Manhattan, New York City, 2020 Traffic Time Series + R Code for Analysis

This dataset includes (1) a .txt file of processed time-series with four traffic congestion levels for the borough of Manhattan, NYC, averaged every 3 hours for the duration of 2020, and (2) an R script for completing analysis of the traffic time series to determine patterns in traffic over the year 2020, and to evaluate the impact of stay-at-home orders implemented in response to the COIVD-19 pandemic.

Methods
Raw (pre-processed) data was collected by automatically downloading tiles of Google Traffic maps. These images were then processed to select colors used by Google Traffic that correspond to traffic congestion level on their maps (green = free-flowing traffic, orange = some traffic delays, red = traffic congestion, dark red / maroon = severely congested traffic) and to determine the percent of the map area covered by each color. Each row in the data represents the average for a 3 hour period. Traffic time series were then analyzed to determine traffic patterns over the course of 2020 and to assess adherence to social distancing interventions put in place during the pandemic. Details about raw data collection, processing, and analysis can be found in other sources: 

Hilpert M, Shearston JA, Cole J, Chillrud SN, Martinez ME. Acquisition and analysis of crowd-sourced traffic data. arXiv. 2021:2105.12235. https://arxiv.org/abs/2105.12235

Jenni A. Shearston, Micaela E. Martinez, Yanelli Nunez, Markus Hilpert. Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data. medRxiv 2021.03.04.21252917; doi:https://doi.org/10.1101/2021.03.04.21252917