• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.8
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• pp.74-84
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• 2010

In this paper, we propose a preference-based shortest path algorithm which is combined with Ant Colony Optimization (ACO) and A* heuristic algorithm. In recent years, with the development of ITS (Intelligent Transportation Systems), there has been a resurgence of interest in a shortest path search algorithm for use in car navigation systems. Most of the shortest path search algorithms such as Dijkstra and A* aim at finding the distance or time shortest paths. However, the shortest path is not always an optimum path for the drivers who prefer choosing a less short, but more reliable or flexible path. For this reason, we propose a preference-based shortest path search algorithm which uses the properties of the links of the map. The preferences of the links are specified by the user of the car navigation system. The proposed algorithm was implemented in C and experiments were performed upon the map that includes 64 nodes with 118 links. The experimental results show that the proposed algorithm is suitable to find preference-based shortest paths as well as distance shortest paths.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3100-3116
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• 2016

With the rapid development of urban traffic system and fast increasing of vehicle numbers, the traditional centralized ways to generate the source-destination shortest path in terms of travel time(the optimal path) encounter several problems, such as high server pressure, low query efficiency, roads state without in-time updating. With the widespread use of smart cameras in the urban traffic and surveillance system, this paper maps the optimal path finding problem in the dynamic road network to the shortest routing problem in the smart camera networks. The proposed distributed optimal path generation algorithm employs the delay routing and caching mechanism. Real-time route update is also presented to adapt to the dynamic road network. The test result shows that this algorithm has advantages in both query time and query packet numbers.

• 전기의세계
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• v.30 no.5
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• pp.297-305
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• 1981

The problem of finding shortest paths between every pair of points in a network is solved employing and optimal network decomposition in which the network is decomposed into a number of subnetworks minimizing the number of cut-set between them while each subnetwork is constrained by a size limit. Shortest path computations are performed on individual subnetworks, and the solutions are recomposed to obtain the solution of the original network. The method when applied to large scale networks significantly reduces core requirement and computation time. This is demonstrated by developing a computer program based on the method and applying it to 30-vertex, 160-vertex, and 273-vertex networks.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.153-158
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• 1995

Finding shortest paths in networks is the fundamental problem in network theory and has numerous in Operations Research and related fields. The purpose of this study is to present a algorithm for solving the length of the shortest paths from a fixed node in a general network in which the arc distance can be arbitrary value. This algorithm has a worst computational bound of $n^3/4$ additions and $n^3/4$ comparisons, which is lower the worst computational bounds of other available algorithms.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.102-112
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• 2015

In this paper we propose an application that can help you to find a school near you easily using smart phone GPS capabilities and Google map mounted on Android system. We use a shortest path algorithm to search near destinations. You can take advantage of the developed application when finding public places or destinations. Schools have usually been used as places for disaster evacuation, entrance examination, and qualifying examination. Even though you are not the young who use the smart phone easily, anyone can find a destination school easily with the proposed application of this paper.

• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.1-7
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• 2000

A quickest path in a network is a path that takes the shortest time to send the amount of data from the source node to the sink node. Martin and Santos presented a theorem on the quickest path by which a quickest path for the amount of data is determined. However, we find a counterexample to Martins and Santos' theorem. In this paper, we present the corrected theorem and give a revised algorithm for finding quickest paths.

• 한국ITS학회:학술대회논문집
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• 2003.11a
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• pp.29-34
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• 2003

• Journal of Korean Society of Transportation
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• v.13 no.3
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• pp.35-52
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• 1995

This paper reviews both network representation schemes of transportation network and algorithms for findings the shortest path between two points in the network. Two types of network representation schmes for considering U-Turn have been proposed along with some modifications of the vine-based Dijkstra shoretest path algorithm. The traditional Sioux-Fall network has been chosen and modified with the introduction of left-turn prohibitions and U-Turns for the purpose of evaluating the peroformance of two modified algorithms and network representation schemes(NA1 an d NA2 ). This type of modification in both network representation scheme (including network data) and algorithms is not only supposed to be needed for route guidance but supposed to contribute to finding more realistic path and estimating more accurate traffic voulume thorughout the entire network.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.425-430
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• 2005

Many problems can be formulated in terms nf graphs and thus solved by graph-theoretic algorithms. This paper is concerned with finding paths between nodes over the distributed and overlapped graphs. The proposed method allows multiple agents to cooperate to find paths without merging the distributed graphs. For each graph there is a designated agent which is charged of providing path-finding service for hot graph and initiating the path-finding tasks of which path starts from the graph. The proposed method earlier on constructs an abstract graph so-called viewgraph for the distributed overlapped graphs and thus enables to extract the information about how to guide the path finding over the graphs. The viewgraph is shared by all agents which determine how to coordinate other agents for the purpose of finding paths. Each agent maintains the shortest path information among the nodes which are placed in different overlapped subgraphs of her graph. Once an agent is asked to get a path from a node on her graph to another node on another's graph, she directs other agents to provide the necessary information for finding paths.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.38-45
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• 2012

Finding shortest paths on time dependent networks is an important task for scheduling and routing plan and real-time navigation system in ITS. In this research, one-to-one time dependent shortest path algorithms based on link flow speeds on urban networks are proposed. For this work, first we select three general shortest path algorithms such as Graph growth algorithm with two queues, Dijkstra's algorithm with approximate buckets and Dijkstra's algorithm with double buckets. These algorithms were developed to compute shortest distance paths from one node to all nodes in a network and have proven to be fast and efficient algorithms in real networks. These algorithms are extended to compute a time dependent shortest path from an origin node to a destination node in real urban networks. Three extended algorithms are implemented on a data set from real urban networks to test and evaluate three algorithms. A data set consists of 4 urban street networks for Anaheim, CA, Baltimore, MD, Chicago, IL, and Philadelphia, PA. Based on the computational results, among the three algorithms for TDSP, the extended Dijkstra's algorithm with double buckets is recommended to solve one-to-one time dependent shortest path for urban street networks.