汽车城市中的数据驱动：车队维护数据的挖掘和建模
Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data
摘要：
底特律市拥有超过2500辆汽车的活跃车队，平均每年花费超过500万美元用于购买，超过770万美元用于维护。对这些数据中的模式和趋势进行建模对各种利益相关者来说特别重要，特别是当底特律从第9章破产中脱颖而出时，但这些数据的结构很复杂，而且该市缺乏专门的资源进行深入分析。底特律市运营和基础设施集团和密歇根大学发起了一项合作，旨在通过分析底特律市车队的数据来解决这一未满足的需求。这项工作提出了一个案例研究，并提供了第一个数据驱动的基准，展示了一套方法，以帮助大型车辆维护数据集的数据理解和预测。我们提出的分析，以解决利益相关者提出的三个关键问题，随着时间的推移，发现多元维护模式;预测维护;和预测车辆和车队的成本。我们提出了一种新的算法，PRISM，自动多元序列数据分析，张量分解。这项工作是第一次提出方法和见解，以指导未来的公民数据研究。
英文摘要：
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 Detroit's 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 Detroit's 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.
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