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A heuristic model of bounded route choice in urban areas

机译:市区有界选路的启发式模型

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There is substantial evidence to indicate that route choice in urban areas is complex cognitive process, conducted under uncertainty and formed on partial perspectives. Yet, conventional route choice models continue make simplistic assumptions around the nature of human cognitive ability, memory and preference. In this paper, a novel framework for route choice in urban areas is introduced, aiming to more accurately reflect the uncertain, bounded nature of route choice decision making. Two main advances are introduced. The first involves the definition of a hierarchical model of space representing the relationship between urban features and human cognition, combining findings from both the extensive previous literature on spatial cognition and a large route choice dataset. The second advance involves the development of heuristic rules for route choice decisions, building upon the hierarchical model of urban space. The heuristics describe the process by which quick, 'good enough' decisions are made when individuals are faced with uncertainty. This element of the model is once more constructed and parameterised according to findings from prior research and the trends identified within a large routing dataset. The paper outlines the implementation of the framework within a real-world context, validating the results against observed behaviours. Conclusions are offered as to the extension and improvement of this approach, outlining its potential as an alternative to other route choice modelling frameworks. (C) 2015 The Authors. Published by Elsevier Ltd.
机译:有大量证据表明,城市地区的路线选择是一个复杂的认知过程,是在不确定的条件下进行的,并形成了部分观点。然而,传统的路线选择模型继续围绕人类认知能力,记忆力和偏好的性质做出简单化的假设。本文介绍了一种新的城市地区选路框架,旨在更准确地反映出选路决策的不确定性,有界性。介绍了两个主要进展。首先涉及定义代表城市特征与人类认知之间关系的空间分层模型,并结合先前关于空间认知的大量文献和大型路线选择数据集的发现。第二个进步涉及在城市空间的分层模型的基础上开发用于路线选择决策的启发式规则。启发式方法描述了当个体面临不确定性时做出快速,“足够好”的决策的过程。根据先前研究的发现以及大型路由数据集中发现的趋势,再次构造和参数化模型的这一元素。本文概述了该框架在现实环境中的实现,并根据观察到的行为验证了结果。提供了有关此方法的扩展和改进的结论,概述了其作为其他路线选择建模框架的替代方法的潜力。 (C)2015作者。由Elsevier Ltd.发布

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