# Situationally Sensitive Path Planning
## 4 Methodology
### Global Environment Layer  
The global layer (Figure 3) provides, to an agent (yellow bubble in Figure 3) upon request, a least-cost path--from one waypoint in the virtual geography of the enclosed space design to another waypoint along that path. One of these waypoints will be a terminal node, which forms the destination for the classic shortest path planner. This is consistent with origin-destination (source-to-sink) trip planning: people usually come from one location and have in mind their next, global destination, and the locations at each \"end\" of the trip are usually site-specific anchors (or the \"anchor\" in an anchor point hypothesis of spatial behavior [189]). The global path therefore represents the substantive \"big picture\" that a pedestrian will have of how they wish to move through the designed space (e.g., from here to there, with the shortest distance, with these waypoints along the way). The local trajectory to be followed _as_ that path will then consider situational factors of the hyper-local details of the space, as well as the dyadic, group, and crowd patterns that emerge during motion [140; 190; 191].  
Agents default to a simple traversal-by-wayfinding along the shortest path if moving alone in the space. This straightforwardly sets the serial node in the graph as the next destination goal for movement, which is articulated as a steer-by-seeking maneuver. Upon reaching a waypoint, the model pedestrian will then recompute their path. This implies that agents in our model commit to their current paths and only reconsider site factors upon reaching a pivotal point in their current path, such as intersections. This is consistent with the \"looking up\" type of behavior we discussed above. It is also consistent with case study evidence from behavioral geography that shows that pedestrian walkers will decompose their initial global path to subdivide their traversal space using waypoints. Similar observations have been made in case studies from civil engineering [177] and from the movement psychology literature [19]. Evidence from environmental psychology has also indicated that landmarks anchor one's space-time paths to localized, situational factors.  
In implementation, the global layer treats the traversable virtual environment as a weighted graph with nodes and edges serving as waypoints and pathways, respectively. When an agent reaches a waypoint and subsequently requests an updated optimal path to their target node, the global layer returns a least-cost path calculated from [PERSON]'s shortest path algorithm with edge costs computed using each edge's property values and a unique set of preference weights provided by the requesting agent. Here, nodes represent the physical nature of the waypoints: their positions in the world and acceptable radii that an agent can use to consider themselves as having reached the node. The radii are considered conceptually to be touchpoints with that waypoint, and we leave this open in implementation: they could be artifacts of the space that are individualized, or environmental affordances that are specific to a set of agents, or they could be generalized atmosprics designed for touchpoint appeal to all agents. Meanwhile, the edges are also open as design levers and can be manually input or they may be designed procedurally, e.g., using different area-based traversal schema such as retail zoning in shops [192], education stations in schools [193], assembly sites in transit stations [194], or even layouts in buses and trains themselves [195]. Similarly, the edges could be auto-computed as parameter values used in travel cost determination: [196], for example, showed that this can be achieved using the principles of least effort for outdoor path planning.