tc.sensors

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Overview

A package for pulling data for Minnesota Department of Transportation (MnDOT) loop detectors installed on the Minnesota Freeway system in 30-second interval measurements of occupancy and volume, data which are pushed daily via the Mayfly API.

Installation 

remotes::install_github("Metropolitan-Council/tc.sensors")

Documentation

To access documentation and for help on how to use the package, run ?<FUNCTION-NAME> (e.g. ?pull_sensor, ?pull_configuration, ?pull_sensor_ids). Access vignettes in the vignettes file to see examples of end-to-end workflows for pulling and storing data locally en masse. Check back for a vignettes for calculating speeds, reference speeds, delay, and VMT from the resulting files.

Relevant definitions

Definitions come from MnDOT Data Extract documentation

  • Volume The number of vehicles that pass through a detector in a given time period.
  • Occupancy The percentage of time a detector’s field is occupied by a vehicle.
  • Flow The number of vehicles that pass through a detector per hour (Volume * Samples per Hour).
  • Headway The number of seconds between each vehicle (Seconds_per_Hour / Flow).
  • Density The number of vehicles per mile (Flow / Speed). See full calculation method for additional context.
  • Speed The average speed of the vehicles that pass in a sampling period (Flow / Density).
  • Lost/Spare Capacity The average flow that a roadway is losing, either due to low traffic or high congestion, throughout the sampling period.
    • Flow > 1800: 0
    • Density > 43: Lost Capacity: Flow - 1800
    • Density >= 43: Lost Capacity: 1800 - Flow

Associated materials and projects

  • loop-sensor-trends Data analysis and interactive R Shiny app for examining changes in regional traffic levels in response to the COVID-19 pandemic.
  • iris MNIT/MnDOT repo for intelligent roadway information system (IRIS).
  • Mayfly API reference The API through which MnDOT pushes loop detector data.

Contributors

Maintainer Liz Roten ()

@ashleyasmus, @eroten, @SeanZhangMC, and @sullivannicole.