Human navigation is shaped by cognitive strategies, spatial awareness, and learned heuristics, yet existing models struggle to capture individual differences in wayfinding. To investigate the cognitive basis of navigational behavior, we conducted a virtual reality experiment where participants maneuvered around a human obstacle in a controlled, static environment. Using trajectory-based features, we classified participants with PartNet, a neural network that outperformed ElasticNet and Random Forest classifiers. While PartNet captured subtle yet consistent behavioral patterns, its interpretability was limited. To address this, we developed an analysis pipeline revealing key behavioral factors, showing that navigational styles differ primarily in midline adherence and speed. Clustering and embedding analyses further demonstrated participant separability, highlighting both individual distinctions and shared tendencies. By identifying structured variability in navigation, our work advances cognitive models of spatial decision-making, informing theories of wayfinding, predictive modeling of human movement, and applications in assistive navigation and urban design.