• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.12 no.6
• /
• pp.1-9
• /
• 2013

The studies on the shortest path algorithms based on Dijkstra algorithm has been done continuously to decrease the time for searching. $A^*$ algorithm is the most represented one. Although fast searching speed is the major point of $A^*$ algorithm, there are high rates of failing in search of the shortest path, because of complex and irregular networks. The failure of the search means that it either did not find the target node, or found the shortest path, witch is not true. This study proposed $A^*$ algorithm applying method that can reduce searching failure rates, preferentially organizing the relations between the starting node and the targeting node, and appling it in reverse according to the organized path. This proposed method may not build exactly the shortest path, but the entire failure in search of th path would not occur. Following the developed algorithm tested in a real complex networks, it revealed that this algorithm increases the amount of time than the usual $A^*$ algorithm, but the accuracy rates of the shortest paths built is very high.

• Journal of Korean Society of Transportation
• /
• v.19 no.6
• /
• pp.183-194
• /
• 2001

The centralized traffic information system is to gather and analyze real-time traffic information, to receive traffic information request from user, and to send user processed traffic information such as a path finding. Position information, result of destination search, and other information. In the centralized traffic information system, a server received path-finding requests from many clients and must process clients requests in time. The algorithm of multiple path-finding is needed for a server to process clients request, effectively in time. For this reason, this paper presents a heuristic algorithm that decreases time to compute path-finding requests. This heuristic algorithm uses results of the neighbor nodes shortest path-finding that are computed periodically. Path-finding results of this multiple path finding algorithm to use results of neighbor nodes shortest path-finding are the same as a real optimal path in many cases, and are a little different from results of a real optimal path in non-optimal path. This algorithm is efficiently applied to the general topology and the hierarchical topology such as traffic network. The computation time of a path-finding request that uses results of a neighbor nodes shortest path-finding is 50 times faster than other algorithms such as one-to-one label-setting and label-correcting algorithms. Especially in non-optimal path, the average error rate is under 0.1 percent.

• 한국정보교육학회:학술대회논문집
• /
• 2010.08a
• /
• pp.227-233
• /
• 2010

Graph is applied to GIS, Network, AI and so on. We use graph concept in our daily life unconsciously. So this paper describe how graph concept is used when robot searches shortest path between two distinct vertices. It is performed in real world. For this, it consists of three step; maze traverse, graph generation, and shortest path search. Maze traverse steps is that robot navigates maze. It is most difficult step. Graph generation step is to represent structural information into graph. Shortest path search step is to that robot move between two vertices. It is not implemented yet. So we introduce process in design level.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.12 no.5
• /
• pp.36-45
• /
• 2013

This paper developed a new shortest path searching algorithm based on Dijkstra's algorithm and $A^*$ algorithm, so it guarantees to find a shortest path in efficient manner. In this developed algorithm, minimum expected weights implies the value that straight line distance from a visiting node to the target node multiplied by minimum link unit, and this value can be the lowest weights between the two nodes. In behalf of the minimum expected weights, at each traversal step, developed algorithm in this paper is able to decide visiting a new node or retreating to the previously visited node, and results are guaranteed. Newly developed algorithm was tested in a real traffic network and found that the searching time of the algorithm was not as fast as other $A^*$ algorithms, however, it perfectly found a minimum path in any case. Therefore, this developed algorithm will be effective for the domain of searching in a large network such as RGV which operates in wide area.

• Journal of KIISE
• /
• v.44 no.7
• /
• pp.710-718
• /
• 2017

As graphs are becoming increasingly large, the costs for storing and managing data are increasing continuously. Shortest path discovery over a large graph requires long running time due to frequent disk I/Os and high complexity of the graph data. Recently, graph summarization techniques have been studied, which reduce the size of graph data and disk I/Os by representing highly dense subgraphs as a single super-node. Decompressing should be minimized for efficient shortest path discovery over the summarized graph. In this paper, we analyze the decompression performance of a summarized graph and propose an approximate technique that discovers the shortest path quickly with a minimum error ratio. We also propose an exact technique that efficiently discovered the shortest path by exploiting an index built on paths containing super-nodes. In our experiments, we showed that the proposed technique based on the summarized graph can reduce the running time by up to 70% compared with the existing techniques performed on the original graph.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.5
• /
• pp.988-995
• /
• 2009

One of the most popular services of Telematics is a shortest path finding from a starting point to a destination. In this paper, a dynamic shortest path finding system with forecasting result of traffic flow in the future was developed and various experiments to verify the performance of our system using real-time traffic information has been conducted. Traffic forecasting has been done by a prediction system using Bayesian network. It searched a dynamic shortest path, a static shortest path and an accumulated shortest path for the same starting point and destination and calculated their travel time to compare with one of its real shortest path. From the experiment, over 75%, the travel time of dynamic shortest paths is the closest to one of their real shortest paths than one of static shortest paths and accumulated shortest paths. Therefore, it is proved that finding a dynamic shortest path by applying traffic flows in the future for intermediated intersections can give more accurate traffic information and improve the quality of services of Telematics than finding a static shortest path applying by traffic flows of the starting time for intermediated intersections.

• Journal of Korean Society of Transportation
• /
• v.18 no.6
• /
• pp.111-124
• /
• 2000

본 연구에서 다루고자 하는 문제는 서비스시간 제약을 갖는 도시부 복합교통망에서의 기종점을 잇는 합리적인 최단경로를 탐색하고자 하는 것이다. 서비스시간 제약은 도시부 복합교통망에서의 현실성을 보다 더 사실적으로 표현하지만 기존의 알고리즘들은 이를 고려하지 않고 있다. 서비스시간 제약은 환승역에서 여행자가 환승차량을 이용해서 다른 지점으로 여행할 수 있는 출발시간이 미리 계획된 차량운행시간들에 의해 제한되어지는 것이다. 환승역에 도착한 여행자는 환승차량의 정해진 운행시간에서만 환승차량을 이용해서 다른 지점으로 여행할 수 있다. 따라서 서비스시간 제약이 고려되어지는 경우 총소요시간에는 여행시간과 환승대기시간이 포함되어지고, 환승대기시간은 여행자가 환승역에 도착한 시간과 환승차량의 출발이 허용되어지는 시간에 의존해서 변한다. 본 논문에서는 이러한 문제를 해결할 수 있는 링크기반의 최단경로탐색 알고리즘을 개발하였다. Dijkstra 알고리즘과 같은 전통적인 탐색법에서는 각 노드까지의 최단도착시간을 계산하여 각 노드에 표지로 설정하지만 제안된 알고리즘에서는 각 링크가지의 최단도착시간과 각 링크에서의 가장 빠른 출발시간을 계산하여 각 링크의 표지로 설정한다. 제안된 알고리즘의 자세한 탐색과정이 간단한 복합교통망에 대하여 예시되어진다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.2
• /
• pp.454-461
• /
• 2005

Car navigation system which is killer application fuses map management techniques into CPS techniques. Even if the existing navigation systems are designed for the shortest path, they are not able to cope efficiently with the change of the traffic flow and the bottleneck point of road. Therefore, it is necessary to find out shortest path algorithm based on time instead of distance which takes traffic information into consideration. In this paper, we propose a optimal path search algorithm based on the traffic information. More precisely. we introduce the system architecture for finding out optimal paths, and the limitations of the existing shortest path search algorithm are also analyzed. And then, we propose a new algorithm for finding out optimal path to make good use of the orientation of the collected traffic information.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.04a
• /
• pp.1354-1356
• /
• 2012

최근 ITS(Intelligent Transportation Systems)의 개발과 함께 차량용 내비게이션의 사용이 급증하면서 경로탐색의 중요성이 더욱 가속화되고 있다. 현재 차량용 내비게이션은 멀티미디어 및 정보통신 기술의 결합과 함께 다양한 기능 및 정보를 사용자에게 제공하고 있으며 이러한 기능과 정보를 사용해서 목적지점까지의 최단경로를 탐색하는 것이 내비게이션 시스템의 핵심기능이다. 이러한 경로탐색 알고리즘은 교통시스템, 통신 네트워크, 운송 시스템은 물론 이동 로봇의 경로 설정 등 다양한 분야에 사용되고 있다. 개미 집단 최적화(Ant Colony Optimization, ACO) 알고리즘은 메타 휴리스틱 탐색 방법으로 그리디 탐색(Greedy Search)뿐만 아니라 긍정적 반응의 탐색을 사용한 모집단에 근거한 접근법으로 순환 판매원 문제(Traveling Salesman Problem, TSP)를 풀기 위해 처음으로 제안되었다. 본 논문에서는 개미 집단 최적화(ACO) 알고리즘이 기존의 경로 탐색 알고리즘으로 알려진 Dijkstra 보다 최단경로 탐색에 있어서 더 적합한 알고리즘이라는 것을 설명하고자 한다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.10a
• /
• pp.613-615
• /
• 2001

최단경로 탐색에 있어서 출발지와 목적지 사이의 최단경로를 계산하는데 있어서 Label-Setting 알고리즘이 Label-Correcting 알고리즘보다 낫다고 믿어왔다. 하지만 특수한 경우에는 Label-Correcting 알고리즘이 GIS기반의 도로에서 더 좋은 결과를 보인다고 Benjamin의 논문에서 밝혔다[1]. 본 논문에서는 Label-Correcting 알고리즘인 Pallottino의 Graph Growth 알고리즘을 수정하여 One-to-One 최단경로탐색에 적합한 알고리즘을 제안한다.