The spatiotemporal sequencing of everyday activities in the large-scale environment

Path planning under spatial uncertainty

“…As pointed out in the introduction, not all spatial planning strategies described in the literature allow deriving predictions in the present scenario. Hence, we concentrate on the following planning strategies: First, the NN strategy, predicting that the closest target place is repeatedly chosen until all targets have been visited (Bureš et al, 1992;Gärling & Gärling, 1988;Gärling et al, 1986); and second, hierarchical planning strategies that can, in principle, be applied to the present scenario-the cluster strategy (Gallistel & Cramer, 1996;Wiener et al, 2004), the hNN method (Vickers, Bovet, et al, 2003), and the region-based planning strategy (Wiener & Mallot, 2003). All three hierarchical strategies have this in common: that the environment is structured into the different clusters or regions taken into account during planning.…”

“…Several planning strategies and heuristics have been described that allow for a reduction of computational effort: A particularly simple strategy to solve path planning tasks with multiple target locations is the nearest neighbor (NN) strategy (e.g., r Bure , Bure ová, & Nerad, 1992;Gärling & Gärling, 1988;Gärling, Säisä, Böök, & Lindberg, 1986;Hirtle & Gärling, 1992). It states that the closest target location is chosen repeatedly until all target locations have been visited.…”

Planning paths to multiple targets: memory involvement and planning heuristics in spatial problem solving

“…And, to the authors' knowledge, no study so far systematically investigated the role and impact of the various memory related constraints on path planning performance, planning processes, and the planning heuristics applied. Gärling and Gärling (1988) demonstrated that most shoppers who minimized the total distance of their shopping routes employed strategies similar to the NN algorithm (see also Gärling, Säisä, Böök, & Lindberg, 1986), a simple algorithm for solving TSP-like tasks quickly: from its current location, the NN algorithm repeatedly visits the closest target that has not been visited before until all target locations have been visited (e.g., Golden, Bodin, Doyle, & Stewart, 1980). Wiener and Mallot (2003) demonstrated that environmental regions inXuenced navigation behavior when planning short paths to visit multiple targets: participants minimized the number of region boundaries they crossed during navigation and preferred paths that allowed for fastest access to the region containing the target.…”

Route planning with transportation network maps: an eye-tracking study

“…Indeed, we found that search time was not affected by the task difficulty while the planning and selection time increased with the increasing task complexity. This result suggests that with the increasing task complexity more processes, Processes required during the Planning and Selection Phase, such as encoding the exact locations of connecting stations or routes in short term memory and manipulating this information, is also more cognitively demanding than a pure visual search (Gärling et al, 1986, Wiener, Ehbauer & Mallot, 2009. Such increases in cognitive demands are also known to result in shorter saccades (Just & Carpenter, 1976;Pomplun et al, 2001).…”