Goal-directed navigation is a complex cognitive process that requires a precise relational representation of the spatial environment and its associated rewards. This process enables agents to choose optimal paths that maximize long-term rewards, which is essential for the survival of both humans and animals. While hippocampal place cells are traditionally linked to representing spatial location information, rodent studies have shown that they also exhibit predictive coding and reward-dependent place fields, suggesting their role extends beyond spatial representation. The Successor Representation (SR) model captures these broader functions by encoding spatial structure through predictive representations alongside a separate reward function to account for immediate rewards. Together, these components form a general-purpose map that is highly effective for optimizing future rewards. However, how the human hippocampus---operating with greater complexity, flexibility, and abstraction compared to rodents---supports goal-directed navigation remains unclear. Here, we investigate how human hippocampal neurons encode spatial and reward-related structures and whether these representations interact in ways that mirror the SR model. We analyzed single-unit recordings from neurosurgical patients performing a virtual spatial two-armed bandit task. In one spatial sequence, 27\% of the 113 neurons showed spatial tuning, with 92\% of place cells exhibiting significant predictive ramping activity as participants approached goal locations. In the other sequence, 18\% of neurons were identified as place cells, and 84\% displayed ramping effects. These findings closely align with SR-place fields, indicating that neurons actively encode participants' future locations. Additionally, 11\% of neurons exhibited firing rate modulation at reward collection points based on reward amount, suggesting a representation consistent with the SR reward function. This study provides the first direct evidence that human hippocampal neurons use successor-type signals to encode spatial and reward-related information, supporting goal-directed navigation. These findings lay crucial groundwork for understanding how the human hippocampus performs computations at the circuit level to support reward-based planning.