi objectives such as travel time, transfer time, and number of transfer and so on are constructed with the primary facts influencing users. Moreover, the method reducing user's path finding alternatives arbitrarily is proposed by selecting the best alternative which provides maximum utility to users among non dominated paths. Therefore, the ultimate goal of this study proposes a multi objective shortest paths finding algorithm which can take into account multiple user classes in a transit network with multiple travel modes. The proposed algorithm is demonstrated based on the two case studies - a small toy network and the large-scaled Seoul Metropolitan subway network.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.617-626
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• 2014

Lately, losses and damage from natural disasters have been increasing. Researchers across various fields in Korea are trying to come up with a response plan, but research for evacuation plans is still far from satisfactory. Hence this paper proposes a model that could find an optimized evacuation route for when disasters occur over wide areas. Development of the model used methods including the Dijkstra shortest path algorithm, feasible path method, genetic algorithm, and pareto efficiency. Computations used parallel computing (SPMD) for high performance. In addition, the developed model is applied to a virtual network to check the validity. Finally the adaptability of the model is verified on a real network by computating for Gumi 1stNational Industrial Complex. Computation results proved that this model is valid and applicable by comparison of the fitness values for before optimization and after optimization. This research can contribute to routing for responder vehicles as well as planning for evacuation by objective when disasters occur.

• Proceedings of the KOR-KST Conference
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• 2000.02a
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• pp.25-47
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• 2000

In route guidance systems fastest-path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this study is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e. Pareto optimal) paths for real-time vehicle routing which reflect the drivers' preferences and route choice behaviors. We propose two pruning algorithms that reduce the search area based on a context-dependent linear utility function and thus reduce the computation time. The basic notion of the proposed approach is that ⅰ) enumerating all nondominated paths is computationally too expensive, ⅱ) obtaining a stable mathematical representation of the drivers' utility function is theoretically difficult and impractical, and ⅲ) obtaining optimal path given a nonlinear utility function is a NP-hard problem. Consequently, a heuristic two-stage strategy which identifies multiple routes and then select the near-optimal path may be effective and practical. As the first stage, we utilize the relaxation based pruning technique based on an entropy model to recognize and discard most of the nondominated paths that do not reflect the drivers' preference and/or the context-dependency of the preference. In addition, to make sure that paths identified are dissimilar in terms of links used, the number of shared links between routes is limited. We test the proposed algorithms in a large real-life traffic network and show that the algorithms reduce CPU time significantly compared with conventional multi-criteria shortest path algorithms while the attributes of the routes identified reflect drivers' preferences and generic route choice behaviors well.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.421-430
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• 2000

This paper presents a new approach which uses $A^*$ search and genetic algorithms for solving large scale multi-objective shortest path problem. The focus of this paper is motivated by the problem of finding Pareto optimal paths for an advanced traveler information system(ATIS) in the context of intelligent transportation system(ITS) application. The individual description, the decoding rule, the selection strategy and the operations of crossover and mutation are proposed for this problem. The keynote points of the algorithm are how to represent individuals and how to calculate the fitness of each individual. The high performance of the proposed algorithm is demonstrated by computer simulations.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.85-94
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• 2018

This study aims to investigate how easy pedestrians get around within/through the "Apartment Complexes (AC), " a common style of high-rise multi-family housing in Korea. Over the past six decades, the AC has been the most conventional way to provide standardized housing efficiently to address the problems of the shortage of housing and the substandard housing, due to the explosion of urban population with the rapid industrialization. The AC is a huge chunk of homeogenous multi-family housing, mostly condos with decent infrastructure, including parks, pedestrian passages, schools, ect. Both in the new town development and urban renewal programs have utilized the advantages of the AC. Since the design principals of AC tend to adopt the "protective design" to prevent cars and pedestrians coming outside from passing it, it has been criticised for dissecting the continuity of socioeconomic context in neighborhoods. The neo-traditional planning urbanists, including Jane Jacobs, emphasize that smaller blocks and grid road newtworks are the key in improving social, cultural, and economic vitality of the neighborhoods, because these design concepts allow more pedestrians and different types of people to be mixed in a neighborhood. In this study, we first adopted objective measures for pedestrian accessibility and pedestrian efficiency. These measures were used to calculate the lengths of shortest paths from residential buildings to the edges of AC. We tested the difference in shortest paths between the current pedestrian networks of AC and hypothetical grid networks on the AC, and the relative difference is considered as the pedestrian efficiency, using the network analysis function of Geographic Information Systems (GIS) and Python programming. We found from the randomly selected 30 ACs that the existing non-grid road networks in ACs are worse than the hypothesized grid networks, in terms of pedestrian efficiency. In average, pedestrians in AC with the conventional road networks have to walk than 25%, 26%, and 27% longer than the networks of $125{\times}45m$, $100{\times}45m$, and $75{\times}45m$, respectively. With the t-test analysis, we found the pedestrian efficiency of AC with the conventional network is lower than grid-networks. Many new urbanists stress, easiness of walking is one of the most import elements for community building and social bonds. With the findings from the objective measures of pedestrian accessibility and efficiency, the AC would have limitations to attract people outside into the AC itself, which would increase dis-connectivity with adjacent areas.