  The COVID-19 pandemic brought unprecedented levels of disruption to the local
and regional transportation networks throughout the United States, especially
the Motor City: Detroit. That was mainly a result of swift restrictive measures
such as statewide quarantine and lock-down orders to confine the spread of the
virus. This work is driven by analyzing five types of real-world data sets from
Detroit related to traffic volume, daily cases, weather, social distancing
index, and crashes from January 2019 to June 2020. The primary goal is figuring
out the impacts of COVID-19 on the transportation network usage (traffic
volume) and safety (crashes) for the Detroit, exploring the potential
correlation between these diverse data features, and determining whether each
type of data (e.g., traffic volume data) could be a useful factor in the
confirmed-cases prediction. In addition, a deep learning model was developed
using long short-term memory networks to predict the number of confirmed cases
within the next one week. The model demonstrated a promising prediction result
with a coefficient of determination (R^2) of up to approximately 0.91.
Moreover, in order to provide statistical evaluation measures of confirmed-case
prediction and to quantify the prediction effectiveness of each type of data,
the prediction results of six feature groups are presented and analyzed.
Furthermore, six essential observations with supporting evidence and analyses
are presented. The goal of this paper is to present a proposed approach which
can be applied, customised, adjusted, and replicated for analysis of the impact
of COVID-19 on a transportation network and prediction of the anticipated
COVID-19 cases using a similar data set obtained for other large cities in the
USA or from around the world.
