# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Deep Learning Methods
It is worth noting that the architecture of the solution proposed by the authors is in the form of an autoencoder. The method was validated on ETH/UCY datasets. The results of this method are similar and slightly inferior to the rest of the state-of-the-art. The innovation of this work lies in the introduction of a trajectory prediction model that seamlessly integrates cross-attention modules and transformer blocks. This unique architecture, resembling an autoencoder, is adept at predicting human trajectories in urban-like environments. However, a limitation is its attention module's permutational invariance, which is not ideal for temporal information, although this is addressed by concatenating 1D positional encodings with agent embedding. Looking forward, the model's simplified architecture offers flexibility for further configurations or modifications, positioning it as a promising foundation for future trajectory prediction endeavours. In their comprehensive study, [PERSON] et al. [65] propose a novel methodology to predict human mobility, which is known as DeepMove. This approach holds significant potential for various applications reliant on location data. The challenges these researchers faced included the complex regularity of sequential transitions, multi-level periodicity, and the heterogeneity and dispersion of trajectory data. To address these issues, they employed a multi-modal embedded recurrent neural network alongside a historical attention model. This model incorporated two specific mechanisms to effectively handle the complexities. The authors evaluated their model on three real-world mobility datasets and showed that DeepMove outperformed state-of-the-art models by more than 10%. Albeit this comparison was not of high quality due to the fact that they used PMM, RNN, and the Markov Model, which are fairly basic methods. Nevertheless, they showed that DeepMove provided clearer explanations for its predictions compared to other neural network models. In summary, the authors have achieved a novel approach that accurately predicts human mobility and provides interpretable results to improve performance in location-based applications. The study's key innovation is the introduction of DeepMove, which is designed to predict human mobility. DeepMove is addressing challenges such as complex sequential transitions, multi-level periodicity, and the heterogeneity of trajectory data. However, a limitation in their comparison arises from using these basic methods as benchmarks. For future work, there should be an expansion of DeepMove to predict spatiotemporal points by considering potential durations. Additionally, the semantic context, such as points of interest and user tweets, should be incorporated. That would not only predict the location, but also the underlying motivations behind user movements. In a noteworthy contribution to the field, [PERSON] et al. [66] present a distinctive model for next position prediction utilizing LSTM neural networks. The paper holds a unique position. It does not meet the EC5 condition, as it deals with trajectory prediction in the context of ships' geographical position. However, it also presents a method involving LSTM. What adds to its uniqueness is that this LSTM method's ready implementation can be found in the authors' repository. The authors present a simple method for predicting the next position of ships, which includes three pipelines: training, transfer learning, and inference. The training pipeline processes ship trajectories, converting geolocations into normalized distance and bearing features, and then feeds them into a genetic algorithm. It is worth mentioning here that the approach of normalizing the trajectory data could be beneficial in the model prototype itself and could improve the quality of the said model. This is the second reason why this work was included. The transfer learning pipeline uses a knowledge base with stored trained DL models. In contrast, the inference pipeline uses the output model from the transfer learning pipeline.