• Journal of Korea Multimedia Society
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• v.5 no.6
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• pp.745-752
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• 2002

A* algorithm is widely used in optimal car route search which is a kind of informed search, since the locations of starting and ending points are known a priori. Unidirectional A* algorithm requires considerable search time but guarantees a optimal path, bidirectional A* algorithm does not guarantee a optimal path and takes even longer search time than unidirectional search to guarantee a optimal path. In this paper, a new bidirectional A* algorithm which requites less search time and guarantees a optimal path is proposed. To evaluate the efficiency of the proposed algorithm, several experiments are conducted in real road map and the results show that the algorithm is very effective in terms of finding a optimal path and search time.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.99-110
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• 2005

Total travel cost of route which connects origin with destination (O-D) is consist of the total sum of link travel cost and route perception cost. If the link perception cost is different according to the origin and destination, optimal route search has limitation to reflect the actual condition by route enumeration problem. The purpose of this study is to propose optimal route searching formulation and algorithm which is enable to reflect different link perception cost by each route, not only avoid the enumeration problem between origin and destination. This method defines minimum unit of route as a link and finally compares routes using link unit costs. The proposed method considers the perception travel cost at both origin and destination in optimal route searching process, while conventional models refect the perception cost only at origin. However this two-way searching algorithm is still not able to guarantee optimum solution. To overcome this problem, this study proposed an orign based optimal route searching method which was developed based on destination based optimal perception route tree. This study investigates whether proposed numerical formulas and algorithms are able to reflect route perception behavior reflected the feature of origin and destination in a real traffic network by the example research including the diversity of route information for the surrounding area and the perception cost for the road hierarchy.

• Journal of The Korean Association of Information Education
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• v.2 no.2
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• pp.252-259
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• 1998

Vehicle navigation systems employ a certain route planning algorithm that provides the shortest path between the starting point and the destination point. The performance of a given route planning algorithm is measured through the degree of optimal route selection and the time cost to complete searching an optimal path. In this paper, various route planning algorithms are evaluated through computer simulation based on a real digital map database. Among those algorithms evaluated in this paper, the Modified Bi-directional A${\ast}$ algorithm is found to be the best algorithm for use in vehicle navigation systems.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.51-56
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• 2004

인터넷 통신과 무선 이동 통신의 발달은 최적 경로 탐색이 인터넷 웹 페이지를 통하여 서비스될 수 있도록 하였으며, CNS(Car Navigation System)를 이용한 서비스도 가능하도록 하였다. 나아가 개인용 휴대통신 장비인 PDA나 핸드폰을 이용하여서도 그 서비스가 제공되고 있다. 기존의 양방향 A＊ 알고리즘에서는 현재 노드에서 목적지까지의 예상 비용을 직선 거리를 사용하였지만 본 논문에서는 현재 노드까지 지나온 경로에 대한 정보를 이용하였다. 지금까지 지나온 경로의 평균 경로 비용을 계산하여 이용함으로써, 직선 길이가 아닌 지금까지 지나온 경로의 정보를 이용할 수 있었고, 지나온 경로의 정보와 앞으로 탐색할 경로에 대한 애매한 정보들(도로의 속도, 날씨, 주행시간대)을 퍼지를 이용하여 계산하였다. 퍼지를 이용하여 도로의 애매한 정보들을 실시간으로 활용할 수 있는 알고리즘을 제안하고 제안한 알고리즘의 타당성을 실험을 통하여 검증하였다.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.75-82
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• 2013

This paper proposes a $O(n^2)$ polynomial time algorithm to obtain optimal solution for Traveling Salesman problem that is a NP-complete because polynomial time algorithm has been not known yet. The biggest problem in a large-scale Traveling Salesman problem is the fact that the amount of data to be processed is $n{\times}n$, and thus as n increases, the data increases by multifold. Therefore, this paper proposes a methodology by which the data amount is first reduced to approximately n/2. Then, it seeks a bi-directional route at a random point. The proposed algorithm has proved to be successful in obtaining the optimal solutions with $O(n^2)$ time complexity when applied to TSP-1 with 26 European cities and TSP-2 with 46 cities of the USA. It could therefore be applied as a generalized algorithm for TSP.