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首页> 外文期刊>EURO journal of transportation and logistics >Day-to-day travel time perception modeling using an adaptive-network-based fuzzy inference system (ANFIS)
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Day-to-day travel time perception modeling using an adaptive-network-based fuzzy inference system (ANFIS)

机译：使用基于自适应网络的模糊推理系统（ANFIS）进行日常旅行时间感知建模

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摘要

Travel time perception and learning play a central role in the modeling of day-to-day travel choice dynamics in traffic networks and have attracted the attention of many researchers, specifically for the analysis and operation of intelligent transportation systems and travel demand management scenarios. In this paper, a fuzzy learning model is proposed to capture the mechanism by which travelers update their travel time perceptions from one day to the next, taking into account their experienced travel times. To capture travelers' mental representations of uncertain travel time involving imprecision and uncertainty, a combined artificial neural network and fuzzy logic (neuro-fuzzy) architecture called adaptive-network-based fuzzy inference system is employed. This framework, which utilizes a set of fuzzy if-then rules, can serve as a basis for modeling the qualitative sides of travelers' knowledge and reasoning processes. From the output of this study, the results of our laboratory-like experiment provide a good fit to the stated data of travelers' behavior, and may reflect the fact that the neuro-fuzzy approach can be considered a promising method in learning and perception updating models. Finally, the proposed learning model is embedded in a microscopic event-based simulation framework to evaluate its credibility within a day-to-day behavior of the traffic network. The results of the simulation, which converge to the equilibrium state of the test network, are finally presented, implying that the proposed perception updating model operates properly.
机译：出行时间的感知和学习在交通网络中日常出行选择动态建模中起着核心作用，并且引起了许多研究人员的关注，特别是在智能交通系统的分析和运行以及出行需求管理场景方面。在本文中，提出了一种模糊学习模型，以捕获旅行者在考虑到他们经历的旅行时间之后，从一天到第二天更新他们的旅行时间知觉的机制。为了捕获旅客的不确定旅行时间的心理表示，其中包括不确定性和不确定性，采用了组合的人工神经网络和称为自适应网络的模糊推理系统的模糊逻辑（神经模糊）体系。该框架利用一组模糊的if-then规则，可以作为对旅行者知识和推理过程的质性方面建模的基础。从这项研究的结果来看，我们类似实验室的实验结果非常符合旅行者行为的既定数据，并且可能反映了以下事实：神经模糊方法可以被认为是学习和感知更新中的一种有前途的方法楷模。最后，将所提出的学习模型嵌入基于微观事件的仿真框架中，以评估其在交通网络日常行为中的可信度。最后，给出了收敛到测试网络平衡状态的仿真结果，这表明所提出的感知更新模型可以正常运行。

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