## 2 Related Work
This aided in capturing the sequential aspect of human trajectory as well as the geo-spatial aspects. A deep CNN decoder, a softmax classifier, and a transformer encoder-based feature extractor made up the deep learning model. Because it can understand linkages and dependencies, the transformer's main feature, Multi-head Self-Attention, confirmed that the sequence-to-sequence model exhibited good performance. The CRAWDAD unm/blebeacon [18] dataset, an open-source human mobility dataset, was used for the study. The problem of trajectory prediction from partial observations owing to the misdetection of dynamic agents (cars, pedestrians, etc. ), which can be brought on by poor image quality or occlusion by other dynamic agents, was studied by [PERSON] et al. [19]. The standard method for handling the incomplete trajectory problem has been to consider misdetection cases to be anomalies and remove them from the dataset. Because such a strategy eliminates the possibility of coordination with the agents that are excluded, there is a higher chance of major mishaps. Traditional methods for trajectory prediction have included those based on Bayesian filters. Their fundamental structure has made them unreliable for long-term prediction. RNNs, most notably, LSTM, are now widely used in modeling to anticipate human motions. The study by [PERSON] et al. proposed a two-block RNN for trajectory prediction from a partial trajectory due to misdetection, which learned the inference step of Bayesian filters. Two publicly accessible datasets, ETH [20] and UCY [21], were used in the experiment. [PERSON] et al. [22] introduced a novel model called Move, Attend, and Predict (MAP), which forecasts a person's next location based on motions previously captured by a mobile device. The model used a deep neural network to identify the important components of mobility history for each location. The program also used an approach called embedding representation learning to extract relevant properties from the movement data. The model was tested on two big datasets and demonstrated that it outperformed other models in location prediction. The study solely took into account two types of input data: locations and timings. It ignored other elements that may impact people's movements, such as preferences, social networks, weather, traffic, etc. The model was tested on two datasets: Geolife and Foursquare [23]. [PERSON] et al. [24] introduced a novel deep learning approach to address the prevalent problem of missing trajectory data in GPS applications. Their proposed method leveraged an RNN with an encoder-decoder architecture and an attention mechanism [25]. To enhance data reconstruction quality, they introduced a new loss function based on the R2 coefficient and implemented a smoothing technique using a moving average and a Savitzky-Golay filter [26]. Evaluating their model on a real-world GPS dataset from the Geolife project, comprising 17,621 trajectories collected over five years from 182 users, they demonstrated its superiority over several baseline methods, including linear interpolation, cubic spline interpolation, a Kalman filter, LSTM, and Bi-LSTM in terms of recovery accuracy and result stability across various missing rates and patterns. Additionally, through ablation tests and parameter sensitivity analysis, they validated the effectiveness of their model components and design choices, underscoring the potential of their deep learning approach for robust trajectory data reconstruction.