AI在智能交通系统中的优化与调度研究

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# 模拟交通流量数据
np.random.seed(0)
data = np.sin(np.linspace(0, 50, 500)) + np.random.normal(0, 0.1, 500)
data = data.reshape(-1, 1)

# 数据归一化
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)

# 构建训练样本
def create_dataset(data, time_step=10):
    X, y = [], []
    for i in range(len(data)-time_step):
        X.append(data[i:(i+time_step), 0])
        y.append(data[i+time_step, 0])
    return np.array(X), np.array(y)

time_step = 10
X, y = create_dataset(data_scaled, time_step)
X = X.reshape(X.shape[0], X.shape[1], 1)

# 构建LSTM模型
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(time_step,1)))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

# 模型训练
model.fit(X, y, epochs=20, batch_size=16, verbose=1)

import gym
import numpy as np
from stable_baselines3 import PPO

# 创建一个简单的交通信号模拟环境（假设已实现）
env = gym.make('TrafficSignal-v0')

# 使用PPO进行训练
model = PPO('MlpPolicy', env, verbose=1)
model.learn(total_timesteps=10000)

# 测试优化效果
obs = env.reset()
for _ in range(50):
    action, _states = model.predict(obs)
    obs, reward, done, info = env.step(action)
    env.render()

import networkx as nx

# 构建交通网络图
G = nx.grid_2d_graph(5,5)
# 添加路段权重（模拟交通流量）
for (u,v) in G.edges():
    G.edges[u,v]['weight'] = np.random.randint(1,10)

# 最短路径计算
start = (0,0)
end = (4,4)
path = nx.shortest_path(G, source=start, target=end, weight='weight')
print("推荐最优路径:", path)