# Situationally Sensitive Path Planning
## 4 Methodology
This projection (a mapping) works interactively with the GIS, forming a third hyper-space in the slip-streamed architecture. Within this state space, agents operate as finite-state machines and poll state conditions within their unique individual situational lens. Our specification of this lens draws upon the conceptual literature from proxemics and spatial perception, which posits that pedestrians will view site-based obstacles and opportunities, as well as situational factors that embody them within their space-time path, and that they will do so in a visual field (frustum) that shifts fixture along with their intended motion velocity. We handle this lensing with malleable, interactive, and reciprocating geometries that can be provided by Velocity Obstacle formulations (and which, again, can be mapped to our GIS as a hyper-space) (Figure 3). Thus, we are simultaneously able to handle sites as geometry with regional properties (e.g., graph traversal) and to treat situations using partial space-time geometries as RVOs (while also retaining preferences toward regional properties). This has real-world practical foundation. As in mental map concepts, we assume that agents have access to a general rubric of traversal through the full regional scope of the enclosed space (i.e., they are not nudging into every nook and cranny of the space like insect motion) and that they may call upon that knowledge when calculating their actual (preferred) path through the environment. (The path planning heuristic ensures that agents, at any point in space and time, have only partial, although region-spanning, knowledge of the global space, rather than total awareness.) Similarly, based on real-world movement in enclosed spaces, an interim space between path planning and steering and collision detection and avoidance is usually observed as wayfinding [187]. Generally, this interim space is used in cognition to first anchor pedestrians to destinations for their motion, second to guide them along a habitual or rote path, and (importantly, with relevance to our implementation) third to essentially break up that path into a series of check-ins along the way. In this manner, movement adheres to global (geographical) knowns of the pedestrian's preferred motion, but can hot-glue to intervening (local, spatial) beacons along the way. Waypoints generally appear as fixed, reliable, and visible sub-sites along a path. Different people seek out different waypoints when walking, including landmarks, fixed attributes of spaces such as wall corners, or even approximated metrics of space and/or time [188]. As pedestrians near waypoints, for example, they may look up from their locomotion to essentially reorient and redraw their cognitive/mental map of the enclosed space. We accommodate this in our implementation using waypoints that are embedded as nodes in the graph structure. Again, this all runs in ways that are compatible with the GIS architecture of our implementation as a hyper-space. Our model implements properties of site and situation as either dynamic or scalar float values:  
* **Segment Length:** Geographic (Euclidean) length of route segment, calculated online from the endpoint node;
* **Path Condition:** Qualitative (physical) condition of a route segment, calculated via scene detection of streetscape design objects (user-placed objects in the design phase, e.g., trash cans);
* **Pedestrian Level of Service (PLOS):** The occupancy (i.e., crowd density and congestion) of a route segment, calculated as a ratio of agents per segment of traversable surface area;
* **Risk:** The level of risk associated with the route segment; this parameter is manually configurable by the user. Although not discussed here for brevity, we can set path complexity, attractiveness, and landmarks using other parameterizations, including procedural modeling, style sheets, dynamic lookups to GIS, and generative AI.