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The City of Detroit maintains an active fleet of over 2500 vehicles, spending an annual average of over $5 million on purchases and over $7.7 million on maintenance. Modeling patterns and trends in this data is of particular importance to a variety of stakeholders, particularly as Detroit emerges from Chapter 9 bankruptcy, but the structure in such data is complex, and the city lacks dedicated resources for in-depth analysis. The City of Detroits Operations and Infrastructure Group and the University of Michigan initiated a collaboration which seeks to address this unmet need by analyzing data from the City of Detroits vehicle fleet. This work presents a case study and provides the first data-driven benchmark, demonstrating a suite of methods to aid in data understanding and prediction for large vehicle maintenance datasets. We present analyses to address three key questions raised by the stakeholders, related to discovering multivariate maintenance patterns over time; predicting maintenance; and predicting vehicle- and fleet-level costs. We present a novel algorithm, PRISM, for automating multivariate sequential data analyses using tensor decomposition. This work is a first of its kind that presents both methodologies and insights to guide future civic data research.
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 da
In this research, a new data mining-based design approach has been developed for designing complex mechanical systems such as a crashworthy passenger car with uncertainty modeling. The method allows exploring the big crash simulation dataset to desig
Anticipating the political behavior of people will be considerable help for election candidates to assess the possibility of their success and to be acknowledged about the public motivations to select them. In this paper, we provide a general schemat
Traffic violations like illegal parking, illegal turning, and speeding have become one of the greatest challenges in urban transportation systems, bringing potential risks of traffic congestions, vehicle accidents, and parking difficulties. To maximi
A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region. It is named Automatic Preference based DI-MOEA (AP-DI-MOEA) where DI-MOEA stands for Diversity-Indicator ba