  Crowd-sourced traffic data offer great promise in environmental modeling.
However, archives of such traffic data are typically not made available for
research; instead, the data must be acquired in real time. The objective of
this paper is to present methods we developed for acquiring and analyzing time
series of real-time crowd-sourced traffic data. We present scripts, which can
be run in Unix/Linux like computational environments, to automatically download
tiles of crowd-sourced Google traffic congestion maps for a user-specifiable
region of interest. Broad and international applicability of our method is
demonstrated for Manhattan in New York City and Mexico City. We also
demonstrate that Google traffic data can be used to quantify decreases in
traffic congestion due to social distancing policies implemented to curb the
COVID-19 pandemic in the South Bronx in New York City.
