Accelerating the k-shortest paths computation in multimodal transportation networks

S. Lam, T. Srikanthan
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引用次数: 7

Abstract

Intermodality in transportation systems is fast becoming a research topic of great interest. The computation of multiple paths in a multimodal network is desirable to identify efficient routes based on user preferences. This paper describes the clustering technique, which improves the performance of conventional k-shortest paths computations in multimodal transportation networks by first transforming the network into an acyclic representation through identification of cycles and clustering them into hypothetical nodes. The generalized Floyd algorithm is then applied on these clusters to compute the k-shortest paths. Simulation results show that the proposed technique significantly improves the performance of the conventional algorithm, particularly when the required number of k-shortest paths increases.
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加速多式联运网络中k最短路径的计算
运输系统中的多式联运正迅速成为一个备受关注的研究课题。多模式网络中多路径的计算是基于用户偏好识别有效路径的必要条件。本文描述了聚类技术,该技术首先通过识别循环并将其聚类到假设节点将网络转换为非循环表示,从而提高了多式联运网络中传统k最短路径计算的性能。然后在这些聚类上应用广义Floyd算法来计算k条最短路径。仿真结果表明,该方法显著提高了传统算法的性能,特别是当所需的k最短路径数增加时。
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