基于lstm的车辆轨迹预测代码

以下是基于 LSTM 的车辆轨迹预测代码示例：

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM

# 读取数据
data = pd.read_csv('vehicle_trajectory.csv')

# 数据预处理
data['time'] = pd.to_datetime(data['time'], format='%Y-%m-%d %H:%M:%S')
data['time'] = data['time'].dt.hour * 60 + data['time'].dt.minute
x = data[['time', 'lon', 'lat']].values
y = data['speed'].values

# 划分训练集和测试集
split_ratio = 0.8
split_index = int(len(x) * split_ratio)
x_train, y_train = x[:split_index], y[:split_index]
x_test, y_test = x[split_index:], y[split_index:]

# 构建 LSTM 模型
model = Sequential()
model.add(LSTM(32, input_shape=(None, 3)))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='adam')

# 训练模型
x_train = np.reshape(x_train, (x_train.shape[0], 1, x_train.shape[1]))
model.fit(x_train, y_train, epochs=50, batch_size=32)

# 预测结果
x_test = np.reshape(x_test, (x_test.shape[0], 1, x_test.shape[1]))
y_pred = model.predict(x_test)

# 评估模型
score = model.evaluate(x_test, y_test, batch_size=32)

print('Test loss:', score)

其中，数据集 `vehicle_trajectory.csv` 包含车辆轨迹数据，包括时间、经度、纬度和速度。代码将时间转换为分钟数，并将经度、纬度和时间作为输入，速度作为输出。使用 LSTM 模型来进行预测，最后输出测试集的损失值。