CAJ | 학술논문

城市道路交通状态具有空间自相关特征。某一道路交通状态的变化会对其周边道路产生影响，故把握道路交通状态的空间自相关性是提高交通规划、交通预测水平的基础。然而，城市道路交通状态又具有空间异质性，即道路交通状态的影响扩散并非各向同性，其使得道路交通状态空间自相关性的度量更为复杂，因此仅从地理空间下道路之间的邻近关系出发进行分析有失偏颇。同时，城市道路具有拓扑结构特征和几何形态特征，二者对于交通状态自相关性的影响和制约，却未引起足够重视。本文从城市道路的拓扑结构特征和几何形态特征出发，提出了一种新的交通状态空间自相关路段识别规则，即基于交通状态变化的路段空间识别规则，通过拓扑社区发现方法刻画路段在空间上的聚集特征，同时，基于Stroke跟踪的几何形态概化来描述道路交通状态变化影响的空间异质性。结果表明，利用本文提出的识别规则产生的交通状态自相关路段集合，较仅考虑地理空间邻近或拓扑结构的识别规则更为合理，更好地揭示了城市道路交通状态的空间自相关特征。
성시도로교통상태구유공간자상관특정。모일도로교통상태적변화회대기주변도로산생영향，고파악도로교통상태적공간자상관성시제고교통규화、교통예측수평적기출。연이，성시도로교통상태우구유공간이질성，즉도로교통상태적영향확산병비각향동성，기사득도로교통상태공간자상관성적도량경위복잡，인차부종지리공간하도로지간적린근관계출발진행분석유실편파。동시，성시도로구유탁복결구특정화궤하형태특정，이자대우교통상태자상관성적영향화제약，각미인기족구중시。본문종성시도로적탁복결구특정화궤하형태특정출발，제출료일충신적교통상태공간자상관로단식별규칙，즉기우교통상태변화적로단공간식별규칙，통과탁복사구발현방법각화로단재공간상적취집특정，동시，기우Stroke근종적궤하형태개화래묘술도로교통상태변화영향적공간이질성。결과표명，이용본문제출적식별규칙산생적교통상태자상관로단집합，교부고필지리공간린근혹탁복결구적식별규칙경위합리，경호지게시료성시도로교통상태적공간자상관특정。
Urban road traffic is spatially autocorrelated. The change of the traffic on a certain road will alter the surrounding roads’traffic status. Understanding the spatial autocorrelation of road traffic is essential for traffic planning and traffic prediction. However, unban road traffic is heterogeneous in spatial, which means that the traffic interactions between neighboring roads are not always isotropy. The spatial heterogeneity of urban traffic makes the measurement of spatial autocorrelation more complex, thus only uses spatial adjacency to define the traffic autocorrelated roads cannot well reveal the characteristics of spatial autocorrelation in urban road traffic. It is worth mentioning that urban roads have topological and geometric properties, which are neglected in the pre-vious research. The aim of our research is to analyze the spatial autocorrelation of urban road traffic based on the topological and geometric properties of urban roads. We first investigated the spatial clustering characteristics of urban roads using community detection algorithm, and then depicted the spatial heterogeneity of the traffic inter-action by measuring the importance of road segments with the use of the roads’generalized geometric forms. Based on those analyses, we proposed a novel approach to cluster together the roads whose traffic is spatially au-tocorrelated. Experiment results for the road network of Beijing indicate that the proposed approach performs better than the approaches that only consider the spatial adjacency or topological structure, which further implies that our approach can capture the spatial autocorrelation characteristics of urban road traffic more reasonably.