Cluster Computing

, Volume 22, Supplement 1, pp 2239–2254 | Cite as

Transit-hub: a smart public transportation decision support system with multi-timescale analytical services

  • Fangzhou SunEmail author
  • Abhishek Dubey
  • Jules White
  • Aniruddha Gokhale


Public transit is a critical component of a smart and connected community. As such, citizens expect and require accurate information about real-time arrival/departures of transportation assets. As transit agencies enable large-scale integration of real-time sensors and support back-end data-driven decision support systems, the dynamic data-driven applications systems (DDDAS) paradigm becomes a promising approach to make the system smarter by providing online model learning and multi-time scale analytics as part of the decision support system that is used in the DDDAS feedback loop. In this paper, we describe a system in use in Nashville and illustrate the analytic methods developed by our team. These methods use both historical as well as real-time streaming data for online bus arrival prediction. The historical data is used to build classifiers that enable us to create expected performance models as well as identify anomalies. These classifiers can be used to provide schedule adjustment feedback to the metro transit authority. We also show how these analytics services can be packaged into modular, distributed and resilient micro-services that can be deployed on both cloud back ends as well as edge computing resources.



This work is supported by The National Science Foundation under the award numbers CNS-1528799 and CNS-1647015 and a TIPS grant from Vanderbilt University. We acknowledge the support provided by our partners from Nashville Metropolitan Transport Authority.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Software Integrated Systems, Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleUSA

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