# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Human Mobility Modeling and Prediction
#### 2.3.2 Models and Methods  
To solve NLP, some surveys have proposed various DL approaches. For example, based on RNN technique, flashback gives a sparse semantic trajectory, predicts the next point of interest (POI) by looking for similar trajectories in terms of temporal characteristics. It is evaluated using social media posts from [66]. In variational attention based next location (VANext), the use of separate encoders to represent the semantic relationships among POIs provide historical and current trajectories in datasets [67]. DeepMove is an RNN for mobility prediction from sparse trajectories. In addition, it embeds multiple factors that govern human mobility, capturing complicated sequential transitions [68]. CFP implements several DL approaches. It uses local-dilated region-shifting network (LDRSN) that consists of five modules. The first module is convolutional residual unit. It leads local spatial dependencies. The second module uses dilated units. Dilate unit deals with distant spatial dependencies and, being a convolution, captures the correlation of two areas. The outputs of these two modules are forwarded and fused to two other modules. Although one handles short-term temporal patterns, the other has long-term temporal dependencies. The long-term module is followed by a long short-term memory (LSTM) model. This model is evaluated on the dataset of taxis' in New York City using MAPE, RMSE, and MAE [52]. Deep spatio-temporal with irregular convolutional residual network (DST-ICRL) combines LSTMs with convolutional residual units. Similarly to spatial-temporal dynamic network (STDN), DST-ICRL uses three pipelines to uncover different time series and spatio-temporal dynamics. In the experiments, in [69], the authors evaluated the flows from mobile phone data, taxi trajectories data, metro or bus swiping data, and bike-sharing data. Other authors use ConvLSTMs to characterize the spatio-temporal dependencies. For example, they take as input a spatial and temporal sequence composed of the number of bicycles in an area. [70] denotes the spatio-temporal dependency, representing the demand for picking up or returning public bikes. The metrics used for the evaluation are the RMSE and the MAE. In TG, we can highlight several projects. Being a model-free GAN framework, MoveSim model domain knowledge of human mobility regularity. For example, [71] conducts experiments on two real-life mobility datasets (i.e., mobile operator and GeoLife-GPS), demonstrate that our framework outperforms seven state-of-the-art baselines significantly in terms of improving the quality of simulated mobility data by 35%. This model is evaluated based on a set of attributes, such as the radius of gyration, number of locations visited daily, G-rank represented in a trajectory dataset. [72] presents a generative model called sequential variational autoencoder (SVAE). This model is based on the combination of two models: LSTM and variational autoencoder (VAE). It combines the ability of LSTMs to process sequential data with the ability of VAEs to construct a latent space that captures salient features of the training data. Then, in data processing, the generative model forces the input trajectories to have fixed timestamps. Lastly, the authors evaluate the SVAE through the mean distance error (MDE). In its turn, [PERSON] et al. [73] trains GAN-based framework to generate density distributions using two datasets extracted from MoMo and taxis in San Francisco. The framework was measured using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percent error (MAPE). The original GM applied in analysis of population included only two variables: population size and spatial distance [74]. Since then, many studies added other variables such as age, politics, income, education, and so on in model. The gradual incorporation of these factors into the model is contributing in explanation of migration phenomena [75, 76, 77].