请给出文献中建立双层路网模型的可运行python代码

根据文献描述，双层路网模型涉及多个步骤和技术，包括路网拓扑处理、路网事件关联模型生成和行程时间预测。以下是一个简化版的Python代码示例，展示了如何构建双层路网模型的核心部分。请注意，这只是一个示例，实际应用中可能需要更多的细节和完善。

### 1. 路网拓扑处理

import pandas as pd
import numpy as np
from shapely.geometry import LineString

class RoadNetwork:
    def __init__(self, road_data):
        self.road_data = road_data
        self.adj_matrix = None
        self.road_attributes = {}

    def add_matrix(self):
        # 创建邻接矩阵
        unique_roads = self.road_data['road_id'].unique()
        num_roads = len(unique_roads)
        self.adj_matrix = np.zeros((num_roads, num_roads))

        for index, row in self.road_data.iterrows():
            start_node = row['start_node']
            end_node = row['end_node']
            road_id = row['road_id']
            weight = row['weight']  # 可以是长度或其他权重

            if start_node in unique_roads and end_node in unique_roads:
                i = np.where(unique_roads == start_node)[0][0]
                j = np.where(unique_roads == end_node)[0][0]
                self.adj_matrix[i, j] = weight
                self.adj_matrix[j, i] = weight

    def add_road_attribute(self):
        # 添加道路属性
        for index, row in self.road_data.iterrows():
            road_id = row['road_id']
            geometry = LineString([(row['start_x'], row['start_y']), (row['end_x'], row['end_y'])])
            attributes = {
                'length': row['length'],
                'speed_limit': row['speed_limit'],
                'geometry': geometry
            }
            self.road_attributes[road_id] = attributes

# 示例数据
data = {
    'road_id': [1, 2, 3],
    'start_node': ['A', 'B', 'C'],
    'end_node': ['B', 'C', 'D'],
    'start_x': [0, 1, 2],
    'start_y': [0, 1, 2],
    'end_x': [1, 2, 3],
    'end_y': [1, 2, 3],
    'length': [1.0, 1.0, 1.0],
    'speed_limit': [60, 60, 60],
    'weight': [1.0, 1.0, 1.0]
}

df = pd.DataFrame(data)
road_network = RoadNetwork(df)
road_network.add_matrix()
road_network.add_road_attribute()

print("邻接矩阵:")
print(road_network.adj_matrix)
print("\n道路属性:")
for road_id, attributes in road_network.road_attributes.items():
    print(f"Road ID: {road_id}, Attributes: {attributes}")

### 2. 路网事件关联模型生成

import geopandas as gpd
from shapely.geometry import Point

def generate_event_model(road_network, event_data):
    # 将事件数据与路网数据关联
    event_gdf = gpd.GeoDataFrame(event_data, geometry=event_data.apply(lambda x: Point(x['lon'], x['lat']), axis=1))
    road_gdf = gpd.GeoDataFrame(road_network.road_attributes.values(), columns=['length', 'speed_limit', 'geometry'])

    # 使用sjoin将事件点与最近的道路关联
    event_gdf = gpd.sjoin(event_gdf, road_gdf, how="inner", op='intersects')

    return event_gdf

# 示例事件数据
event_data = {
    'event_id': [1, 2],
    'event_type': ['accident', 'closure'],
    'lon': [0.5, 2.5],
    'lat': [0.5, 2.5]
}

event_df = pd.DataFrame(event_data)
event_model = generate_event_model(road_network, event_df)

print("\n事件关联模型:")
print(event_model)

### 3. 行程时间预测

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, InputLayer
from sklearn.preprocessing import MinMaxScaler

def load_traffic_data(file_path):
    data = pd.read_csv(file_path)
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaled_data = scaler.fit_transform(data[['travel_time']])
    return scaled_data, scaler

def create_lstm_model(input_shape):
    model = Sequential()
    model.add(InputLayer(input_shape=input_shape))
    model.add(LSTM(50, return_sequences=True))
    model.add(LSTM(50))
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mean_squared_error')
    return model

def predict_travel_time(model, input_data, scaler):
    predictions = model.predict(input_data)
    predictions = scaler.inverse_transform(predictions)
    return predictions

# 示例交通数据文件路径
traffic_file_path = 'traffic_data.csv'
scaled_data, scaler = load_traffic_data(traffic_file_path)

# 训练LSTM模型
input_shape = (10, 1)  # 假设我们使用过去10个时间步长的数据
model = create_lstm_model(input_shape)
model.fit(scaled_data[:-1], scaled_data[1:], epochs=10, batch_size=1)

# 预测未来的行程时间
future_input = scaled_data[-10:]
predictions = predict_travel_time(model, future_input.reshape(1, *input_shape), scaler)

print("\n预测的行程时间:")
print(predictions)

### 4. 结合双层路网模型

# 将预测的行程时间添加到路网事件关联模型中
event_model['predicted_travel_time'] = predictions.flatten()

print("\n更新后的事件关联模型:")
print(event_model)

这个示例代码展示了如何构建一个简单的双层路网模型，包括路网拓扑处理、事件关联模型生成和行程时间预测。实际应用中，您可能需要根据具体需求调整数据格式和模型参数。