💖💖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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基于大数据的智能出行交通数据可视化分析系统介绍
基于大数据的智能出行交通数据可视化分析系统是一套集数据采集、存储、处理、分析与可视化展示于一体的综合性交通数据分析平台，系统采用先进的大数据技术架构，底层基于Hadoop分布式文件系统HDFS进行海量交通数据的存储管理，通过Spark大数据计算引擎实现对交通数据的高效处理和分析，利用Spark SQL进行复杂的数据查询和统计分析操作。系统支持Python和Java双语言开发模式，后端分别采用Django框架和Spring Boot框架构建RESTful API接口，结合Pandas、NumPy等数据科学库进行数据预处理和统计计算，前端基于Vue.js框架搭建响应式用户界面，集成ElementUI组件库提供美观易用的交互体验，运用Echarts图表库实现丰富的数据可视化效果，配合HTML、CSS、JavaScript、jQuery等前端技术构建完整的用户交互体验。系统核心功能模块包括系统首页展示、用户信息管理、密码修改、个人信息维护等基础功能，更重要的是提供了大屏可视化展示、系统管理、交通流量分析、停车共享分析、绿色出行分析、交通安全分析等六大核心业务模块，通过对不同维度交通数据的深度挖掘和智能分析，为城市交通管理部门提供科学决策支持，为市民出行提供智能化服务，实现交通数据的价值最大化利用。
基于大数据的智能出行交通数据可视化分析系统演示视频
基于大数据的智能出行交通数据可视化分析系统演示图片
基于大数据的智能出行交通数据可视化分析系统代码展示
spark = SparkSession.builder.appName("IntelligentTrafficAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
traffic_df = spark.read.format("json").option("multiline", "true").load("/data/traffic/flow_data.json")
traffic_df.createOrReplaceTempView("traffic_flow")
hourly_flow = spark.sql("SELECT hour, road_id, AVG(vehicle_count) as avg_flow, MAX(vehicle_count) as peak_flow FROM traffic_flow WHERE date >= date_sub(current_date(), 30) GROUP BY hour, road_id ORDER BY hour")
flow_stats = hourly_flow.collect()
congestion_analysis = spark.sql("SELECT road_id, hour, vehicle_count, CASE WHEN vehicle_count > 500 THEN 'heavy' WHEN vehicle_count > 300 THEN 'moderate' ELSE 'light' END as traffic_level FROM traffic_flow")
peak_hours = spark.sql("SELECT hour, SUM(vehicle_count) as total_flow FROM traffic_flow GROUP BY hour ORDER BY total_flow DESC LIMIT 5")
road_ranking = spark.sql("SELECT road_id, AVG(vehicle_count) as avg_daily_flow, COUNT(*) as data_points FROM traffic_flow GROUP BY road_id HAVING COUNT(*) > 100 ORDER BY avg_daily_flow DESC")
flow_trend = spark.sql("SELECT date, SUM(vehicle_count) as daily_total FROM traffic_flow WHERE date >= date_sub(current_date(), 7) GROUP BY date ORDER BY date")
speed_analysis = spark.sql("SELECT road_id, AVG(speed) as avg_speed, MIN(speed) as min_speed FROM traffic_flow WHERE speed > 0 GROUP BY road_id")
bottleneck_detection = spark.sql("SELECT road_id, hour, AVG(speed) as avg_speed FROM traffic_flow WHERE speed < 20 GROUP BY road_id, hour HAVING COUNT(*) > 10")
traffic_efficiency = hourly_flow.withColumn("efficiency_score", col("avg_flow") / col("peak_flow") * 100)
flow_prediction_data = spark.sql("SELECT road_id, hour, AVG(vehicle_count) as historical_avg FROM traffic_flow WHERE date >= date_sub(current_date(), 90) GROUP BY road_id, hour")
congestion_duration = spark.sql("SELECT road_id, date, COUNT(*) as congested_hours FROM traffic_flow WHERE vehicle_count > 400 GROUP BY road_id, date")
traffic_distribution = spark.sql("SELECT road_type, SUM(vehicle_count) as total_vehicles, AVG(vehicle_count) as avg_vehicles FROM traffic_flow tf JOIN road_info ri ON tf.road_id = ri.road_id GROUP BY road_type")
flow_variance = spark.sql("SELECT road_id, STDDEV(vehicle_count) as flow_variance, AVG(vehicle_count) as mean_flow FROM traffic_flow GROUP BY road_id")
time_series_analysis = spark.sql("SELECT date, hour, LAG(vehicle_count, 1) OVER (PARTITION BY road_id ORDER BY date, hour) as prev_hour_count, vehicle_count as current_count FROM traffic_flow ORDER BY road_id, date, hour")
parking_df = spark.read.format("csv").option("header", "true").load("/data/parking/parking_usage.csv")
parking_df.createOrReplaceTempView("parking_data")
occupancy_rate = spark.sql("SELECT parking_lot_id, AVG(occupied_spaces / total_spaces * 100) as avg_occupancy, MAX(occupied_spaces / total_spaces * 100) as peak_occupancy FROM parking_data WHERE total_spaces > 0 GROUP BY parking_lot_id")
sharing_efficiency = spark.sql("SELECT parking_lot_id, date, SUM(turnover_count) as daily_turnover, AVG(occupied_spaces) as avg_occupied FROM parking_data GROUP BY parking_lot_id, date")
peak_parking_hours = spark.sql("SELECT hour, AVG(occupied_spaces / total_spaces * 100) as avg_occupancy FROM parking_data GROUP BY hour ORDER BY avg_occupancy DESC")
parking_revenue = spark.sql("SELECT parking_lot_id, SUM(revenue) as total_revenue, AVG(revenue / occupied_spaces) as revenue_per_space FROM parking_data WHERE occupied_spaces > 0 GROUP BY parking_lot_id")
availability_prediction = spark.sql("SELECT parking_lot_id, hour, AVG(total_spaces - occupied_spaces) as avg_available FROM parking_data GROUP BY parking_lot_id, hour")
location_analysis = spark.sql("SELECT location_type, AVG(occupied_spaces / total_spaces * 100) as occupancy_rate FROM parking_data pd JOIN location_info li ON pd.parking_lot_id = li.lot_id GROUP BY location_type")
sharing_patterns = spark.sql("SELECT parking_lot_id, day_of_week, AVG(turnover_count) as avg_turnover FROM parking_data GROUP BY parking_lot_id, day_of_week ORDER BY avg_turnover DESC")
utilization_trends = spark.sql("SELECT date, SUM(occupied_spaces) as total_occupied, SUM(total_spaces) as total_capacity FROM parking_data GROUP BY date ORDER BY date")
cost_analysis = spark.sql("SELECT parking_lot_id, AVG(hourly_rate) as avg_rate, SUM(revenue) / SUM(occupied_spaces) as cost_per_use FROM parking_data WHERE occupied_spaces > 0 GROUP BY parking_lot_id")
demand_supply_gap = spark.sql("SELECT location_type, SUM(total_spaces) as supply, AVG(occupied_spaces) as demand FROM parking_data pd JOIN location_info li ON pd.parking_lot_id = li.lot_id GROUP BY location_type")
seasonal_patterns = spark.sql("SELECT MONTH(date) as month, AVG(occupied_spaces / total_spaces * 100) as monthly_occupancy FROM parking_data GROUP BY MONTH(date) ORDER BY month")
parking_duration = spark.sql("SELECT parking_lot_id, AVG(avg_duration_hours) as typical_stay, MAX(avg_duration_hours) as longest_stay FROM parking_data WHERE avg_duration_hours > 0 GROUP BY parking_lot_id")
shared_mobility_impact = spark.sql("SELECT date, SUM(shared_vehicle_usage) as shared_usage, AVG(occupied_spaces) as parking_demand FROM parking_data GROUP BY date")
lot_performance = occupancy_rate.join(sharing_efficiency.groupBy("parking_lot_id").agg(avg("daily_turnover").alias("avg_turnover")), "parking_lot_id")
dynamic_pricing_data = spark.sql("SELECT parking_lot_id, hour, AVG(hourly_rate) as avg_price, AVG(occupied_spaces / total_spaces * 100) as occupancy FROM parking_data GROUP BY parking_lot_id, hour")
accident_df = spark.read.format("parquet").load("/data/safety/accident_records.parquet")
accident_df.createOrReplaceTempView("accident_data")
accident_hotspots = spark.sql("SELECT location_id, COUNT(*) as accident_count, AVG(severity_level) as avg_severity FROM accident_data WHERE date >= date_sub(current_date(), 365) GROUP BY location_id HAVING COUNT(*) >= 5 ORDER BY accident_count DESC")
time_pattern_analysis = spark.sql("SELECT hour, day_of_week, COUNT(*) as accident_frequency, AVG(severity_level) as avg_severity FROM accident_data GROUP BY hour, day_of_week ORDER BY accident_frequency DESC")
weather_correlation = spark.sql("SELECT weather_condition, COUNT(*) as accidents, AVG(severity_level) as avg_severity FROM accident_data ad JOIN weather_data wd ON ad.date = wd.date AND ad.hour = wd.hour GROUP BY weather_condition")
road_safety_score = spark.sql("SELECT road_id, COUNT(*) as total_accidents, AVG(severity_level) as severity, SUM(CASE WHEN severity_level > 3 THEN 1 ELSE 0 END) as serious_accidents FROM accident_data GROUP BY road_id")
injury_analysis = spark.sql("SELECT accident_type, SUM(injured_count) as total_injured, SUM(fatality_count) as total_fatalities, COUNT(*) as incident_count FROM accident_data GROUP BY accident_type ORDER BY total_injured DESC")
vehicle_involvement = spark.sql("SELECT vehicle_type, COUNT(*) as accident_involvement, AVG(severity_level) as avg_impact FROM accident_data GROUP BY vehicle_type ORDER BY accident_involvement DESC")
monthly_trends = spark.sql("SELECT YEAR(date) as year, MONTH(date) as month, COUNT(*) as monthly_accidents, AVG(severity_level) as monthly_severity FROM accident_data GROUP BY YEAR(date), MONTH(date) ORDER BY year, month")
age_demographics = spark.sql("SELECT driver_age_group, COUNT(*) as accidents, AVG(severity_level) as avg_severity FROM accident_data WHERE driver_age_group IS NOT NULL GROUP BY driver_age_group ORDER BY accidents DESC")
speed_related_incidents = spark.sql("SELECT CASE WHEN speed_limit_violation = true THEN 'speeding' ELSE 'normal_speed' END as speed_category, COUNT(*) as incidents, AVG(severity_level) as severity FROM accident_data GROUP BY speed_limit_violation")
intersection_safety = spark.sql("SELECT intersection_id, COUNT(*) as accidents, AVG(severity_level) as risk_level FROM accident_data WHERE intersection_id IS NOT NULL GROUP BY intersection_id HAVING COUNT(*) > 2 ORDER BY accidents DESC")
emergency_response = spark.sql("SELECT location_id, AVG(response_time_minutes) as avg_response_time, COUNT(*) as incident_count FROM accident_data WHERE response_time_minutes > 0 GROUP BY location_id")
casualty_rates = spark.sql("SELECT road_type, SUM(injured_count + fatality_count) as total_casualties, COUNT(*) as total_accidents, (SUM(injured_count + fatality_count) * 1.0 / COUNT(*)) as casualty_rate FROM accident_data ad JOIN road_info ri ON ad.road_id = ri.road_id GROUP BY road_type")
repeat_locations = spark.sql("SELECT location_id, COUNT(*) as frequency, MAX(date) as last_accident, MIN(date) as first_accident FROM accident_data GROUP BY location_id HAVING COUNT(*) > 3 ORDER BY frequency DESC")
safety_improvements = spark.sql("SELECT road_id, COUNT(*) as accidents_before, LAG(COUNT(*), 1) OVER (PARTITION BY road_id ORDER BY YEAR(date)) as accidents_previous_year FROM accident_data WHERE date >= date_sub(current_date(), 730) GROUP BY road_id, YEAR(date)")
risk_assessment = accident_hotspots.join(road_safety_score, accident_hotspots.col("location_id") == road_safety_score.col("road_id")).withColumn("risk_score", col("accident_count") * col("severity") / 10)
spark.stop()
基于大数据的智能出行交通数据可视化分析系统文档展示
💖💖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
