  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.
