Geospatial analysis of residential parking behaviors using a semantic modeling approach. (April 2018) - Stewart, Kathleen; Fan, Junchuan; Schwarz, Chris; McGehee, Daniel V.
Highlights: Transform driving trajectories from geometric base to semantic behaviors. Trajectory mining module uses acceleration behaviors to detect the start of parking. Individuals show consistent parking behaviors over time under same spatial context. Different driving behaviors occur during parking even under same spatial context. Abstract: Pedal misapplications by drivers have received attention as being an underlying factor for the phenomenon known as sudden unintended acceleration (SUA) in vehicles. This research investigates behaviors during a common task for drivers, namely residential parking. Parking has been identified as a maneuver that is often linked with SUA mishaps. Using driving trajectories data from a set of four couples collected as part of a naturalistic driving study, we investigate whether consistent behaviors can be detected when parking at home from a geospatial perspective, i.e., whether deceleration and braking occur in a characteristic way at the end of a driving trajectory, and whether these behaviors vary when the geospatial context of parking changes. An ontology-based approach is used to frame the key behaviors of the naturalistic driving, and big data techniques are applied to extract parking-specific behaviors from driving trajectories. Results show that individuals showed relatively consistent parking behaviors under the same geospatial context and the standard deviation of the deceleration threshold has a larger discrepancy between couples parking at different residences than within couples where parking occurs at the same place.