  The City of Detroit maintains an active fleet of over 2500 vehicles, spending
an annual average of over \$5 million on new vehicle purchases and over \$7.7
million on maintaining this fleet. Understanding the existence of patterns and
trends in this data could be useful to a variety of stakeholders, particularly
as Detroit emerges from Chapter 9 bankruptcy, but the patterns in such data are
often complex and multivariate and the city lacks dedicated resources for
detailed analysis of this data. This work, a data collaboration between the
Michigan Data Science Team (http://midas.umich.edu/mdst) and the City of
Detroit's Operations and Infrastructure Group, seeks to address this unmet need
by analyzing data from the City of Detroit's entire vehicle fleet from
2010-2017. We utilize tensor decomposition techniques to discover and visualize
unique temporal patterns in vehicle maintenance; apply differential sequence
mining to demonstrate the existence of common and statistically unique
maintenance sequences by vehicle make and model; and, after showing these
time-dependencies in the dataset, demonstrate an application of a predictive
Long Short Term Memory (LSTM) neural network model to predict maintenance
sequences. Our analysis shows both the complexities of municipal vehicle fleet
data and useful techniques for mining and modeling such data.
