用车联网数据精准测算城市道路可视性，找出行人最易看到的热点位置。
Rapid Quantification of Outdoor Object Visibility in Urban Setting Using Connected-Vehicle Fields of View
- 基于车辆轨迹推算动态视野范围，结合建筑顶点构建空间索引。
- 发现可视性高度集中，少数热点获远超平均的曝光量。
- 适合城市规划、广告投放与街面设施布局决策者参考。
在城市环境中，识别能最大化吸引车流视线的位置是城市分析的核心问题，广泛应用于城市设计、导航服务及街面设施布局。传统选址方法依赖静态交通流量或主观判断。本研究提出一种基于大规模车联网轨迹数据的数据驱动方法，通过插值轨迹推算每辆车位置的前向视野区域，结合从OpenStreetMap提取的建筑顶点，量化道路沿线数千个潜在兴趣点的累积视觉暴露量（即‘可视次数’）。核心技术在于对建筑顶点构建BallTree空间索引，实现高效（时间复杂度O(logN)）的半径查询，可快速判断数百万轨迹点对应的视域内包含哪些顶点，显著优于暴力几何检查。分析显示：1）可视性高度集中，存在明显‘视觉热点’，其曝光量远超平均水平；2）顶点级可视次数总和符合对数正态分布。
原文摘要 · Abstract (English)
Identifying locations that offer maximum visual exposure to passing vehicular traffic is a core problem in urban analytics, with applications spanning urban design, navigation, location-based services, and the placement of street-level assets. 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 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 along roadways. 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.
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