Modeling human motion in complex environments without losing long-range dependencies is difficult due to the large number of combinatorially distinct paths humans may follow. Existing representations avoid this difficulty by limiting the prediction of human motion to a local level. As a result, robot motion planning algorithms that use these representations are reactive in nature, and fail to exploit higher-order dependencies. We present a novel motion model capable of representing the global path behavior of people. Our model compactly encodes higher-order temporal dependencies inherent in human mobility traces on an abstract representation of the environment that lends itself to combinatorial planning. We incorporate uncertainties into the planning process using POMDPs and present a general predictive multi-robot planning algorithm applicable to pedestrian datasets commonly found in the literature. We evaluate our planner by simulating multiple instances of a variant of the visibility-based target-tracking problem inspired by our previous work. We report encouraging results that demonstrate our multi-robot plans exhibit desirable combinatorial structure, e.g. robot re-use.