基于空间认知和社区识别理论的路径选择模型

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。

Route choice model based on spatial cognition and community recognition theory

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.