import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import poisson

# 读取视频数据并计算交通流参数
def read_video_data(video_file):
    cap = cv2.VideoCapture(video_file)
    vehicle_count = []
    frame_count = 0

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        frame_count += 1
        # 假设每帧处理逻辑已经实现，车辆计数放入vehicle_count中
        # vehicle_count.append(process_frame(frame))  # 需要实现process_frame方法
        
    cap.release()
    return vehicle_count

# 计算流量、密度和速度
def calculate_traffic_parameters(vehicle_counts, road_length, delta_t):
    flow = [count / delta_t for count in vehicle_counts]
    density = [count / road_length for count in vehicle_counts]
    speed = [flow[i] / density[i] if density[i] > 0 else 0 for i in range(len(flow))]
    return flow, density, speed

# 拥堵预警模型
def congestion_warning(density, threshold, duration):
    alert_times = []
    for i in range(len(density)):
        if density[i] > threshold:
            if all(density[j] > threshold for j in range(i, min(i + duration, len(density)))):
                alert_times.append(i)
    return alert_times

# M/G/1排队模型计算
def mg1_queue_model(arrival_rate, service_rate):
    rho = arrival_rate / service_rate
    Lq = (arrival_rate**2) / (2 * service_rate * (1 - rho))
    Wq = Lq / arrival_rate
    return Lq, Wq

# 主函数
def main(video_files, road_length, delta_t, congestion_threshold, warning_duration):
    all_vehicle_counts = []
    
    for video_file in video_files:
        vehicle_counts = read_video_data(video_file)
        all_vehicle_counts.append(vehicle_counts)

    flow, density, speed = zip(*[calculate_traffic_parameters(counts, road_length, delta_t) for counts in all_vehicle_counts])
    
    for d in density:
        alerts = congestion_warning(d, congestion_threshold, warning_duration)
        print(f"Alerts for density: {alerts}")

    # 示例参数
    arrival_rate = np.mean(flow)
    service_rate = 1.0  # 假设的服务率
    Lq, Wq = mg1_queue_model(arrival_rate, service_rate)
    
    print(f"Average queue length (Lq): {Lq}, Average waiting time (Wq): {Wq}")
    
    # 绘制流量、密度和速度变化图
    plt.figure(figsize=(15, 5))
    plt.subplot(1, 3, 1)
    plt.plot(flow)
    plt.title('Traffic Flow')
    plt.subplot(1, 3, 2)
    plt.plot(density)
    plt.title('Traffic Density')
    plt.subplot(1, 3, 3)
    plt.plot(speed)
    plt.title('Traffic Speed')
    plt.tight_layout()
    plt.show()

# 设置参数并运行主函数
if __name__ == "__main__":
    video_files = ["video1.mp4", "video2.mp4", "video3.mp4", "video4.mp4"]
    road_length = 5000  # 路段长度
    delta_t = 1  # 时间间隔
    congestion_threshold = 0.8  # 拥堵阈值
    warning_duration = 10  # 持续时间
    main(video_files, road_length, delta_t, congestion_threshold, warning_duration)

import numpy as np
import pandas as pd
from scipy.optimize import linprog
import matplotlib.pyplot as plt

# 假设数据
def generate_traffic_data(num_time_slots):
    np.random.seed(0)
    flow = np.random.randint(50, 150, size=num_time_slots)
    density = np.random.uniform(0.1, 0.7, size=num_time_slots)
    return flow, density

# 参数设置
road_length = 5000  # 路段长度（米）
delta_t = 1  # 时间间隔（分钟）
capacity = 100  # 道路通行能力（辆/分钟）
alpha = 0.8  # 启用应急车道的阈值系数
avg_delay_time = 5  # 平均通过时间（分钟）

# 延迟计算
def calculate_delay(flow):
    delay = np.maximum(0, (flow / capacity - 1) * avg_delay_time)
    return delay

# 优化模型
def emergency_lane_decision(flow, density):
    num_time_slots = len(flow)
    total_delay = calculate_delay(flow)

    c = total_delay
    A_ub = np.zeros((num_time_slots, num_time_slots))
    b_ub = np.zeros(num_time_slots)

    for i in range(num_time_slots):
        A_ub[i, i] = 1
        if flow[i] >= alpha * capacity:
            b_ub[i] = 1  # 启用应急车道的约束条件

    bounds = [(0, 1) for _ in range(num_time_slots)]
    
    result = linprog(c, A_ub=A_ub, b_ub=b_ub, bounds=bounds, method='highs')
    
    return result

# 主函数
def main():
    num_time_slots = 60  # 假设有60个时间段
    flow, density = generate_traffic_data(num_time_slots)
    
    result = emergency_lane_decision(flow, density)

    print("Optimization Result:")
    if result.success:
        print("Optimal Decision Variables (x):")
        print(result.x)
        print("Total Delay Reduction:", sum(result.x * calculate_delay(flow)))
    else:
        print("Optimization failed.")

    # 可视化
    plt.figure(figsize=(12, 6))
    plt.plot(flow, label='Traffic Flow', color='blue')
    plt.plot(density * capacity, label='Density Capacity', color='orange', linestyle='--')
    plt.axhline(y=alpha * capacity, color='red', linestyle='--', label='Emergency Lane Threshold')
    plt.title('Traffic Flow and Density')
    plt.xlabel('Time Slots')
    plt.ylabel('Vehicles')
    plt.legend()
    plt.show()

if __name__ == "__main__":
    main()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 假设数据生成函数
def generate_traffic_data(num_time_slots):
    np.random.seed(0)
    flow = np.random.randint(50, 150, size=num_time_slots)
    density = np.random.uniform(0.1, 0.7, size=num_time_slots)
    return flow, density

# 参数设置
road_length = 5000
capacity = 100
avg_delay_time = 5
K_threshold = 0.5
theta = 0.8

# 延迟计算
def calculate_delay(flow):
    delay = np.maximum(0, (flow / capacity - 1) * avg_delay_time)
    return delay

# 决策函数
def emergency_lane_decision(flow, density):
    num_time_slots = len(flow)
    decisions = np.zeros(num_time_slots)

    for t in range(num_time_slots):
        if density[t] > K_threshold and flow[t] >= theta * capacity:
            decisions[t] = 1

    return decisions

# 主函数
def main():
    num_time_slots = 60
    flow, density = generate_traffic_data(num_time_slots)

    decisions = emergency_lane_decision(flow, density)
    delays = calculate_delay(flow)

    print("Flow Data:", flow)
    print("Density Data:", density)
    print("Decisions (1:启用应急车道, 0:不启用):", decisions)
    print("Delays:", delays)

    plt.figure(figsize=(12, 6))
    plt.plot(flow, label='Traffic Flow', color='blue')
    plt.plot(density * capacity, label='Density Capacity', color='orange', linestyle='--')
    plt.axhline(y=K_threshold * capacity, color='red', linestyle='--', label='Density Threshold')
    plt.title('Traffic Flow and Density with Emergency Lane Decisions')
    plt.xlabel('Time Slots')
    plt.ylabel('Vehicles')
    plt.legend()
    plt.show()

if __name__ == "__main__":
    main()