  Signalized intersections, arguably the most complicated type of traffic
scenario, are essential to urban mobility systems. With recent advancements in
intelligent transportation technologies, signalized intersections have great
prospects for making transportation greener, safer, and faster. Several studies
have been conducted focusing on intersection-level control and optimization.
However, arbitrarily structured signalized intersections that are often used do
not represent the ground-truth distribution, and there is no standardized way
that exists to extract information about real-world signalized intersections.
As the largest open-source map in the world, OpenStreetMap (OSM) has been used
by many transportation researchers for a variety of studies, including
intersection-level research such as adaptive traffic signal control and
eco-driving. However, the quality of OSM data has been a serious concern.
  In this paper, we propose a pipeline for effectively extracting information
about signalized intersections from OSM and constructing a comprehensive
dataset. We thoroughly discuss challenges related to this task and we propose
our solution for each challenge. We also use Salt Lake City as an example to
demonstrate the performance of our methods. The pipeline has been published as
an open-source Python library so everyone can freely download and use it to
facilitate their research. Hopefully, this paper can serve as a starting point
that inspires more efforts to build a standardized and systematic data pipeline
for various types of transportation problems.
