Visualize and analyse traffic

Introduction

This vignette illustrates the potential visualizations (and the ensuing analysis) of traffic data from Telraam sensors provided by the package.

Before we go on, we’ll attach the packages we use.

library(telraamStats)

We will also load the data: here, we will use the dataset attached to the package, but you can certainly replicate these visualizations and analyses using data from your own sensors. For more information on data retrieval, you can refer to the dedicated vignette (vignette("data-details")).

Included dataset is lazily loaded with the telraamStats package. If you have already called library(telraamStats), the dataset is already attached and you can call it directly :

head(traffic)

Period availability for your segments

Road segment-specific sensors do not operate continuously; they are limited by daylight duration and may also face other external issues (being unplugged, falling, software problems, etc.). The data collected during a period may not be continuous and could have interruptions ranging from an hour to several days.

To visualize traffic, it is important to be aware of the operating periods of the sensors being analyzed, as well as the quality of their data. The data reliability is provided through the uptime indicator. Uptime, ranging from 0 to 1, corresponds to the percentage of time during which the camera is actively counting passages. Following Telraam’s recommendations, here an uptime lower than 0.5 is considered associated with poor-quality data.

The graphical representation below, obtained using the gg_availability() function, provides an overview of data availability and quality for different sensors over the entire period. This indicator is calculated over daytime hours (between 5 a.m. and 8 p.m.) when the Telraam sensor is operational. It represents the average uptime during these hours. It illustrates the daily average uptime evolution: the higher this value, the better the data quality for that sensor on the respective day. Its purpose is to assist you in selecting study periods.

plot(
  gg_availability(traffic)
)

On this graph, for example, we can observe through black time slots that the camera on the segment ‘ParisArcEnCiel-05’ did not report any data at the end of June 2022 and at the end of the year 2022. We can also notice that the average uptime is higher during the summer period. This illustrates the fact that the duration of daylight, and therefore the possible filming period, is longer in summer.

All the graphs generated from the functions presented below are based on data filtered according to this uptime parameter: only hours with an uptime > 0.5 are retained. This filter is not editable at the moment and ensures that the data considered have a sufficient level of reliability.

Traffic

plot(
  gg_traffic_evolution(traffic)
)

plot(
  gg_traffic_evolution(traffic,
                       date_range = c('2022-01-01','2022-03-01'),
                       segment = 'RteVitre-06',
                       mode = c('car','pedestrian'),
                       direction = 'lft',
                       smoothed = FALSE,
                       agg_day = FALSE)
)

plot(
  gg_traffic_avg(traffic)
)

plot(
  gg_traffic_avg(traffic,
                 date_range = c('2022-07-01','2022-09-01'),
                 segment = 'RteVitre-06',
                 mode = 'car',
                 direction = 'rgt',
                 weekday = c('monday','friday')
                 )
)

plot(
  gg_traffic_avg(traffic, 
                 aggregated_by = "segment_name")
)

plot(
  gg_traffic_avg(traffic, 
                 aggregated_by = 'segment_name',
                 weekday = 'sunday',
                 mode = 'car',
                 direction = 'rgt')
)

plot(
  gg_traffic_avg(traffic, 
                 aggregated_by = 'direction',
                 weekday = 'monday',
                 mode = 'car',
                 segments = 'RteVitre-06'
                 )
)

plot(
  gg_traffic_avg(traffic, 
                 aggregated_by = 'mode',
                 mode = c('heavy','car'),
                 segment = 'RteVitre-06')
)

Car speed

Telraam sensors offer speed estimation for cars on two levels :

• the estimated car speed distribution (ranging from 0 to 120+ km/h), in 10 or 5 km/h bins ;
• v85, the estimated car speed limit in km/h that 85% of all cars respect. 15% of drivers drive faster than this v85 indicator.

Telraam specifies that all these speed-related measurements have biases: their accuracy is likely not better than +/- 10%. Moreover, these measurements pertain exclusively to cars and do not include other modes of transportation.

plot(
  gg_car_speed_histogram(traffic)
)

plot(
  gg_car_speed_histogram(traffic, 
                         aggregated_by = 'segment_name')
)

plot(
  gg_car_speed_histogram(traffic, 
                         weekday = c('monday','sunday'),
                         segments = 'RteVitre-06',
                         hours = 17:20,
                         aggregated_by = "weekday")
)

plot(
  gg_car_speed_v85(traffic)
)

plot(
  gg_car_speed_v85(traffic,
                   aggregated_by = 'segment_name')
)

plot(
  gg_car_speed_v85(traffic,
                   aggregated_by = 'weekday',
                   date_range = c('2022-01-01','2022-03-01'),
                   segments = 'RteVitre-06')
)