基于空间认知和社区识别理论的路径选择模型

doi:10.3976/j.issn.1002-4026.2020.06.013

山东科学 ›› 2020, Vol. 33 ›› Issue (6): 96-102.doi: 10.3976/j.issn.1002-4026.2020.06.013

基于空间认知和社区识别理论的路径选择模型

刘喜敏¹，徐宁²，卢守峰^2*

1. 南京信息职业技术学院智能交通学院，江苏南京 210023；2.长沙理工大学交通运输工程学院，湖南长沙 410114

收稿日期:2020-04-13 出版日期:2020-12-09 发布日期:2020-12-10
通信作者: 卢守峰（1978—），男，博士，教授，研究方向为中观交通流模型、智慧交通。E-mail:itslu@126.com E-mail:ximunliu@csust.edu.cn
作者简介:刘喜敏（1980—），女，博士，研究方向为交通流理论、智能交通。E-mail:ximunliu@csust.edu.cn
基金资助:
湖南省教育厅优秀青年项目（15B011）

Route choice model based on spatial cognition and community recognition theory

LIU Xi-min¹, XU Ning², LU Shou-feng^2*

1.Intelligent Transport Department, Nanjing Vocational College of Information Technology, Nanjing 210023, China；2. Transportation Engineering College, Changsha University of Science and Technology, Changsha 410114, China

Received:2020-04-13 Online:2020-12-09 Published:2020-12-10

摘要/Abstract

摘要： 将启发式决策、空间认知以及社区识别理论结合，描述出行者的路径选择与路网结构的关系。采用基于模块增益的社区结构算法解构路网结构，描述人们的认知过程，建立相应的路径选择算法。以长沙市中心城区路网为例，利用建立的路径选择方法，分别用静态路阻（距离）、动态路阻（速度）对路网进行解构，计算路径选择集；采用问卷调查方法和出租车GPS数据对实际的路径选择轨迹进行提取，并将理论计算结果与实际调查结果进行对比分析，采用静态路阻的一致率为85%，采用动态路阻的一致率为73%。结果表明建立的集成空间认知和模块增益的路径选择模型可以较好地描述人们的路径选择过程，对于静态路阻的路径选择描述具有更高的准确性。研究成果对于城市规划和交通规划具有一定的借鉴价值。

关键词: 城市交通, 路径选择, 空间认知, 社区识别, 模块增益

Abstract: Heuristic decision making, spatial cognition, and community recognition theory are combined to describe the relationship between route selection and road network structure. The community structure algorithm based on module gain is used to deconstruct the road network structure, describe people's cognitive process, and establish the corresponding path selection algorithm. Taking the road network within the central area of Changsha city as an example, the proposed path selection method is used to deconstruct the road network and calculate the path selection sets for the static road impedance (distance) and dynamic road impedance (speed). The questionnaire survey and taxi GPS data are used to extract the actual path selection trajectories, and the theoretical computation results and the actual survey results are compared. When the static road resistance is used, the consistency rate is 85%, when the dynamic road resistance is used, the consistency rate is 73%. These results show that the proposed model integrating spatial cognition and community recognition can describe people’s route choice process better. The model has a better accuracy for static road resistance-based route choice. The results of this study can be a reference for urban planning and traffic planning.

Key words: urban traffic, route choice, spatial cognition, community recognition, module gain

中图分类号:

U491.4

刘喜敏, 徐宁, 卢守峰. 基于空间认知和社区识别理论的路径选择模型[J]. 山东科学, 2020, 33(6): 96-102.

LIU Xi-min, XU Ning, LU Shou-feng. Route choice model based on spatial cognition and community recognition theory[J]. Shandong Science, 2020, 33(6): 96-102.

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基于空间认知和社区识别理论的路径选择模型

Route choice model based on spatial cognition and community recognition theory

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