346 lines
13 KiB
Python
346 lines
13 KiB
Python
"""评估训练好的模型并绘制速度时空图"""
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import os
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import yaml
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.use("Agg")
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from sumo_edge_vsl_environment import SUMOEdgeVSLEnvironment
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from ppo_agent import PPOAgent
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from appo_sumo_agent import APPOSUMOAgent
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from dqn_agent import DQNAgent
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import traci
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def evaluate_model(model_type, checkpoint_dir, config):
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"""评估单个模型"""
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env = SUMOEdgeVSLEnvironment(config)
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if model_type == "PPO":
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agent = PPOAgent(
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state_dim=env.state_dim,
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action_dims=[env.action_dim] * env.num_edges,
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hidden_layers=[256, 256, 128],
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device="cuda"
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)
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agent.load(os.path.join(checkpoint_dir, "model_best.pt"))
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elif model_type == "APPO":
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agent = APPOSUMOAgent(
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state_dim=env.state_dim,
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action_dims=[env.action_dim] * env.num_edges,
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hidden_dim=128,
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device="cuda",
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total_episodes=500
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)
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agent.load(os.path.join(checkpoint_dir, "model_best.pt"))
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elif model_type == "DQN":
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agents = []
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for i in range(env.num_edges):
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agent_i = DQNAgent(
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state_dim=env.state_dim,
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num_actions=5,
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hidden_dim=256,
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device="cuda"
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)
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agent_i.load(os.path.join(checkpoint_dir, f"model_best_edge{i}.pt"))
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agents.append(agent_i)
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agent = agents
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# 收集所有车检器组位置（只包含有检测器的位置）
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detector_groups = []
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for edge_id in env.control_edges:
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edge_info = env.parser.edge_info[edge_id]
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for pos in edge_info.detector_positions:
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pos_idx = edge_info.detector_positions.index(pos)
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# 检查该位置是否有检测器
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has_detector = False
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for lane_idx in edge_info.traffic_lane_indices:
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if edge_info.detectors.get((lane_idx, pos_idx)):
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has_detector = True
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break
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if has_detector:
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detector_groups.append((edge_id, pos))
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# 运行仿真
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state = env.reset(seed=42)
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speed_data = []
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density_data = []
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action_data = []
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brake_counts = []
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speed_variances = []
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throughputs = []
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done = False
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step = 0
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while not done:
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if model_type == "DQN":
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action = np.array([ag.select_action(state) for ag in agent])
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else:
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action, _, _ = agent.select_action(state, deterministic=True)
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action_data.append(action.copy())
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state, reward, done, info = env.step(action)
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if not done:
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step_speeds = []
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step_densities = []
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for edge_id, pos in detector_groups:
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edge_info = env.parser.edge_info[edge_id]
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pos_idx = edge_info.detector_positions.index(pos)
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# 获取该位置所有车道的速度和占有率并取平均
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speeds = []
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occupancies = []
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for lane_idx in edge_info.traffic_lane_indices:
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det_id = edge_info.detectors.get((lane_idx, pos_idx))
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if det_id:
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spd = traci.inductionloop.getLastIntervalMeanSpeed(det_id)
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occ = traci.inductionloop.getLastIntervalOccupancy(det_id)
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if spd >= 0:
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speeds.append(spd * 3.6)
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if occ >= 0:
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occupancies.append(occ)
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# 如果没有检测器数据，使用edge的实时速度作为备用
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if speeds:
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avg_speed = np.mean(speeds)
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else:
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edge_speed = traci.edge.getLastStepMeanSpeed(edge_id)
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avg_speed = edge_speed * 3.6 if edge_speed >= 0 else 0
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step_speeds.append(avg_speed)
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avg_occupancy = np.mean(occupancies) if occupancies else 0
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step_densities.append(avg_occupancy)
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speed_data.append(step_speeds)
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density_data.append(step_densities)
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total_brakes = 0
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all_speeds = []
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for edge_id, edge_info in env.parser.edge_info.items():
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for det_id in edge_info.detectors.values():
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total_brakes += traci.inductionloop.getLastIntervalVehicleNumber(det_id)
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spd = traci.inductionloop.getLastIntervalMeanSpeed(det_id)
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if spd >= 0:
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all_speeds.append(spd * 3.6)
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brake_counts.append(total_brakes)
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speed_variances.append(np.var(all_speeds) if all_speeds else 0)
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throughputs.append(info["throughput"])
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step += 1
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env.close()
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return {
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"speed_data": np.array(speed_data),
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"density_data": np.array(density_data),
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"action_data": np.array(action_data),
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"brake_counts": brake_counts,
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"speed_variances": speed_variances,
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"throughputs": throughputs
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}
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def plot_results(results_dict, output_dir):
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"""绘制对比图并保存CSV"""
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os.makedirs(output_dir, exist_ok=True)
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# 保存CSV数据
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import pandas as pd
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for name, results in results_dict.items():
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speed_df = pd.DataFrame(results["speed_data"])
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speed_df.to_csv(os.path.join(output_dir, f"{name}_speed.csv"), index=False)
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density_df = pd.DataFrame(results["density_data"])
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density_df.to_csv(os.path.join(output_dir, f"{name}_density.csv"), index=False)
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action_df = pd.DataFrame(results["action_data"])
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action_df.to_csv(os.path.join(output_dir, f"{name}_action.csv"), index=False)
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metrics_df = pd.DataFrame({
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"brake_counts": results["brake_counts"],
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"speed_variances": results["speed_variances"],
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"throughputs": results["throughputs"]
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})
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metrics_df.to_csv(os.path.join(output_dir, f"{name}_metrics.csv"), index=False)
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# 速度时空图 - 2x2布局
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fig, axes = plt.subplots(2, 2, figsize=(16, 12))
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axes = axes.flatten()
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for idx, (name, results) in enumerate(results_dict.items()):
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im = axes[idx].imshow(results["speed_data"].T, aspect="auto", cmap="RdYlGn",
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vmin=0, vmax=120, origin="lower")
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axes[idx].set_title(f"{name} Speed Heatmap")
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axes[idx].set_xlabel("Time Step")
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axes[idx].set_ylabel("Detector Group Index")
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plt.colorbar(im, ax=axes[idx], label="Speed (km/h)")
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plt.tight_layout()
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plt.savefig(os.path.join(output_dir, "speed_heatmaps.png"), dpi=150)
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plt.close()
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# 密度时空图 - 2x2布局
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fig, axes = plt.subplots(2, 2, figsize=(16, 12))
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axes = axes.flatten()
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for idx, (name, results) in enumerate(results_dict.items()):
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im = axes[idx].imshow(results["density_data"].T, aspect="auto", cmap="YlOrRd",
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vmin=0, vmax=100, origin="lower")
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axes[idx].set_title(f"{name} Density Heatmap")
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axes[idx].set_xlabel("Time Step")
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axes[idx].set_ylabel("Detector Group Index")
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plt.colorbar(im, ax=axes[idx], label="Occupancy (%)")
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plt.tight_layout()
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plt.savefig(os.path.join(output_dir, "density_heatmaps.png"), dpi=150)
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plt.close()
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# 关键指标曲线
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fig, axes = plt.subplots(2, 2, figsize=(15, 10))
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for name, results in results_dict.items():
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axes[0, 0].plot(results["brake_counts"], label=name, alpha=0.7)
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axes[0, 1].plot(results["speed_variances"], label=name, alpha=0.7)
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axes[1, 0].plot(results["throughputs"], label=name, alpha=0.7)
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axes[0, 0].set_title("Brake Counts Over Time")
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axes[0, 0].set_xlabel("Time Step")
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axes[0, 0].set_ylabel("Brake Count")
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axes[0, 0].legend()
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axes[0, 0].grid(True, alpha=0.3)
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axes[0, 1].set_title("Speed Variance Over Time")
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axes[0, 1].set_xlabel("Time Step")
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axes[0, 1].set_ylabel("Variance (km/h)²")
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axes[0, 1].legend()
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axes[0, 1].grid(True, alpha=0.3)
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axes[1, 0].set_title("Throughput Over Time")
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axes[1, 0].set_xlabel("Time Step")
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axes[1, 0].set_ylabel("Throughput (veh/h)")
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axes[1, 0].legend()
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axes[1, 0].grid(True, alpha=0.3)
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# 统计摘要
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summary_text = ""
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for name, results in results_dict.items():
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summary_text += f"{name}:\n"
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summary_text += f" Avg Brake: {np.mean(results['brake_counts']):.1f}\n"
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summary_text += f" Avg Variance: {np.mean(results['speed_variances']):.2f}\n"
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summary_text += f" Avg Throughput: {np.mean(results['throughputs']):.1f}\n\n"
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axes[1, 1].text(0.1, 0.5, summary_text, fontsize=10, family="monospace",
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verticalalignment="center", transform=axes[1, 1].transAxes)
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axes[1, 1].axis("off")
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plt.tight_layout()
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plt.savefig(os.path.join(output_dir, "metrics_comparison.png"), dpi=150)
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plt.close()
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def evaluate_no_control(config):
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"""评估无管控基准"""
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env = SUMOEdgeVSLEnvironment(config)
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# 收集所有车检器组位置（只包含有检测器的位置）
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detector_groups = []
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for edge_id in env.control_edges:
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edge_info = env.parser.edge_info[edge_id]
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for pos in edge_info.detector_positions:
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pos_idx = edge_info.detector_positions.index(pos)
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has_detector = False
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for lane_idx in edge_info.traffic_lane_indices:
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if edge_info.detectors.get((lane_idx, pos_idx)):
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has_detector = True
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break
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if has_detector:
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detector_groups.append((edge_id, pos))
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state = env.reset(seed=42)
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speed_data = []
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density_data = []
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action_data = []
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brake_counts = []
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speed_variances = []
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throughputs = []
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done = False
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while not done:
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action = np.array([4] * env.num_edges)
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action_data.append(action.copy())
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state, reward, done, info = env.step(action)
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if not done:
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step_speeds = []
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step_densities = []
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for edge_id, pos in detector_groups:
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edge_info = env.parser.edge_info[edge_id]
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pos_idx = edge_info.detector_positions.index(pos)
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speeds = []
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occupancies = []
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for lane_idx in edge_info.traffic_lane_indices:
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det_id = edge_info.detectors.get((lane_idx, pos_idx))
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if det_id:
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spd = traci.inductionloop.getLastIntervalMeanSpeed(det_id)
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occ = traci.inductionloop.getLastIntervalOccupancy(det_id)
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if spd >= 0:
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speeds.append(spd * 3.6)
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if occ >= 0:
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occupancies.append(occ)
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if speeds:
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avg_speed = np.mean(speeds)
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else:
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edge_speed = traci.edge.getLastStepMeanSpeed(edge_id)
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avg_speed = edge_speed * 3.6 if edge_speed >= 0 else 0
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step_speeds.append(avg_speed)
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avg_occupancy = np.mean(occupancies) if occupancies else 0
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step_densities.append(avg_occupancy)
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speed_data.append(step_speeds)
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density_data.append(step_densities)
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total_brakes = 0
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all_speeds = []
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for edge_id, edge_info in env.parser.edge_info.items():
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for det_id in edge_info.detectors.values():
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total_brakes += traci.inductionloop.getLastIntervalVehicleNumber(det_id)
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spd = traci.inductionloop.getLastIntervalMeanSpeed(det_id)
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if spd >= 0:
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all_speeds.append(spd * 3.6)
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brake_counts.append(total_brakes)
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speed_variances.append(np.var(all_speeds) if all_speeds else 0)
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throughputs.append(info["throughput"])
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env.close()
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return {
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"speed_data": np.array(speed_data),
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"density_data": np.array(density_data),
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"action_data": np.array(action_data),
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"brake_counts": brake_counts,
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"speed_variances": speed_variances,
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"throughputs": throughputs
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}
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if __name__ == "__main__":
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with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
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config = yaml.safe_load(f)
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models = {
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"PPO": "checkpoints_sumo_vsl/final",
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"APPO": "checkpoints_sumo_appo/final",
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"DQN": "checkpoints_sumo_dqn/final"
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# "PPO": "checkpoints_sumo_vsl/20260324_202539",
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# "APPO": "checkpoints_sumo_appo/20260324_202514",
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# "DQN": "checkpoints_sumo_dqn/20260324_202548"
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}
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results = {}
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for name, ckpt in models.items():
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print(f"Evaluating {name}...")
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results[name] = evaluate_model(name, ckpt, config)
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print("Evaluating No Control...")
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results["No Control"] = evaluate_no_control(config)
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plot_results(results, "evaluation_results")
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print("Evaluation complete! Results saved to evaluation_results/")
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