  Pedestrian safety is a priority for transportation system managers and
operators, and a main focus of the Vision Zero strategy employed by the City of
Austin, Texas. While there are a number of treatments and technologies to
effectively improve pedestrian safety, identifying the location where these
treatments are most needed remains a challenge. Current practice requires
manual observation of candidate locations for limited time periods, leading to
an identification process that is time consuming, lags behind traffic pattern
changes over time, and lacks scalability. Mid-block locations, where safety
countermeasures are often needed the most, are especially hard to identify and
monitor. The goal for this research is to understand the correlation between
bus stop locations and mid-block crossings, so as to assist traffic engineers
in implementing Vision Zero strategies to improve pedestrian safety. In a prior
work, we have developed a tool to detect pedestrian crossing events with
traffic camera video using a deep neural network model to identify crossing
events. In this paper, we extend the methods to identify bus stop usage with
traffic camera video from off-the-shelf CCTV pan-tilt-zoom (PTZ) traffic
monitoring cameras installed at nearby intersections. We correlate the video
detection results for mid-block crossings near a bus stop, with pedestrian
activity at the bus stops in each side of the mid-block crossing. We also
implement a web portal to facilitate manual review of pedestrian activity
detections by automating creation of video clips that show only crossing
events, thereby vastly improving the efficiency of the human review process.
