标题： 城市可视性热点：基于空间索引利用车联网轨迹量化建筑顶点可视性 显示英文标题

标题： Urban Visibility Hotspots: Quantifying Building Vertex Visibility from Connected Vehicle Trajectories using Spatial Indexing

摘要： 户外广告和街道家具的有效布局需要准确识别能为目标受众提供最大视觉曝光的地点，尤其是机动车流。 传统选址方法通常依赖于静态交通流量统计或主观评估。 本研究介绍了一种基于数据的方法，通过分析城市环境中来自Compass IoT的大规模联网车辆轨迹数据，客观量化位置的可视性。 我们使用从插值轨迹推导出的每个车辆位置的前向投影可视区域来建模动态驾驶员视野。 通过将此与从OpenStreetMap提取的建筑物顶点位置相结合，我们量化了道路附近数千个潜在兴趣点的累积视觉曝光，即“可视计数”。 分析表明，可视性高度集中，确定了一些特定的“视觉热点”，它们接收到的曝光量远高于平均位置。 核心技术贡献在于在建筑物顶点上构建了一个BallTree空间索引。 这使得能够以高效的方式（O(logN)复杂度）进行半径查询，以确定哪些顶点落在数百万个轨迹点在多次行程中的视圈内，显著优于暴力几何检查。 分析揭示了两个关键发现： 1) 可视性高度集中，确定了明显的“视觉热点”，它们接收到的曝光量远高于平均位置。 2) 顶点上的聚合可视计数符合对数正态分布。 显示英文摘要

摘要： Effective placement of Out-of-Home advertising and street furniture requires accurate identification of locations offering maximum visual exposure to target audiences, particularly vehicular traffic. Traditional site selection methods often rely on static traffic counts or subjective assessments. This research introduces a data-driven methodology to objectively quantify location visibility by analyzing large-scale connected vehicle trajectory data (sourced from Compass IoT) within urban environments. We model the dynamic driver field-of-view using a forward-projected visibility area for each vehicle position derived from interpolated trajectories. By integrating this with building vertex locations extracted from OpenStreetMap, we quantify the cumulative visual exposure, or ``visibility count'', for thousands of potential points of interest near roadways. The analysis reveals that visibility is highly concentrated, identifying specific ``visual hotspots'' that receive disproportionately high exposure compared to average locations. The core technical contribution involves the construction of a BallTree spatial index over building vertices. This enables highly efficient (O(logN) complexity) radius queries to determine which vertices fall within the viewing circles of millions of trajectory points across numerous trips, significantly outperforming brute-force geometric checks. Analysis reveals two key findings: 1) Visibility is highly concentrated, identifying distinct 'visual hotspots' receiving disproportionately high exposure compared to average locations. 2) The aggregated visibility counts across vertices conform to a Log-Normal distribution.