# A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency
## 2 Related Work
### Traffic Prediction Based on Spatio-Temporal Neural Networks  
The spatio-temporal neural networks have been widely applied in the traffic speed, traffic flow, travel demands, and traffic map predictions. Ref. [26] proposed an innovative method for predicting a dynamic OD matrix in a subway network using long short-term memory (LSTM) by analyzing the swipe data of subway passengers. This method cannot be directly applied to road network OD prediction due to the relatively fixed subway lines and the small scale of the subway network. Ref. [29] investigated the spatio-temporal dependence of ODs and proposed a multi-perspective graph convolutional network. The temporal features and spatial dependencies were extracted for each OD pair using LSTM and a two-dimensional graph convolutional network. Most taxi OD prediction methods only considered the demands at origin ignoring the destination the taxi arrived at. Ref. [30] proposed a contextualized spatio-temporal network to predict the future interactive demands between all regional ODs. Ref. [31] adopted tensor data to characterize OD streams and proposed a multi-scale convolutional LSTM to handle high-dimensional travel features by tensor permutation and matrixing. To alleviate OD data sparsity, Ref. [32] used a grid partition method dividing the road network to explore the correlation of ODs. This model combines geographic and semantic neighborhoods, with the geographic domain measuring the intrinsic closeness of grids and the semantic domain modeling the intensity of traffic between grids. Ref. [33] proposed a spatio-temporal residual neural network to predict the inflow and outflow of ODs in each region based on temporal closeness, period, and trend features. The ST-ResNet aggregates the outputs of three residual networks and allocates different weights to different branches and regions, further combining the weather factor to predict the regional traffic. The time-varying traffic flow of road sections is another perspective that reveals the evolution of traffic network, and real-time traffic flow prediction is the core issue in ITS. In previous traffic flow prediction, time series models were used to capture the autocorrelation between traffic statuses [34; 35]. Subsequently, graph topological features were introduced to fit the spatial dependence of traffic flow [36]. Considering the complicated topological connectivity and nonlinear temporal dynamics of traffic context, [PERSON]. [37] proposed a diffusion convolutional recurrent neural network. It captures the spatial correlation using bidirectional random walks on the graph and temporal dependence using the code-and-decode architecture. To improve the generality of convolution, [PERSON]. [38] proposed a spatio-temporal graph convolutional network that simultaneously extracts spatio-temporal features from time-series graphs. It models the spatial features of the traffic network using general graph and adopts fully convolutional layers on the time axis. [PERSON]. [39] extended the existing graph convolutional networks and proposed a novel graph-based neural network that distinguishes the intensity of connections to adjacent nodes revealing the hidden features of traffic propagation. To cope with the constraints of topology and the dynamic changes in traffic, [PERSON]. [40] proposed a temporal graph convolutional network, which learns the complex topology to capture the spatial dependence, and extracts the dynamic temporal dependence of traffic. In the temporal dimension, the status feature in the distant past is not necessarily less important than the recent feature. Ref. [41] proposed an attentional temporal graph convolutional network that captures both global temporal dynamics and spatial correlation, which introduces an attentional mechanism to adjust the importance of different time series. Most of the existing graph convolution methods only consider local spatial correlations, but non-local associations are equally important. To obtain the non-local spatio-temporal features, Ref.