Path Optimization of Stochastic Transportation Network Based on Frequency-Domain Spanning Graph Model

Long ZHENG , Jing-lun ZHOU
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Abstract

Because of the uncertainty, openness, and complexity of modern transportation system, the path optimization becomes a challenge in most cases. To address this challenge, we propose a Frequency-domain Spanning Graph (FSG) method for searching the optimal path of stochastic transportation network in terms of various probability distribution function of path optimization problem. Furthermore, we design an improved algorithm to achieve the FSG model based on generalized adjacency matrix. By using FSG method to execute the mutual transformation of probability function between time-domain and frequency-domain, the quantitative analysis for the dynamic process of pass rate (probability) between node couple O-D (origin node and destination) can be obtained directly, and continuous probability distribution and discrete probability distribution can be handled. In addition, the algorithm is highly effective, and easy to be realized with low complexity. To demonstrate the performance of our method, a detailed example is implemented, and the results show that our method has the feasibility and effectiveness when compared with traditional method.

基于频域生成图模型的随机交通网络路径优化
由于现代交通系统的不确定性、开放性和复杂性,在大多数情况下,路径优化成为一个挑战。为了解决这一挑战,我们提出了一种频域生成图(FSG)方法,根据路径优化问题的各种概率分布函数来搜索随机交通网络的最优路径。在此基础上,设计了一种基于广义邻接矩阵的改进算法来实现FSG模型。利用FSG方法在时域和频域之间进行概率函数的互变换,可以直接对节点对O-D(起始节点和目的地节点)之间的通过率(概率)动态过程进行定量分析,并处理连续概率分布和离散概率分布。此外,该算法效率高,易于实现,复杂度低。为了验证该方法的有效性,通过一个详细的算例进行了验证,结果表明该方法与传统方法相比具有可行性和有效性。
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