基于python的智能交通流量分析与预测系统

题目：智能交通流量分析与预测系统
需求分析
一.功能需求
实时交通流量监控
交通拥堵预测
最优路径推荐
交通事故检测
交通数据可视化大屏

二.非功能需求
实时数据更新（<5秒延迟）
支持百万级数据处理
预测准确率>80%
7×24小时稳定运行

三.系统设计
架构设计
数据源：摄像头、地磁传感器、GPS数据
流处理：Apache Kafka + Spark Streaming
批处理：Hadoop + Spark
预测模型：LSTM神经网络
可视化：D3.js + WebGL

四.系统流程
数据采集 → 数据清洗 → 实时处理 → 模型预测 → 结果展示
       ↓
   数据存储 → 离线分析 → 模型训练

     以下实现一个完整的"基于Python的智能交通流量分析与预测系统"。这个系统包含所有需求功能，并可以直接运行。

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from datetime import datetime, timedelta import time import threading import queue import warnings warnings.filterwarnings('ignore') # 设置中文显示 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False print("="*60) print("智能交通流量分析与预测系统 v1.0") print("="*60) # 1. 数据模拟生成模块 class TrafficDataGenerator: """模拟交通数据生成器""" def __init__(self, num_roads=10, num_cameras=50): self.num_roads = num_roads self.num_cameras = num_cameras self.roads = [f"道路{i+1}" for i in range(num_roads)] self.cameras = [f"摄像头{i+1}" for i in range(num_cameras)] self.sensors = [f"地磁传感器{i+1}" for i in range(30)] def generate_realtime_data(self, timestamp): """生成实时交通数据""" data = [] base_hour = timestamp.hour for road in self.roads: # 模拟早晚高峰 if 7 <= base_hour <= 9 or 17 <= base_hour <= 19: base_flow = np.random.randint(800, 1200) speed = np.random.uniform(20, 40) elif 10 <= base_hour <= 16: base_flow = np.random.randint(500, 800) speed = np.random.uniform(40, 60) else: base_flow = np.random.randint(100, 300) speed = np.random.uniform(60, 80) # 添加随机波动 flow = int(base_flow * np.random.uniform(0.8, 1.2)) # 计算拥堵指数 (0-1, 越高越拥堵) congestion = 1 - (speed / 80) data.append({ 'timestamp': timestamp, 'road': road, 'flow': flow, 'speed': round(speed, 1), 'congestion': round(congestion, 2), 'accident': np.random.random() < 0.02 # 2%概率发生事故 }) return pd.DataFrame(data) def generate_historical_data(self, days=30): """生成历史数据""" all_data = [] end_time = datetime.now() start_time = end_time - timedelta(days=days) current = start_time while current <= end_time: # 模拟每天的数据模式 for hour in range(24): timestamp = current.replace(hour=hour, minute=0, second=0) for road in self.roads: # 周末交通模式不同 if current.weekday() >= 5: # 周末 if 10 <= hour <= 22: base_flow = np.random.randint(600, 1000) else: base_flow = np.random.randint(100, 300) else: # 工作日 if 7 <= hour <= 9: base_flow = np.random.randint(800, 1200) elif 17 <= hour <= 19: base_flow = np.random.randint(700, 1100) elif 10 <= hour <= 16: base_flow = np.random.randint(500, 800) else: base_flow = np.random.randint(100, 300) flow = int(base_flow * np.random.uniform(0.9, 1.1)) all_data.append({ 'timestamp': timestamp, 'road': road, 'flow': flow, 'day_of_week': current.weekday(), 'hour': hour, 'is_weekend': 1 if current.weekday() >= 5 else 0 }) current += timedelta(days=1)