打车申请的等待时间计算

import pandas as pd
from sklearn.linear_model import LinearRegression

# 假设我们有一些历史数据
data = {
    'request_time': [...],  # 请求时间
    'driver_response_time': [...],  # 司机响应时间
    'distance': [...],  # 距离
    'traffic_condition': [...],  # 交通状况
    'actual_wait_time': [...]  # 实际等待时间
}

df = pd.DataFrame(data)

# 特征工程
df['hour'] = pd.to_datetime(df['request_time']).dt.hour
df['day_of_week'] = pd.to_datetime(df['request_time']).dt.dayofweek

# 训练模型
X = df[['hour', 'day_of_week', 'driver_response_time', 'distance', 'traffic_condition']]
y = df['actual_wait_time']

model = LinearRegression()
model.fit(X, y)

# 预测等待时间
def predict_wait_time(request_time, driver_response_time, distance, traffic_condition):
    hour = pd.to_datetime(request_time).hour
    day_of_week = pd.to_datetime(request_time).dayofweek
    features = [[hour, day_of_week, driver_response_time, distance, traffic_condition]]
    return model.predict(features)[0]

# 示例调用
predicted_wait_time = predict_wait_time('2023-10-05T18:00:00', 2, 5.5, 0.8)
print(f"Predicted Wait Time: {predicted_wait_time} minutes")