Identifying spatial communities in massive vehicle trajectory data greatly facilitates the understanding of spatial interactions in a city. However, it is still challenging to identify irregularly shaped and statistically significant spatial communities in vehicle movements. To overcome this challenge, we develop a spatial scan statistic based on ant colony optimization. A spatially embedded network was constructed with road segments as nodes and the numbers of vehicle trips between the road segments as weights. To evaluate the statistical significance of spatial communities, we first defined a random graph with a given expected strength sequence, and then, constructed a new likelihood ratio test statistic. To detect irregularly shaped spatial communities without a brute-force search, a significance test was first used to identify road segments highly interacting with other road segments, and then, a contiguity-constrained ant colony optimization was employed to combine these road segments to form spatial communities. The statistical significance of the spatial communities was evaluated using Monte Carlo simulation. Experiments on both the simulated and observed trajectories showed that the proposed method outperforms the five state-of-the-art methods in detecting irregularly shaped spatial communities and ensures that the detected spatial communities are statistically significant.

中文翻译：

---

用于检测城市道路网络上车辆运动空间社区的空间扫描统计

在海量车辆轨迹数据中识别空间社区极大地促进了对城市空间相互作用的理解。然而，在车辆运动中识别形状不规则且具有统计意义的空间社区仍然具有挑战性。为了克服这一挑战，我们开发了基于蚁群优化的空间扫描统计。以路段为节点，路段之间的车辆行程次数为权重，构建了一个空间嵌入的网络。为了评估空间社区的统计显着性，我们首先定义了一个具有给定预期强度序列的随机图，然后构建了一个新的似然比检验统计量。为了检测不规则形状的空间社区而无需暴力搜索，首先使用显着性检验识别与其他路段高度交互的路段，然后采用邻接约束蚁群优化将这些路段组合形成空间社区。使用蒙特卡罗模拟评估空间社区的统计显着性。对模拟轨迹和观测轨迹的实验表明，所提出的方法在检测不规则形状的空间群落方面优于五种最先进的方法，并确保检测到的空间群落具有统计学意义。使用蒙特卡罗模拟评估空间社区的统计显着性。对模拟轨迹和观测轨迹的实验表明，所提出的方法在检测不规则形状的空间群落方面优于五种最先进的方法，并确保检测到的空间群落具有统计学意义。使用蒙特卡罗模拟评估空间社区的统计显着性。对模拟轨迹和观测轨迹的实验表明，所提出的方法在检测不规则形状的空间群落方面优于五种最先进的方法，并确保检测到的空间群落具有统计学意义。