Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.

中文翻译：

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迈向人群感知的室内路径规划（扩展版）

室内场所可容纳许多集体聚集的人群。这样的人群反过来会影响人们的路线选择，例如，人们从A到B走路时可能更喜欢避开拥挤的房间。本文研究了两种类型的可感知人群的室内路径规划查询。室内人群感知最快路径查询（FPQ）查找存在人群时旅行时间最短的路径，而室内最小人群路径查询（LCPQ）查找在途中遇到最少物体的路径。为了处理查询，我们设计了包含三个主要组件的统一框架。首先，室内人群模型组织室内拓扑并捕获房间之间的对象流。其次，时间演化的人口估算器会导出房间的人口以用于将来的时间戳，以支持查询处理中的可感知人群的路由成本计算。第三，两种精确的查询处理算法和两种近似的查询处理算法处理每种类型的查询。所有算法均基于室内人群模型上的图遍历，并使用相同的搜索框架，并在搜索过程中使用不同的策略来更新种群。所有建议均通过综合和真实数据进行实验评估。实验结果证明了我们的框架和查询处理算法的效率和可扩展性。