Markovian traffic equilibrium assignment based on network generalized extreme value model
Markovian traffic equilibrium assignment based on network generalized extreme value model
复制标题
基于网络广义极值模型的马尔可夫交通均衡分配
DOI：
10.1016/j.trb.2021.10.013
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Akamatsu Takashi
中科院分区：
文献类型：
--
作者：
Oyama Yuki;Hara Yusuke;Akamatsu Takashi
本文建立了基于网络广义极值模型的马尔可夫交通均衡配流，称之为网络广义极值均衡配流。最近提出了使用NGEV模型进行路线选择建模，并且它使得能够在没有明确的路径枚举的情况下捕获路径相关性。然而，该模型在交通分配中的理论特性尚未在文献中进行研究，这限制了NGEV模型在交通分配领域的实用性。本研究通过提供NGEV均衡分配所需的理论发展来解决研究空白。我们首先表明，NGEV分配可以制定和解决传统的马尔可夫交通分配模型相同的路径代数。此外，我们提出了等价的NGEV均衡分配的优化公式。该配方使我们能够获得原始和对偶类型的有效的解决方案的算法。特别是，双重算法是基于加速梯度法，这是第一次应用于交通分配。数值实验表明，所提出的原始和对偶算法具有良好的收敛性和互补关系。
This study establishes Markovian traffic equilibrium assignment based on the network generalized extreme value (NGEV) model, which we callNGEV equilibrium assignment. The use of the NGEV model for route choice modeling has recently been proposed, and it enables capturing the path correlation without explicit path enumeration. However, the theoretical properties of the model in traffic assignment have yet to be investigated in the literature, which has limited the practical applicability of the NGEV model in the traffic assignment field. This study addresses the research gap by providing the theoretical developments necessary for the NGEV equilibrium assignment. We first show that the NGEV assignment can be formulated and solved under the same path algebra as the traditional Markovian traffic assignment models. Moreover, we present the equivalent optimization formulations to the NGEV equilibrium assignment. The formulations allow us to derive both primal and dual types of efficient solution algorithms. In particular, the dual algorithm is based on the accelerated gradient method that is for the first time applied in the traffic assignment. The numerical experiments showed the excellent convergence and complementary relationship of the proposed primal and dual algorithms.