## 3 Gap Analysis  
Table 2 provides a comprehensive analysis of the limitations found in previous research. The gap analysis of the reviewed literature underscores several notable limitations encountered in prior research. Notably, [PERSON] et al. demonstrated effectiveness in identifying patterns in common scenarios, but their method may struggle with complex, crowded trajectory movements.  
Additionally, [PERSON] et al.'s [11] approach, despite enhancing predictive accuracy, introduces uncertainty and noise by treating missing values as variables. The transformer-based model by [PERSON] et al.[17] exhibits promise with Bluetooth low-energy data but lacks exploration on other trajectory data types. [PERSON] et al.'s [22] MAP model faces constraints in handling new or unseen locations, while [PERSON] et al.'s [19] Bayesian filter framework adds complexity, potentially impacting scalability. Moreover, [PERSON] et al.'s [24] model, leveraging an RNN with an encoder-decoder architecture, may overlook comprehensive performance assessment due to limited evaluation metrics. These findings underscore the necessity for further research to address these limitations comprehensively.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors** & **Method** & **Dataset** & **Evaluation Metric** & **Limitation** & **Our Situation** \\ \hline \multirow{8}{*}{[PERSON] et al. [13]} & Encoder collected input data to create a context vector. Decoder used it for trajectory prediction, by Beam Search for sequence optimization. & & This method can identify patterns in displacement & The inD dataset we have used contains complex, common scenarios but may fail in movements, which our model has successfully addressed. \\ \cline{2-5}  & BRTTS-I Algorithm enhanced predictive accuracy with bidirectional recurrent dynamics, enabling error backpropagation from past time series analysis. & & & Treating missing values as variables adds uncertainty and noise, potentially diminishing model performance. & Our model applied data imputation techniques to estimate the missing values based on the available data, thus decreasing the effects of noise and uncertainty. \\ \hline \multirow{8}{*}{[PERSON] et al. [17]} & A transformer-based deep neural network was used. & BLE data packet generated by enabled the transformer to achieve good performance. & & The proposed approach may perform well on BLE data, but its effectiveness on other types of trajectory data has not been explored. & This problem also persists in our research, because apart from using the drone dataset, our model did not explore GPS or other kinds of datasets. \\ \hline \multirow{8}{*}{[PERSON] et al. [22]} & MAP consists of three core components: RNNs, an Attention Model, and a softmax classifier. RNNs capture spatial sequences, generating a high-level representation of historical movement summaries. & & One notable constraint is the model’s incapacity to household movement & This limitation also lies in our research. We did not check our model’s capacity or incapacity to unseen locations. \\ \cline{2-5}  & The authors introduced a two-block recurrent neural network (RNN) architecture. & ETH and UCY & ADE and FDE and FDE & The introduction of a Bayesian filter framework provided additional complexity, which may have impacted scalability. \\ \cline{2-5}  & The authors leveraged an RNN with an encoder-decoder architecture with an attention mechanism. & & Other than accuracy metrics with varying p and lambda, no other evaluation metric was used. & We used L1 loss, MSE loss, ADE loss, and quantile training loss to test our model’s correctness from different aspects. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Gap analysis of the reviewed literature.