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Apache-2.0

bus-analysis

公交定位数据分析项目,主要实现根据公交大数据,分析公交车辆在跑那条线路,根据大量定位点,去分析该公交线路的具体轨迹路径

启动的步骤

1.确保hadoop启动与存取定位数据源

启动方式参照hadoop集群实战pdf文档的第3.2章节——启动hadoop。 启动好之后,上传文件到hdfs中去。

hadoop fs -put /root/hadoop-data/22lu.csv /line/test1/

/root/hadoop-data/22lu.csv是linux文件系统的文件,上传到hdfs的/line/test1/目录下。 执行hdfs的ls指令可查看22lu.csv是否已经在hdfs中。

hadoop fs -ls /line/test1/

2.确保数据库可连接

运行分析任务的前,需要获取数据库中uu_bus_stations与uu_bus_lines表的数据,也就是需要获得站点数据与线路数据作为参考。
找到项目下的resources目录下的druid.properties文件,配置mysql连接账号密码跟地址。

3.确保redis中保存有站点数据与线路数据

在运行分析任务的时候多线程并发,并不是每次需要参考数据库mysql的数据,而是访问redis来获得参考数据,所以在运行分析任务之前redis没有站点跟线路数据的时候,执行com.uu.application.FillDataToRedis来把mysql的数据萃取操作,然后存放到redis中。
找到项目下的resources目录下的system.properties文件,配置redis连接密码跟地址。

4.启动分析任务

4.1 本地java应用执行方式(非hadoop方式)

该方式仅适合对单个线路或单个车辆的数据进行分析

4.1.1 输入数据文件路径常量的确定

必须确定该数据文件是同一个线路或同一车辆的定位数据,在consts.properties中找到一下文件路径常量,修改成自己需要分析的定位数据文件的路径:

#本地分析任务的输入文件路径
local.analysis.source.input = E:\\workspace\\new\\bus-analysis\\src\\main\\resources\\data\\粤SA2262实时定位点.csv

4.1.2 执行本地模式程序

运行com.uu.application.LocalAnalysisApplication程序,输出方式:

***************************************
line.id:32
[
    {"lon":113.75046773163753,"lat":23.055426696904547},
    ...
    ,{"lon":113.74868172870724,"lat":23.055455790125823}
]
***************************************
***************************************
line.id:31
[
    {"lon":113.76827154520025,"lat":22.98206217960156},
    ...,
    {"lon":113.76829608556086,"lat":22.982355715887824}
]
***************************************

4.2 hadoop之上的分析任务

适合海量定位数据

4.2.1 配置hadoop连接地址,与hdfs输入路径与输出结果的路径


找到项目下的resources目录下的consts.properties文件,配置相关变量。
# hadoop的地址
hadoop.url = hdfs://hadoop2:9000

# 定位数据csv文件所在的根目录
map.reduce.input = /line/test1

# 轨迹分析的班次输出目录,作为轨迹分析二次MapReduce的输入目录
shuttle.reduce.output = /line/test1/output/shuttle

# 轨迹分析最终结果的输出目录
tracing.point.result.output = /line/test1/output/result

# 车辆分类分析最终结果的输出目录
car.line.result.output = /line/test1/output/car

在本文档的第1章节/line/test1/作为原始定位数据,所以hdfs的/line/test1目录下必须有原始数据文件,而后缀是output的文件目录均会在分析之后生成,例如/line/test1/output/shuttle,在/line/test1中并不存在,在分析任务完成后自动生成。

4.2.2 仅仅进行车牌车辆分类的分析任务

该分析任务对原始数据进行分析,仅仅对车辆是属于哪条线路做出分类,并不做轨迹点线路的路径进行分析。 4.2.1章节中的car.line.result.output常量就是该任务的输出结果的路径,可使用指令:

hadoop fs -get /line/test1/output

获取hdfs的文件夹到当前文件夹中
image
用xftp下载到win下查看,car目录下的part-r-00000就是车辆分类任务的最终输出结果文件。
image
输出格式是:
粤SA2262 31路
粤SA2283 31路
粤SA2287 31路
粤SA2293 31路
粤SA2298 31路
粤SD5835 31路
粤SD5838 31路
粤SD5850 31路
粤SD5856 31路
粤SD5857 31路

执行com.uu.application.hadoop.CarApplication即可执行车辆分类任务

4.2.3 轨迹分析任务

consts.properties中的tracing.point.result.output常量即是轨迹分析任务的最终结果输出的目录,输入的原始定位数据目录跟车辆分类任务一样是map.reduce.input
执行com.uu.application.hadoop.LineApplication,执行完后可以使用指令

hadoop fs -get /line/test1/output

获得分析结果,然后使用xftp等工具下载到win本地。同4.2.2一样output下找到输出结果的目录!
image
轨迹分析的结果格式是
lineID:31 [{"lat":22.984803369996936,"lon":113.76309095493292}]
lineID:32 [{"lat":23.058133582759325,"lon":113.74526880963269}]
key是lineID:xxx,value是对应线路ID的轨迹点数组

5.数据展示

把轨迹分析结果的某一个线路ID的轨迹点,由于分析的结果是wgs84标准的定位数据,而网页需要用gcj2的高德坐标系。所以需要进行一次转换才行。

  • 先把轨迹分析结果的value的json数组拷贝到web\data\wgs84-points.json中;
  • 然后执行com.uu.application.GeoMapConvert.kt程序,该程序会读取web\data\wgs84-points.json文件中的数据,转换后写入到web\data\tracing-points.json文件中;
  • 然后执行web根目录下的index.html网页,查看轨迹分析的结果。
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公交定位数据分析项目,主要实现根据公交大数据,分析公交车辆在跑那条线路,根据大量定位点,去分析该公交线路的具体轨迹路径 展开 收起
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