作者计算机毕业设计杰瑞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校实战项目计算机毕业设计选题推荐目录基于大数据的智慧城市交通流量与出行数据可视化分析介绍基于大数据的智慧城市交通流量与出行数据可视化分析演示视频基于大数据的智慧城市交通流量与出行数据可视化分析演示图片基于大数据的智慧城市交通流量与出行数据可视化分析代码展示基于大数据的智慧城市交通流量与出行数据可视化分析文档展示基于大数据的智慧城市交通流量与出行数据可视化分析介绍本系统《基于大数据的智慧城市交通流量与出行数据可视化分析》旨在解决城市交通数据量大、多源异构、分析维度复杂等现实问题为计算机专业毕业设计提供一个完整、可落地的大数据实战项目。系统采用Hadoop HDFS作为分布式存储底座承载原始GPS轨迹、路段流量、卡口过车等城市出行数据集利用Spark计算引擎进行内存级分布式处理通过Spark SQL实现多维度指标聚合与关联查询替代传统关系库在海量数据场景下的性能瓶颈。后端采用Spring Boot框架提供RESTful API服务前端基于Vue3与Element Plus构建管理界面并集成ECharts实现交通流量热力图、出行OD弦图、时段折线图等可视化大屏组件。功能模块涵盖用户认证与账户档案管理、城市出行数据的增删改查与条件筛选、基于Spark的出行特征分析如高峰时段识别、热门路段TopN、平均速度分布、以及出行数据拼图式的多图表联动看板。整个系统从数据采集、存储、计算到展示形成闭环既体现了大数据技术的工程价值又为智慧城市交通管理提供了直观的数据支撑。基于大数据的智慧城市交通流量与出行数据可视化分析演示视频演示视频基于大数据的智慧城市交通流量与出行数据可视化分析演示图片基于大数据的智慧城市交通流量与出行数据可视化分析代码展示SparkSession sparkSparkSession.builder().appName(TrafficFlowAnalysis).master(local[*]).config(spark.sql.shuffle.partitions,8).getOrCreate();DatasetRowdfspark.read().option(header,true).option(inferSchema,true).csv(hdfs://localhost:9000/traffic/gps_raw/*.csv);DatasetRowcleanedDfdf.filter(df.col(latitude).isNotNull().and(df.col(longitude).isNotNull()).and(df.col(speed).cast(double).gt(0)).and(df.col(gps_time).isNotNull()));cleanedDf.createOrReplaceTempView(gps_records);DatasetRowhourTrafficspark.sql(SELECT hour(gps_time) as hour, count(*) as total_count, avg(speed) as avg_speed, max(speed) as max_speed, min(speed) as min_speed FROM gps_records WHERE speed 0 GROUP BY hour(gps_time) ORDER BY hour);ListRowhourListhourTraffic.collectAsList();for(Row row:hourList){inthourrow.getInt(0);longcountrow.getLong(1);double avgSpdrow.getDouble(2);double maxSpdrow.getDouble(3);double minSpdrow.getDouble(4);jdbcTemplate.update(INSERT INTO traffic_hourly_stats (hour, total_count, avg_speed, max_speed, min_speed) VALUES (?, ?, ?, ?, ?) ON DUPLICATE KEY UPDATE total_count ?, avg_speed ?, max_speed ?, min_speed ?,hour,count,avgSpd,maxSpd,minSpd,count,avgSpd,maxSpd,minSpd);}DatasetRowroadRankspark.sql(SELECT road_id, count(*) as pass_count, avg(speed) as avg_speed, stddev(speed) as speed_std FROM gps_records WHERE road_id IS NOT NULL GROUP BY road_id ORDER BY pass_count DESC LIMIT 10);ListRowrankListroadRank.collectAsList();for(Row row:rankList){String roadIdrow.getString(0);longpassCntrow.getLong(1);double avgSpdrow.getDouble(2);double stdSpdrow.getDouble(3);jdbcTemplate.update(REPLACE INTO road_hot_rank (road_id, pass_count, avg_speed, speed_stddev) VALUES (?, ?, ?, ?),roadId,passCnt,avgSpd,stdSpd);}DatasetRowtimeRangeFilterspark.sql(SELECT gps_time, vehicle_id, latitude, longitude, speed, road_id FROM gps_records WHERE gps_time BETWEEN 2026-09-01 07:00:00 AND 2026-09-01 09:00:00 AND speed 5 ORDER BY gps_time DESC LIMIT 500);ListRowtimeListtimeRangeFilter.collectAsList();for(Row row:timeList){String gpsTimerow.getString(0);String vehicleIdrow.getString(1);double latrow.getDouble(2);double lonrow.getDouble(3);double speedrow.getDouble(4);String roadIdrow.getString(5);jdbcTemplate.update(INSERT INTO filtered_gps_data (gps_time, vehicle_id, latitude, longitude, speed, road_id) VALUES (?, ?, ?, ?, ?, ?),gpsTime,vehicleId,lat,lon,speed,roadId);}DatasetRowodMatrixspark.sql(SELECT origin_zone, dest_zone, count(*) as trip_count FROM (SELECT first_value(zone_id) over (partition by vehicle_id, session_id order by gps_time) as origin_zone, last_value(zone_id) over (partition by vehicle_id, session_id order by gps_time) as dest_zone FROM gps_records WHERE session_id IS NOT NULL) t WHERE origin_zone IS NOT NULL AND dest_zone IS NOT NULL GROUP BY origin_zone, dest_zone ORDER BY trip_count DESC LIMIT 20);ListRowodListodMatrix.collectAsList();for(Row row:odList){String originrow.getString(0);String destrow.getString(1);longcntrow.getLong(2);jdbcTemplate.update(INSERT INTO od_pair_matrix (origin_zone, dest_zone, trip_count) VALUES (?, ?, ?) ON DUPLICATE KEY UPDATE trip_count ?,origin,dest,cnt,cnt);}DatasetRowcongestionLevelspark.sql(SELECT road_id, avg(speed) as avg_spd, CASE WHEN avg(speed) 10 THEN severe WHEN avg(speed) 25 THEN moderate WHEN avg(speed) 40 THEN light ELSE smooth END as level, count(*) as sample_count FROM gps_records WHERE speed 0 AND road_id IS NOT NULL GROUP BY road_id);ListRowcongListcongestionLevel.collectAsList();for(Row row:congList){String roadIdrow.getString(0);double avgSpdrow.getDouble(1);String levelrow.getString(2);longcntrow.getLong(3);jdbcTemplate.update(REPLACE INTO road_congestion (road_id, avg_speed, congestion_level, sample_count) VALUES (?, ?, ?, ?),roadId,avgSpd,level,cnt);}DatasetRowspeedDistributionspark.sql(SELECT floor(speed/5)*5 as speed_bin, count(*) as freq FROM gps_records WHERE speed 0 AND speed 120 GROUP BY floor(speed/5)*5 ORDER BY speed_bin);ListRowdistListspeedDistribution.collectAsList();for(Row row:distList){intbinrow.getInt(0);longfreqrow.getLong(1);jdbcTemplate.update(REPLACE INTO speed_distribution (speed_bin, frequency) VALUES (?, ?),bin,freq);}DatasetRowpeakVehiclespark.sql(SELECT vehicle_id, count(*) as record_count, avg(speed) as avg_speed, max(gps_time) as last_time FROM gps_records WHERE date(gps_time) 2026-09-01 GROUP BY vehicle_id HAVING record_count 50 ORDER BY record_count DESC LIMIT 100);ListRowpeakListpeakVehicle.collectAsList();for(Row row:peakList){String vidrow.getString(0);longcntrow.getLong(1);double avgSpdrow.getDouble(2);String lastrow.getString(3);jdbcTemplate.update(INSERT INTO active_vehicles (vehicle_id, daily_record_count, avg_speed, last_gps_time) VALUES (?, ?, ?, ?) ON DUPLICATE KEY UPDATE daily_record_count ?, avg_speed ?, last_gps_time ?,vid,cnt,avgSpd,last,cnt,avgSpd,last);}spark.stop();基于大数据的智慧城市交通流量与出行数据可视化分析文档展示作者计算机毕业设计杰瑞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校实战项目计算机毕业设计选题推荐