Anomaly detection has emerged as one of the most critical analytical challenges in modern cyber-physical systems, smart cities, surveillance networks, and large-scale digital infrastructures. The rapid expansion of sensing technologies, mobility platforms, and networked environments has resulted in unprecedented volumes of spatiotemporal trajectory data whose interpretation cannot rely on traditional static outlier detection techniques alone. Instead, anomaly detection must integrate contextual awareness, temporal continuity, behavioral semantics, and uncertainty-aware inference in order to correctly differentiate between benign variability and genuinely harmful or abnormal patterns. This study presents a comprehensive, theoretically grounded, and methodologically integrated framework for context-aware deep trajectory anomaly detection by synthesizing advances in auto-encoder modeling, attention-based sequence learning, probabilistic similarity analysis, contextual data stream processing, and interactive anomaly interpretation.

The conceptual foundation of this work draws from deep learning-based representation learning, particularly auto-encoder networks for capturing normal behavioral manifolds, as demonstrated by Shi et al. (2021) and Zhang et al. (2023), as well as attention-driven sequence modeling for real-time trajectory analysis, as proposed by Wang, Li, and Chen (2023). These approaches are complemented by distributional trajectory similarity modeling introduced by Wang et al. (2024), which allows anomalies to be detected not merely as isolated deviations but as statistically inconsistent behavioral processes over time. Furthermore, contextual anomaly detection methods in smart city data streams, as articulated by Xu, Zhang, and Liu (2020), provide the structural basis for integrating environmental, temporal, and situational cues into the anomaly decision process.

This research extends these foundations by embedding them within a context discovery framework inspired by Thorne (2025), where anomaly detection is no longer treated as a purely data-driven exercise but as a dynamic interpretive process that evolves with situational knowledge. In addition, classical robust statistical perspectives on outlier detection, such as those articulated by Rousseeuw and Leroy (2005) and Hodge and Austin (2004), are used to ensure that deep learning models remain theoretically grounded and resistant to spurious deviations. The framework also draws from hierarchical, interactive, and online detection paradigms developed for sensor networks and streaming environments, including the works of Chatzigiannakis et al. (2006), Ahmad et al. (2017), and Laxhammar and Falkman (2014).

The methodological contribution of this article lies in the integration of these diverse strands into a unified conceptual pipeline that models normal behavior as a learned distribution over trajectories, identifies deviations through distributional and representation-based discrepancies, and refines decisions through contextual reasoning and interactive interpretation. Rather than relying on static thresholds or single-model outputs, the proposed approach treats anomaly detection as a layered reasoning process in which deep models generate candidate anomalies that are then filtered and contextualized through probabilistic similarity metrics, Bayesian reasoning, and domain-informed interaction.

The results discussed in this article demonstrate that such a context-aware deep trajectory framework offers substantial improvements in the interpretability, robustness, and real-time applicability of anomaly detection in complex environments. By aligning representation learning with statistical rigor and contextual semantics, the framework reduces false alarms while enhancing sensitivity to subtle but meaningful deviations. The discussion further explores the theoretical implications of treating anomalies as context-dependent phenomena, the limitations associated with deep model uncertainty and data drift, and the future potential of combining interactive visual analytics with self-adapting anomaly detection systems.

Overall, this article provides a comprehensive and original synthesis of contemporary anomaly detection research, establishing a coherent theoretical and methodological foundation for next-generation intelligent monitoring systems in smart cities, transportation networks, and cyber-physical infrastructures.