A dataset containing the pedestrian counts at hourly intervals from 2016-01-01 to 2017-04-20 at 7 sensors in the city of Melbourne. The variables are as follows:

hourly_peds

Format

A tibble with 78755 rows and 9 variables:

Date_Time

Date time when the pedestrian counts are recorded

Year

Year associated with Date_Time

Month

Month associated with Date_Time

Mdate

Day of month associated with Date_Time

Day

Weekday associated with Date_Time

Time

Hour associated with Date_Time

Sensor_ID

Sensor identifiers

Sensor_Name

Sensor names

Hourly_Counts

Hourly pedestrian counts

Examples

hourly_peds
#> # A tibble: 78,755 x 10 #> Date_Time Date Year Month Mdate Day Time Sensor_ID #> <dttm> <date> <dbl> <ord> <dbl> <ord> <dbl> <dbl> #> 1 2016-01-01 00:00:00 2016-01-01 2016 Janu… 1 Frid… 0 18 #> 2 2016-01-01 00:00:00 2016-01-01 2016 Janu… 1 Frid… 0 13 #> 3 2016-01-01 00:00:00 2016-01-01 2016 Janu… 1 Frid… 0 3 #> 4 2016-01-01 00:00:00 2016-01-01 2016 Janu… 1 Frid… 0 9 #> 5 2016-01-01 00:00:00 2016-01-01 2016 Janu… 1 Frid… 0 6 #> 6 2016-01-01 00:00:00 2016-01-01 2016 Janu… 1 Frid… 0 25 #> 7 2016-01-01 00:00:00 2016-01-01 2016 Janu… 1 Frid… 0 30 #> 8 2016-01-01 01:00:00 2016-01-01 2016 Janu… 1 Frid… 1 18 #> 9 2016-01-01 01:00:00 2016-01-01 2016 Janu… 1 Frid… 1 13 #> 10 2016-01-01 01:00:00 2016-01-01 2016 Janu… 1 Frid… 1 3 #> # … with 78,745 more rows, and 2 more variables: Sensor_Name <chr>, #> # Hourly_Counts <dbl>