Mobility Prediction Based on Deep Learning Approach Using GPS Phone Data
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Abstract
Accurate prediction of activity location is a crucial component in various mobility applications and is particularly vital for the creation of customized, environmentally friendly transport systems. Next-location prediction, which entails predicting a user's forthcoming place by analyzing their previous movement patterns, has substantial ramifications in diverse fields, such as urban planning, geo-marketing, disease transmission, wireless network performance, recommender systems, and numerous other sectors. Recently, researchers have proposed a variety of predictors, including cutting-edge ones that utilize advanced deep learning methods, to tackle this problem. This study introduces robust models for predicting a user's future location based on their previous location. It proposes a Recurrent Neural Networks (RNNs) prediction scheme and a Gated Recurrent Unit (GRU), which are well-suited for learning from sequential data. Additionally, the clustering technique Density-Based Clustering (DBSCAN) is implemented to extract the stay points. Furthermore, the suggested method is more accurate at predicting the future than the current method, showing improvements in loss mean square error of up to 0.0005 in the RNN model and 0.01 in the GUR model. So, the models that were used led to a decrease in loss MSE, which was shown in the real-world dataset (Geolife) in this paper. The results are also consistent with other similar works that look at the same issue, showing how well the models can predict mobility.
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References
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