【摘要】 由于城市路网更新频繁及传统地图更新方法的局限性，很多城市路网地图数据无法及时更新。基于移动轨迹数据的路网地图更新是实现城市路网地图快速、高效更新的有效途径。虽然目前基于移动轨迹数据发展了一些基于栅格化的方法、基于聚类的方法和增量化的方法来提取与更新城市路网，但这些方法对于参数依赖性较强，且难以准确提取较为复杂的道路形状。因此，本文结合移动轨迹数据的特点及道路网的结构特征，提出了一种基于移动轨迹匹配的城市路网地图动态更新方法。首先，通过轨迹点的位置和方向属性进行轨迹点–路段匹配；然后，对未匹配的轨迹点采用顾及方向约束的空间自适应聚类算法进行聚类，并对每个聚类进行最优曲线拟合，获得新增道路；最后，以城市浮动车轨迹数据为例进行实验与对比分析。实验结果表明，与现有方法相比，本文方法需要的参数较少，且能够处理复杂形状道路的提取，具有较高的运行效率。更多还原

【Abstract】 Due to the frequent updates of urban roads infrastructure and the limitations of traditional road updating methods, many urban roads cannot be updated promptly. To date, road network updating based on moving trajectories has proven to be an effective way to realize the rapid and efficient updating of urban roads. While some methods based on GPS trajectories, such as grid-based methods,clustering-based methods and incremental methods, have been developed, these methods tend to be sensitive to parameters. Moreover, it is still difficult for these methods to accurately extract the incremental roads with complex structures. In light of these concerns, we propose an automatic urban road updating method based on moving trajectory matching. This method takes into account both the characteristics of the moving trajectory data and the structural characteristics of the urban roads. In this paper, the location and direction of the trajectory points are firstly used to make point-to-line matching. Then, an adaptive spatial clustering algorithm, which incorporates the direction constraint, is developed to segment the unmatched points into tracks, and the optimal curve fitting is performed for each cluster to obtain the center lines of the incremental roads. Finally, the urban floating car trajectory data was used to test the proposed method. When compared with commonly used rasterization methods and center track point fitting methods, our proposed method can better identify new additive roads in the urban road network. This paper’s primary contributions are as follows:(1) We focus on extracting localized changes of the existing road network using a partial trajectory map matching strategy. This avoids the reconstruction of the entire urban road network, thereby enhancing the efficiency of real-time update of the urban road network;(2) Based on the spatial proximity and direction constraints, the local matching method of massive track-road network is proposed. This approach offers high matching accuracy and lower computing complexity;(3) The adaptive spatial clustering method based on direction constraints is used, which can effectively distinguish trajectory clustering in different road directions and helps to effectively extract complex road structure information such as road intersections, and the proposed method requires fewer parameters compared to the existing available methods. Experimental results highlight that, compared with the existing methods, our proposed method performs reasonably better in comparison with the existing state-of-the-art methods.更多还原