论文标题
基于网络通用极值模型的马尔可夫交通平衡分配
Markovian Traffic Equilibrium Assignment based on Network Generalized Extreme Value Model
论文作者
论文摘要
这项研究建立了基于网络通用极值（NGEV）模型的马尔可夫交通均衡分配，我们称之为NGEV平衡分配。最近提出了NGEV模型在路由选择建模中的使用，它使得无需明确的路径枚举即可捕获路径相关。但是，该模型在交通分配中的理论属性尚未在文献中研究，该文献限制了NGEV模型在交通分配领域的实际适用性。这项研究通过提供NGEV均衡分配所需的理论发展来解决研究差距。我们首先表明，NGEV分配可以在与传统的马尔可夫交通分配模型的代数相同的路径代数下制定和解决。此外，我们将等效优化公式提交给NGEV平衡分配。这些配方使我们能够得出有效的解决方案算法的原始和双重类型。特别是，双重算法基于第一次在流量分配中应用的加速梯度方法。数值实验显示了所提出的原始偶算法的出色收敛性和互补关系。
This study establishes Markovian traffic equilibrium assignment based on the network generalized extreme value (NGEV) model, which we call NGEV 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-dual algorithms.
