混合流量网络中链路容量约束的流捕获定位模型

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2022-07-01 DOI:10.2478/jaiscr-2022-0015
Pingping Liu, Jinde Cao, Yiping Luo, J. Guo, Wei Huang
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引用次数: 0

摘要

摘要本文构造并求解了混合交通网络中具有链路容量约束的充电设施选址问题。研究这一问题的原因是,传统的用户均衡模型研究大多缺乏或缺少链路容量约束,从而导致道路交通网络状态定义的模糊性。在路网中加入容量约束是提高传统均衡模型现实性的一种妥协。本文提出了考虑路段容量约束条件下的充电设施效率评价的两层模型。所提出的双层模型中的上层模型是一个非线性整数规划公式,其目的是使捕获的纯电动汽车环节流最大化。低层模型除了包含混合路网上电动汽车的路段容量约束和行驶距离约束外,是典型的交通均衡分配模型。在Frank-Wolfe算法的基础上,采用改进的算法框架对所构造的问题进行求解,最后通过数值算例对所提出的模型和求解算法进行了验证。
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Flow-Capture Location Model with Link Capacity Constraint Over a Mixed Traffic Network
Abstract This paper constructs and settles a charging facility location problem with the link capacity constraint over a mixed traffic network. The reason for studying this problem is that link capacity constraint is mostly insufficient or missing in the studies of traditional user equilibrium models, thereby resulting in the ambiguous of the definition of road traffic network status. Adding capacity constraints to the road network is a compromise to enhance the reality of the traditional equilibrium model. In this paper, we provide a two-layer model for evaluating the efficiency of the charging facilities under the condition of considering the link capacity constraint. The upper level model in the proposed bi-level model is a nonlinear integer programming formulation, which aims to maximize the captured link flows of the battery electric vehicles. Moreover, the lower level model is a typical traffic equilibrium assignment model except that it contains the link capacity constraint and driving distance constraint of the electric vehicles over the mixed road network. Based on the Frank-Wolfe algorithm, a modified algorithm framework is adopted for solving the constructed problem, and finally, a numerical example is presented to verify the proposed model and solution algorithm.
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来源期刊
Journal of Artificial Intelligence and Soft Computing Research
Journal of Artificial Intelligence and Soft Computing Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.00
自引率
25.00%
发文量
10
审稿时长
24 weeks
期刊介绍: Journal of Artificial Intelligence and Soft Computing Research (available also at Sciendo (De Gruyter)) is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.
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