RNN模型预测拥堵消散时间代码实现

以下是使用PyTorch实现RNN模型预测拥堵消散时间的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义RNN模型
class RNN(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(RNN, self).__init__()
        self.hidden_dim = hidden_dim
        self.rnn = nn.RNN(input_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h0 = torch.zeros(1, x.size(0), self.hidden_dim).to(device)
        out, _ = self.rnn(x, h0)
        out = self.fc(out[:, -1, :])
        return out

# 准备数据
data = np.loadtxt('traffic_data.csv', delimiter=',')
x_train = torch.tensor(data[:, :-1], dtype=torch.float32)
y_train = torch.tensor(data[:, -1:], dtype=torch.float32)

# 定义模型超参数
input_dim = x_train.shape[1]
hidden_dim = 32
output_dim = 1
learning_rate = 0.01
num_epochs = 1000

# 初始化模型和优化器
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RNN(input_dim, hidden_dim, output_dim).to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    optimizer.zero_grad()
    outputs = model(x_train.to(device))
    loss = criterion(outputs, y_train.to(device))
    loss.backward()
    optimizer.step()
    if (epoch+1) % 100 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 预测结果
with torch.no_grad():
    x_test = torch.tensor([[1.2, 3.4, 5.6], [2.3, 4.5, 6.7]], dtype=torch.float32)
    y_pred = model(x_test.to(device))
    print(y_pred.cpu().numpy())

其中，数据文件`traffic_data.csv`包含了历史拥堵数据和相应的拥堵消散时间。模型使用一个单层RNN和一个全连接层进行建模，并使用均方误差作为损失函数进行训练。训练完成后，可以使用训练好的模型进行拥堵消散时间的预测。