• Management Science and Financial Engineering
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• v.4 no.2
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• pp.1-13
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• 1998

The Shortest Path Problem in Time-dependent Networks, where the travel time of each link depends on the time interval, is not realistic since the model and its solution violate the Non-passing Property (NPP:often referred to as FIFO) of real phenomena. Furthermore, solving the problem needs much more computational and memory complexity than the general shortest path problem. A new model for Time-dependent Networks where the flow speeds of each link depend on time interval, is suggested. The model is more realistic since its solution maintains the NPP. Solving the problem needs just a little more computational complexity, and the same memory complexity, as the general shortest path problem. A solution algorithm modified from Dijkstra's label setting algorithm is presented. We extend this model to the problem of Minimum Expected Time Path in Time-dependent Stochastic Networks where flow speeds of each link change statistically on each time interval. A solution method using the Kth-shortest Path algorithm is presented.

• Management Science and Financial Engineering
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• v.10 no.1
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• pp.53-75
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• 2004

Most existing studies on time-dependent networks have been focused on finding a minimum delay path given a departure time at the origin. There, however, frequently happens a situation where users can select any departure time in a certain time interval and want to spend as little time as possible on traveling the networks. In that case. the delay spent on traveling networks depends on not only paths but also the actual departure time at the origin. In this paper, we propose a new problem in time-dependent networks whose objective is to find an optimal departure time given possible departure time interval at the origin. From the optimal departure time, we can obtain a path with minimum delay among all paths for possible departure times at the origin. In addition, we present an algorithm for finding an optimal departure time by enumerating trees which remain shortest path tree for a certain time interval.

• Journal of Distribution Science
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• v.14 no.2
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• pp.41-45
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• 2016

Purpose - Finding an optimal path is an essential component for the design and operation of smart transportation or logistics network. Many applications in navigation system assume that travel time of each link is fixed and same. However, in practice, the travel time of each link changes over time. In this paper, we introduce a new transportation problem to find a latest departing time and delivery path between the two nodes, while not violating the appointed time at the destination node. Research design, data, and methodology - To solve the problem, we suggest a mathematical model based on network optimization theory and a backward search method to find an optimal solution. Results - First, we introduce a dynamic transportation problem which is different with traditional shortest path or minimum cost path. Second, we propose an algorithm solution based on backward search to solve the problem in a large-sized network. Conclusions - We proposed a new transportation problem which is different with traditional shortest path or minimum cost path. We analyzed the problem under the conditions that travel time is changing, and proposed an algorithm to solve them. Extending our models for visiting two or more destinations is one of the further research topics.

• 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.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.192-200
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• 1998

The quickest path problem is one of the important things for quality of services in communication networks. It is to find a path to send a given amount of data from the source to the sink with minimum transmission time, where the transmission time is dependent on both the capacities and the traversal times of the arcs in the network. This is found under the networks that the capacity and the lead time of each ring are predetermined. It is general to solve the quickest path problem using shortest path algorithms. The relevant algorithms proposed till now are based on the capacity of rings in distributed environments. When the configuration of networks is changed, there can be two a, pp.oaches to find the quickest paths. The one is to find new quickest paths, and the other is to update the current quickest paths. As one of the algorithms for the latter, the distributed quickest path update algorithm was proposed. This paper aims to propose the distributed algorithm a, pp.icable to find the quickest path, when the configuration of networks is changed, using the quickest path tree update altorithm, and to verify its possibility of a, pp.ication by analyzing the transmission amount of data from one node to another from the theoretical point of view.

• 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.