如何从历史数据中检索行程?

How to retrieve trips from historical data?

我在 Hive 中有以下 table mytable

id    radar_id     car_id     datetime
1     A21          123        2017-03-08 17:31:19.0
2     A21          555        2017-03-08 17:32:00.0
3     A21          777        2017-03-08 17:33:00.0
4     B15          123        2017-03-08 17:35:22.0
5     B15          555        2017-03-08 17:34:05.0
5     B15          777        2017-03-08 20:50:12.0
6     A21          123        2017-03-09 11:00:00.0
7     C11          123        2017-03-09 11:10:00.0
8     A21          123        2017-03-09 11:12:00.0
9     A21          555        2017-03-09 11:12:10.0
10    B15          123        2017-03-09 11:14:00.0
11    C11          555        2017-03-09 11:20:00.0

我想获取同一行程中通过雷达 A21B15 的汽车的路线。例如,如果同一 car_id 的日期不同,则不是同一趟旅行。基本上,我想考虑同一辆车的雷达 A21B15 之间的最大时间差应该是 30 分钟。如果它更大,那么行程就不一样了,例如 car_id 777.

我的最终目标是统计每天的平均行程数(不唯一,所以如果同一辆车经过同一条路线2次,那么应该计算2次)。

预期结果如下:

radar_start   radar_end       avg_tripscount_per_day
A21           B15             1.5

日期 2017-03-08 雷达 A21B15 之间有 2 次行程(由于 30 分钟的限制,汽车 777 不被考虑),而在date 2017-03-09 只有一趟。平均每天2+1=1.5次。

我怎样才能得到这个结果?基本上,我不知道如何在查询中引入 30 分钟限制以及如何按 radar_startradar_end.

对游乐设施进行分组

谢谢。

更新:

  1. 行程在开始日期登记。
  2. 如果汽车在 2017-03-08 23:55 时被雷达 A21 和在 2017-03-09 00:15 时被雷达 B15 触发,则应将其视为为日期 2017-03-08.
  3. 如果ids6和8同车123经过A21两次,然后转向B15id 10).最后一骑id8应该考虑。所以,8-10。因此,最接近 B15 的前一个。解释是一辆汽车经过A21两次,第二次转向B15

我没有注意到您正在使用 Hive,因此开始为 SQL-Server 编写查询,但也许它会对您有所帮助。尝试这样的事情:

查询

select radar_start, 
       radar_end, 
       convert(decimal(6,3), count(*)) / convert(decimal(6,3), count(distinct dt)) as avg_tripscount_per_day
from (
    select 
        t1.radar_id as radar_start,
        t2.radar_id as radar_end,
        convert(date, t1.[datetime]) dt,
        row_number() over (partition by t1.radar_id, t1.car_id, convert(date, t1.[datetime]) order by t1.[datetime] desc) rn1,
        row_number() over (partition by t2.radar_id, t2.car_id, convert(date, t2.[datetime]) order by t2.[datetime] desc) rn2
    from trips as t1
    join trips as t2 on t1.car_id = t2.car_id 
        and datediff(minute,t1.[datetime], t2.[datetime]) between 0 and 30
        and t1.radar_id = 'A21' 
        and t2.radar_id = 'B15'
)x
where rn1 = 1 and rn2 = 1
group by radar_start, radar_end

输出

radar_start radar_end   avg_tripscount_per_day
A21         B15         1.5000000000

示例数据

create table trips
(
    id int,
    radar_id char(3),
    car_id int,
    [datetime] datetime
)
insert into trips values
(1,'A21',123,'2017-03-08 17:31:19.0'),
(2,'A21',555,'2017-03-08 17:32:00.0'),
(3,'A21',777,'2017-03-08 17:33:00.0'),
(4,'B15',123,'2017-03-08 17:35:22.0'),
(5,'B15',555,'2017-03-08 17:34:05.0'),
(5,'B15',777,'2017-03-08 20:50:12.0'),

(6,'A21',123,'2017-03-09 11:00:00.0'),
(7,'C11',123,'2017-03-09 11:10:00.0'),
(8,'A21',123,'2017-03-09 11:12:00.0'),

(9,'A21',555,'2017-03-09 11:12:10.0'),
(8,'B15',123,'2017-03-09 11:14:00.0'),
(9,'C11',555,'2017-03-09 11:20:00.0')
select  count(*) / count(distinct to_date(datetime))    as trips_per_day

from   (select  radar_id
               ,datetime
               ,lead(radar_id) over w  as next_radar_id
               ,lead(datetime) over w  as next_datetime                    

        from    mytable

        where   radar_id in ('A21','B15')

        window  w as 
                (
                    partition by  car_id
                    order by      datetime
                )
        ) t

where   radar_id        = 'A21'
    and next_radar_id   = 'B15'
    and datetime + interval '30' minutes >= next_datetime
;

+----------------+
| trips_per_day  |
+----------------+
| 1.5            |
+----------------+

P.s.
如果您的版本不支持间隔,最后的代码记录可以替换为 -
and to_unix_timestamp(datetime) + 30*60 > to_unix_timestamp(next_datetime)