• 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.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.633-640
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• 1999

Reinforcement learning performs learning through interacting with trial-and-error in dynamic environment. Therefore, in dynamic environment, reinforcement learning method like Q-learning and TD(Temporal Difference)-learning are faster in learning than the conventional stochastic learning method. However, because many of the proposed reinforcement learning algorithms are given the reinforcement value only when the learning agent has reached its goal state, most of the reinforcement algorithms converge to the optimal solution too slowly. In this paper, we present COMREL(COMpressed REinforcement Learning) algorithm for finding the shortest path fast in a maze environment, select the candidate states that can guide the shortest path in compressed maze environment, and learn only the candidate states to find the shortest path. After comparing COMREL algorithm with the already existing Q-learning and Priortized Sweeping algorithm, we could see that the learning time shortened very much.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.3B
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• pp.493-503
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• 2000

Multicast routing algorithms such PIM, CBT, BGMP use shared multicast routing trees and the location of the multicast tree has great impact on the packet delay. In this pater we propose three new center location algorithms and analyze their performance through simulation studies. these three algorithms consider as candidates for the center not only multicast group members but also a few non-members nodes. To select these non-member nodes, we first find all the shortest paths among every couple of members and consider either nodes which are most frequently visited during the process of finding shortest paths or nodes which lie at the center of a shortest path and are most frequently visited during the same process. There the proposed algorithms are able to find the better center than not only algorithms which consider only member nodes but also other algorithms which consider selected non-member nodes in addition ot member nodes. The proposed algorithms either incur too much overhead nor depend upon unicasting algorithms.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.843-845
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• 2003

Shortest Path Problems are among the most studied network flow optimization problems, with interesting applications in various fields. One such field is the route determination service, where various kinds of shortest path problems need to be solved in location-based service. Our research aim is to propose a route technique in real-time locationbased service (LBS) environments according to user’s route preferences such as shortest, fastest, easiest and so on. Turn costs modeling and computation are important procedures in route planning. There are major two kinds of cost parameters in route planning. One is static cost parameter which can be pre-computed such as distance and number of traffic-lane. The other is dynamic cost parameter which can be computed in run-time such as number of turns and risk of congestion. In this paper, we propose a new cost modeling method for turn costs which are traditionally attached to edges in a graph. Our proposed route determination technique also has an advantage that can provide service interoperability by implementing XML web service for the OpenLS route determination service specification. In addition to, describing the details of our shortest path algorithms, we present a location-based service system by using proposed routing algorithms.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.85-96
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• 2004

Given a Path represented by a sequence of link numbers in a transportation network, the reasonable path is defined as a path that any link is appeared multiple times in it. Application of the link labelmethod(LLM) to the shortest path algorithms(SPA) enables to model the reasonable path choice behavior in urban networks. This study aims at expanding the LLM to a Kth shortest path algorithms(KPSA), which adopts the node label setting method. The small-scaled network test demonstrated that the proposed algorithm works correctly and the revised Sioux fall network test showed that the path choice behaviors are reasonably reflected. In the large-scaled network based on the South Korea peninsula, drivers' route diversion perceptions are included as cost terms in total cost. The algorithm may be applied as an alternative route information tools for the deployment of ATIS.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.1
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• pp.1-9
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• 1998

In the circuit model that outputs are latched and input vectors are successively applied at inputs, the gate resizing approach to reduce the delay of the critical pathe may not improve the performance. Since the clock period is etermined by delays of both long and short paths in combinational circuits, the performance (clock period) can be optimized by decreasing the delay of the longest path, or increasing the delay of the shortest path. In order to achieve the desired clock period of a circuit, gates lying in sensitizable long and short paths can be selected for resizing. However, the gate selection in path sensitization approach is a difficult problem due to the fact that resizing a gate in shortest path may change the longest sensitizable path and viceversa. For feasible settings of the clock period, new algorithms and corresponding gate selection methods for resizing are proposed in this paper. Our new gate selection methods prevent the delay of the longest path from increasing while resizing a gate in the shortest path and prevent the delay of the shortest path from decreasing while resizing a gate in the longest sensitizable path. As a result, each resizing step is guaranteed not to increase the clock period. Our algorithmsare teted on ISCAS85 benchmark circuits and experimental results show that the clock period can beoptimized efficiently with out gate selection methods.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.129-137
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• 2010

Currently used shortest path search algorithms involve graphs with vertices and weighted edges between each vertex. However, when finding the shortest path with a randomly shaped obstacle(an island, for instance) positioned in between the starting point and the destination, using such algorithms involves high memory inefficiency and is significantly time consuming - all positions in the map should be considered as vertices and every line connecting any of the two adjacent vertices should be considered an edge. Therefore, we propose a new method for finding the shortest path in such conditions without using weighted graphs. This algorithm will allow finding the shortest obstacle bypass given only the positions of the obstacle boundary, the starting point and the destination. When the row and column size of the minimum boundary rectangle to include an obstacle is m and n, respectively, the proposed algorithm has the maximum time complexity, O(mn). This performance shows the proposed algorithm is very efficient comparing with the currently used algorithms.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.607-620
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• 2002

In this paper, we consider the updating problems to reconstruct the biconnected-components and to reconstruct the weighted shortest path in response to the topology change of the network. We propose two distributed algorithms. The first algorithm solves the updating problem that reconstructs the biconnected-components after the several processors and links are added and deleted. Its bit complexity is O((n'+a+d)log n'), its message complexity is O(n'+a+d), the ideal time complexity is O(n'), and the space complexity is O(e long n+e' log n'). The second algorithm solves the updating problem that reconstructs the weighted shortest path. Its message complexity and ideal-time complexity are $O(u^2+a+n')$ respectively.

• Journal of Korean Society of Transportation
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• v.19 no.4
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• pp.85-96
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• 2001

This paper discusses the problem of finding the Origin-Destination(O-D) shortest path in urban road networks that have variable special qualifies such as time windows for passing as well as geometrical special qualities such as U-turn and left-turn prohibition. The focus of this paper is motivated by the problem of finding minimum travel time paths for an advanced traveler information system (ATIS) in the context of intelligent transportation system(ITS) application. The transportation network with variable and geometrical special qualities is a more realistic representation of the urban road network in the real word. But, the traditional and existing shortest path algorithms can not search practical shortest path that variable special quality is reflected. This paper presents a shortest path algorithm which can search reasonable shortest path information for the urban ATIS application within a real time. The algorithm is based on genetic algorithm(GA). The high performance of the proposed algorithm is demonstrated by computer simulations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.265-270
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• 2010

Reinforcement learning performs learning through interacting with trial-and-error in dynamic environment. Therefore, in dynamic environment, reinforcement learning method like TD-learning and TD(${\lambda}$)-learning are faster in learning than the conventional stochastic learning method. However, because many of the proposed reinforcement learning algorithms are given the reinforcement value only when the learning agent has reached its goal state, most of the reinforcement algorithms converge to the optimal solution too slowly. In this paper, we present GDRLS algorithm for finding the shortest path faster in a maze environment. GDRLS is select the candidate states that can guide the shortest path in maze environment, and learn only the candidate states to find the shortest path. Through experiments, we can see that GDRLS can search the shortest path faster than TD-learning and TD(${\lambda}$)-learning in maze environment.