## 4 Analysis of Methods for Forecasting Transportation Flow
### Based on Intelligent Algorithmic Models
In the BP networks, taking the BP neural network with a 7-3-1 structure as an example: \(X_{1},~{}X_{2},\dots\)_and_\(X_{7}\) represent seven sets of input vectors; \(Y_{1}\) represents a set of output vectors; \(W_{ij}\) is the connection weight between the input layer and the hidden layer; \(V_{ji}\) is the connection weight between the hidden layer and the output layer; \(\theta_{j}\) and \(\mu_{t}\) are the node thresholds for the hidden layer and the output layer, respectively. (Note: the figure shows schematic representations of neural network structures, which should not be interpreted as detailed designs of actual networks.) A CNN is composed of five parts, an input layer, a convolution layer, a full connection layer, a pooling layer, and an output layer. At present, the commonly used CNN structure applies an unsaturated nonlinear function as an excitation function (such as the ReLU function), which can extract and classify features independently. However, its construction needs substantial training data. Meanwhile, it is mostly used in image recognition, state monitoring, and other fields. Le-Net-5 is one of the classic CNN models [42]. For example, [PERSON] et al. obtained more than 97% accuracy when using a CNN to diagnose lung medical images [60]. On the other hand, [PERSON] et al. predicted short-term traffic congestion through a CNN and found that pooling and convolution caused the loss of target location information and other problems, reducing the prediction accuracy [61]. The network structure of an FCN and a CNN is similar, but the difference lies in the connection of adjacent nodes in the network. It converts the full connection mode of a CNN into convolution substitution, which is higher than a CNN in input efficiency, but massive and complex network parameters result in its slow calculation speed and easy overfitting. In general, an FCN is composed of multiple convolution layers. When large convolution cores are needed, too many convolution layers bring a lot of difficulties in calculation and optimization, and the complexity of calculation increases significantly. [PERSON] et al. classified the green tide concentration image and found that applying an FCN to sample the high-power image caused contour distortion and detail loss [62]. LSTM can solve the limitation of an RNN by introducing the concept of the gate control unit, which is the gradient vanishing problem. However, it cannot fully learn the problem of long-time correlation due to its time lag characteristics. Thus, it has high requirements  
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for training data sets. Meanwhile, its good robustness uses the time characteristics of data to solve regression problems, such as the interval or long delay, that the general recurrent neural network cannot process. Therefore, it is mostly used in the direction of traffic flow prediction, autonomous speech recognition, etc. [63; 64]. [PERSON] et al. used LSTM to predict the closing price of the Hong Kong Hang Seng index in time series [65]. Although it could solve the problem that the general neural network could not remember and use historical information, the lag significantly reduced its prediction effect. As a multilayer feedforward neural network, the BPNN is widely used in function approximation and time series prediction. Its core idea is to use the forward transmission of information and the reverse transmission of error, adopt the gradient descent method, and apply the gradient search technology to achieve the minimum error between the actual output value and the expected output value. The research showed that a three-layer BPNN model could accurately and comprehensively reflect the correlation between complex data. Therefore, it could approximate most complex nonlinear functions, with an excellent nonlinear mapping ability and a generalization performance. In a way, it is one of the best traffic flow prediction models [66; 67].