AI赋能智能交通：人工智能怎样改善交通流量管理和道路安全？ - ToB企服应用市场:ToB评测及商务社交产业平台

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
# 加载数据
data = pd.read_csv('data.csv')
# 假设数据集中有一列名为'traffic_flow'代表交通流量，并且按时间顺序排列
# 此外，假设还有时间戳列，虽然在这个简化的demo中我们不会使用它
# 数据预处理：标准化
scaler = MinMaxScaler(feature_range=(0, 1))
data['traffic_flow'] = scaler.fit_transform(data['traffic_flow'].values.reshape(-1, 1))
# 创建训练集和测试集
train_size = int(len(data) * 0.7)
test_size = len(data) - train_size
train, test = data[0:train_size], data[train_size:len(data)]
# 构建序列数据
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
X.append(a)
Y.append(dataset[i + look_back, 0])
return np.array(X), np.array(Y)
look_back = 3 # 使用过去3个时间步长来预测下一个时间步长的流量
X_train, y_train = create_dataset(train['traffic_flow'].values, look_back)
X_test, y_test = create_dataset(test['traffic_flow'].values, look_back)
# 重塑输入为[samples, time steps, features]
X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
# 创建并编译LSTM模型
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer=Adam())
# 训练模型
model.fit(X_train, y_train, epochs=100, batch_size=1, verbose=2)
# 预测
trainPredict = model.predict(X_train)
testPredict = model.predict(X_test)
# 将预测值转换回原始比例
trainPredict = scaler.inverse_transform(trainPredict)
y_train = scaler.inverse_transform([y_train])
testPredict = scaler.inverse_transform(testPredict)
y_test = scaler.inverse_transform([y_test])
# 绘制训练和测试预测结果
plt.plot(scaler.inverse_transform(train['traffic_flow'].values), color='blue', label='True Data')
plt.plot(trainPredict, color='green', label='Prediction')
plt.title('Traffic Flow Prediction')
plt.xlabel('Time')
plt.ylabel('Traffic Flow')
plt.legend()
plt.show()
# 测试集结果
plt.plot(scaler.inverse_transform(test['traffic_flow'].values[look_back:]), color='blue', label='True Data')
plt.plot(testPredict, color='red', label='Prediction')
plt.title('Traffic Flow Prediction')
plt.xlabel('Time')
plt.ylabel('Traffic Flow')
plt.legend()
plt.show()

复制代码