💖💖作者：计算机毕业设计小途 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
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基于Python的交通数据分析应用系统介绍
基于Python的交通数据分析应用是一套采用B/S架构设计的综合性数据分析平台，该系统支持Java+SpringBoot和Python+Django两种技术实现方案，前端采用Vue框架结合ElementUI组件库构建现代化用户界面，后端通过MySQL数据库进行数据存储和管理。系统核心功能涵盖交通数据的采集、存储、分析和可视化展示，用户可以通过系统首页快速了解平台概况，通过用户管理模块实现多角色权限控制，交通数据模块负责各类交通信息的录入和维护，交通预测功能基于历史数据运用算法模型对未来交通状况进行智能预测分析。系统最大亮点是大屏可视化分析功能，能够将复杂的交通数据以图表、地图等直观形式展现，为交通管理决策提供有力支撑。同时系统还提供个人信息管理、密码修改、系统公告发布、轮播图管理等辅助功能，确保平台的完整性和实用性。整个系统采用前后端分离的开发模式，支持IDEA和PyCharm开发环境，具有良好的扩展性和维护性，能够满足交通行业对数据分析和可视化展示的实际需求，为用户提供专业、高效的交通数据分析解决方案。
基于Python的交通数据分析应用系统演示视频
基于Python的交通数据分析应用系统演示图片
基于Python的交通数据分析应用系统代码展示
spark = SparkSession.builder.appName("TrafficDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_traffic_data(request):
traffic_data = TrafficData.objects.all().values()
df = spark.createDataFrame(list(traffic_data))
df.createOrReplaceTempView("traffic_table")
hourly_stats = spark.sql("SELECT HOUR(record_time) as hour, AVG(vehicle_count) as avg_count, MAX(vehicle_count) as max_count FROM traffic_table GROUP BY HOUR(record_time) ORDER BY hour")
peak_hours = hourly_stats.filter(hourly_stats.avg_count > hourly_stats.agg({"avg_count": "avg"}).collect()[0][0])
congestion_analysis = spark.sql("SELECT location, COUNT(*) as congestion_frequency FROM traffic_table WHERE vehicle_count > 100 GROUP BY location ORDER BY congestion_frequency DESC")
speed_analysis = df.groupBy("road_type").agg({"average_speed": "mean", "vehicle_count": "sum"}).orderBy("road_type")
trend_data = spark.sql("SELECT DATE(record_time) as date, SUM(vehicle_count) as daily_total, AVG(average_speed) as daily_avg_speed FROM traffic_table GROUP BY DATE(record_time) ORDER BY date DESC LIMIT 30")
result_data = {"hourly_stats": [row.asDict() for row in hourly_stats.collect()], "peak_hours": [row.asDict() for row in peak_hours.collect()], "congestion_hotspots": [row.asDict() for row in congestion_analysis.collect()], "speed_analysis": [row.asDict() for row in speed_analysis.collect()], "trend_data": [row.asDict() for row in trend_data.collect()]}
correlation_df = df.select("vehicle_count", "average_speed", "weather_condition")
correlation_matrix = correlation_df.stat.corr("vehicle_count", "average_speed")
weather_impact = spark.sql("SELECT weather_condition, AVG(vehicle_count) as avg_vehicles, AVG(average_speed) as avg_speed FROM traffic_table GROUP BY weather_condition")
result_data["correlation"] = correlation_matrix
result_data["weather_impact"] = [row.asDict() for row in weather_impact.collect()]
return JsonResponse({"status": "success", "data": result_data})
def predict_traffic_flow(request):
historical_data = TrafficData.objects.filter(record_time__gte=timezone.now() - timedelta(days=90)).values()
df = spark.createDataFrame(list(historical_data))
df.createOrReplaceTempView("historical_traffic")
hourly_patterns = spark.sql("SELECT HOUR(record_time) as hour, DAYOFWEEK(record_time) as weekday, AVG(vehicle_count) as avg_count, STDDEV(vehicle_count) as std_count FROM historical_traffic GROUP BY HOUR(record_time), DAYOFWEEK(record_time)")
seasonal_patterns = spark.sql("SELECT MONTH(record_time) as month, AVG(vehicle_count) as monthly_avg FROM historical_traffic GROUP BY MONTH(record_time) ORDER BY month")
location_patterns = spark.sql("SELECT location, HOUR(record_time) as hour, AVG(vehicle_count) as location_avg FROM historical_traffic GROUP BY location, HOUR(record_time)")
current_hour = timezone.now().hour
current_weekday = timezone.now().weekday() + 1
current_month = timezone.now().month
base_prediction = hourly_patterns.filter((hourly_patterns.hour == current_hour) & (hourly_patterns.weekday == current_weekday)).select("avg_count").collect()
seasonal_factor = seasonal_patterns.filter(seasonal_patterns.month == current_month).select("monthly_avg").collect()
if base_prediction and seasonal_factor:
predicted_count = base_prediction[0]["avg_count"] * (seasonal_factor[0]["monthly_avg"] / seasonal_patterns.agg({"monthly_avg": "avg"}).collect()[0][0])
else:
predicted_count = df.agg({"vehicle_count": "avg"}).collect()[0][0]
location_predictions = []
for location_row in location_patterns.filter(location_patterns.hour == current_hour).collect():
location_pred = {"location": location_row["location"], "predicted_count": int(location_row["location_avg"]), "confidence": min(0.95, max(0.6, 1 - (location_row["location_avg"] * 0.1 / predicted_count)))}
location_predictions.append(location_pred)
congestion_risk = "高" if predicted_count > df.agg({"vehicle_count": "avg"}).collect()[0][0] * 1.5 else ("中" if predicted_count > df.agg({"vehicle_count": "avg"}).collect()[0][0] else "低")
prediction_result = {"predicted_traffic_count": int(predicted_count), "prediction_time": timezone.now().strftime("%Y-%m-%d %H:%M:%S"), "congestion_risk": congestion_risk, "location_predictions": location_predictions, "confidence_score": 0.85}
return JsonResponse({"status": "success", "prediction": prediction_result})
def generate_visualization_data(request):
traffic_data = TrafficData.objects.all().values()
df = spark.createDataFrame(list(traffic_data))
df.createOrReplaceTempView("traffic_viz")
heatmap_data = spark.sql("SELECT location, HOUR(record_time) as hour, AVG(vehicle_count) as intensity FROM traffic_viz GROUP BY location, HOUR(record_time)").collect()
flow_direction_data = spark.sql("SELECT direction, COUNT(*) as count, AVG(vehicle_count) as avg_flow FROM traffic_viz GROUP BY direction").collect()
time_series_data = spark.sql("SELECT DATE_FORMAT(record_time, 'yyyy-MM-dd HH:00:00') as time_slot, SUM(vehicle_count) as total_count FROM traffic_viz GROUP BY DATE_FORMAT(record_time, 'yyyy-MM-dd HH:00:00') ORDER BY time_slot").collect()
speed_distribution = spark.sql("SELECT CASE WHEN average_speed < 30 THEN '低速' WHEN average_speed < 60 THEN '中速' ELSE '高速' END as speed_range, COUNT(*) as count FROM traffic_viz GROUP BY CASE WHEN average_speed < 30 THEN '低速' WHEN average_speed < 60 THEN '中速' ELSE '高速' END").collect()
geographic_data = spark.sql("SELECT location, latitude, longitude, AVG(vehicle_count) as avg_traffic, MAX(vehicle_count) as peak_traffic FROM traffic_viz WHERE latitude IS NOT NULL AND longitude IS NOT NULL GROUP BY location, latitude, longitude").collect()
congestion_timeline = spark.sql("SELECT DATE(record_time) as date, COUNT(CASE WHEN vehicle_count > 80 THEN 1 END) as congestion_incidents FROM traffic_viz GROUP BY DATE(record_time) ORDER BY date DESC LIMIT 14").collect()
radar_chart_data = spark.sql("SELECT location, AVG(vehicle_count) as traffic_volume, AVG(average_speed) as speed, COUNT(*) as frequency, AVG(CASE WHEN weather_condition = 'sunny' THEN 100 ELSE 50 END) as weather_score FROM traffic_viz GROUP BY location LIMIT 10").collect()
visualization_result = {"heatmap": [{"location": row["location"], "hour": row["hour"], "intensity": float(row["intensity"])} for row in heatmap_data], "flow_direction": [{"direction": row["direction"], "count": row["count"], "avg_flow": float(row["avg_flow"])} for row in flow_direction_data], "time_series": [{"time": row["time_slot"], "value": row["total_count"]} for row in time_series_data], "speed_distribution": [{"range": row["speed_range"], "count": row["count"]} for row in speed_distribution], "geographic": [{"location": row["location"], "lat": float(row["latitude"]), "lng": float(row["longitude"]), "avg_traffic": float(row["avg_traffic"]), "peak_traffic": row["peak_traffic"]} for row in geographic_data], "congestion_timeline": [{"date": str(row["date"]), "incidents": row["congestion_incidents"]} for row in congestion_timeline], "radar_data": [{"location": row["location"], "traffic_volume": float(row["traffic_volume"]), "speed": float(row["speed"]), "frequency": row["frequency"], "weather_score": float(row["weather_score"])} for row in radar_chart_data]}
return JsonResponse({"status": "success", "visualization": visualization_result})
基于Python的交通数据分析应用系统文档展示
💖💖作者：计算机毕业设计小途 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
