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The City of Detroit maintains an active fleet of over 2500 vehicles, spendingan annual average of over \$5 million on new vehicle purchases and over \$7.7million on maintaining this fleet. Understanding the existence of patterns andtrends in this data could be useful to a variety of stakeholders, particularlyas Detroit emerges from Chapter 9 bankruptcy, but the patterns in such data areoften complex and multivariate and the city lacks dedicated resources fordetailed analysis of this data. This work, a data collaboration between theMichigan Data Science Team (http://midas.umich.edu/mdst) and the City ofDetroit's Operations and Infrastructure Group, seeks to address this unmet needby analyzing data from the City of Detroit's entire vehicle fleet from2010-2017. We utilize tensor decomposition techniques to discover and visualizeunique temporal patterns in vehicle maintenance; apply differential sequencemining to demonstrate the existence of common and statistically uniquemaintenance sequences by vehicle make and model; and, after showing thesetime-dependencies in the dataset, demonstrate an application of a predictiveLong Short Term Memory (LSTM) neural network model to predict maintenancesequences. Our analysis shows both the complexities of municipal vehicle fleetdata and useful techniques for mining and modeling such data.

Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit