关键词: 众源轨迹数据, 轨迹延续性, 长序特征, 立体交叉, 路网构建

Abstract:

The grade-separated junction is a complex multi-level structure composed of roads intersecting longitudinally and transversely. With the modernization of transportation, extracting the geometric and topological structures inside the grade separation from trajectory data is a key step to build a refined navigable road network. Most existing methods extract roads based on the local distance and orientation similarity of trajectory units, which may lead to errors such as merging nearby roads and missing structures. Therefore, this paper proposes a method to construct the road network of highway interchanges based on combination of local and long-term trajectory feature. Firstly, based on the continuity of trajectories, trajectory segments are classified into trajectory clusters with unified paths. The central line of the trajectory clusters is extracted using an adaptive binarization method. Then, considering the local direction feature and long-term distance feature, the central line tracking from the same entrance is carried out to dynamically capture the diversion phenomenon of nearby parallel roads and extract the subnetwork. Finally, the node candidate set of the subnetwork is clustered and the road segments are truncated based on node information. All the subnetworks are merged to generate the road network structure. The road network construction experiments using crowdsourcing trajectory data in Shenzhen are conducted to evaluate the performance of the proposed method, and results show the effectiveness and high accuracy in geometric and topological information. The overall GEO-F₁ score reaches 94.5%, and the TOPO-F₁ score is 94.8%, which outperforms existing state-of-the-art methods.

Key words: crowdsourcing trajectory data, trajectory continuity, long-term feature, grade separation, road network construction