# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 4 CPS Application Domains
### Transportation-Based CPS
#### 4.1.1 Traffic Forecasting  
Traffic forecasting predicts future traffic patterns in an urban or city transportation system, which is necessary for traffic control, navigation systems management, and transportation planning. Accurate traffic forecasting aids in reducing congestion, enhancing safety, and maximizing the use of available transportation resources. Nowadays, DNNs learn the latent relationships and patterns within the traffic data to generate predictions based on those patterns. This motivated the effort of [PERSON] et al. [37], who proposed a _\"wide-attention and deep-composite (WADC) model\"_. To investigate its performance, they used CNN-LSTM to train the model with traffic flow spatial-temporal datasets. The result revealed that it outperformed other models. Similarly, [PERSON] et al. [55] proposed a _\"graph attention-temporal convolutional network (GATCN)\"_ to forecast traffic speed in the short term. Graph attention and temporal convolution networks are combined to form each layer in the GATCN to apprehend the hidden spatial-temporal relationships concurrently. Likewise, [PERSON] et al. [56] proposed a capsule network (CapsNet) and Nested LSTM (NLSTM) for network speed prediction, in which CapsNet was considered to extract extensive spatial features from roadway networks, while NLSTM was leveraged to capture traffic state hierarchical temporal dependencies. Furthermore, [PERSON] et al. [57] aimed to achieve an accurate and adaptable scheme for traffic flow prediction by proposing a graph-based network model. The model employed a fully connected layer to create a matrix from traffic data. LSTM was applied to the data to capture the temporal dependency, while ChebNet captured the spatial dependency. The spatial-temporal attributes were further combined for accurate traffic flow forecasting. [PERSON] et al. [58] stated that graph-based neural networks could be applied to enhance forecasting of traffic speed. To this end, they proposed a scheme that can learn time-specific spatial dependencies and a dynamic graph convolution module that aggregates hidden states of neighboring nodes to focal nodes using dynamic adjacency matrices and message passing. According to their study, the proposed scheme could offer clear and interpretable spatial relationships between road segments. Furthermore, [PERSON] et al. [59] proposed a multi-step prediction model that integrates CNN with an attention mechanism. In this way, the spatial-temporal dependencies and forecast traffic conditions of road networks could be captured. With the self-adaptive node embedding, the model is capable of extracting the latent spatial relationships in the data even without prior knowledge of the graph topology. [PERSON] et al. [60] observed that spatial-temporal correlations among road networks are changeable and complex. They proposed a model to achieve a dynamic traffic flow prediction model. Their proposed model comprises an adaptive mechanism block that preprocesses the data, improves its quality, and passes it to the multi-sensor data correlation convolution block to learn the dynamic temporal and spatial correlation among roads. There are other related efforts. For example, [PERSON] et al. [23] aimed for an effective traffic jam forecasting strategy in smart cities by proposing a _\"Relative Position Congestion Tensor (RPCT)\"_ and a predictor for the _\"Position Congestion Tensor\"_. The proposed schemes leveraged the concept of relative locations to realize congestion matrices on regional traffic networks and convert them into spatial-temporal tensors. ConvLSTM was used to forecast future traffic congestion across the entire road network. Likewise, [PERSON] et al. [61] discussed the significance of accurate traffic condition predictions in intelligent transportation systems.