  The novel Corona virus pandemic is one of the biggest worldwide problems
right now. While hygiene and wearing masks make up a large portion of the
currently suggested precautions by the Centers for Disease Control and
Prevention (CDC) and World Health Organization (WHO), social distancing is
another and arguably the most important precaution that would protect people
since the airborne virus is easily transmitted through the air. Social
distancing while walking outside, can be more effective, if pedestrians know
locations of each other and even better if they know locations of people who
are possible carriers. With this information, they can change their routes
depending on the people walking nearby or they can stay away from areas that
contain or have recently contained crowds. This paper presents a mobile device
application that would be a very beneficial tool for social distancing during
Coronavirus Disease 2019 (COVID-19). The application works, synced close to
real-time, in a networking fashion with all users obtaining their locations and
drawing a virtual safety bubble around them. These safety bubbles are used with
the constant velocity pedestrian model to predict possible future social
distancing violations and warn the user with sound and vibration. Moreover, it
takes into account the virus staying airborne for a certain time, hence,
creating time-decaying non-safe areas in the past trajectories of the users.
The mobile app generates collision free paths for navigating around the
undesired locations for the pedestrian mode of transportation when used as part
of a multi-modal trip planning app. Results are applicable to other modes of
transportation also. Features and the methods used for implementation are
discussed in the paper. The application is tested using previously collected
real pedestrian walking data in a realistic environment.
