Using data analytics to optimize public transportation on a college campus

Kurt Zimmer, Hasan Kurban, Mark Jenne, Logan Keating, Perry Maull, Mehmet Dalkilic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Using a large volume of bus data in the form of GPS coordinates (over 100 million data points) and automated passenger count data (over 1 million data points) we have developed (1) a system of analysis and prediction of future public transportation demand (2) a new model that uses concepts specific to college campuses that maximizes passenger satisfaction. Using these concepts we improve service of a model college public transportation service and more specifically the Indiana University Campus Bus Service (IUCBS).

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018
EditorsFrancesco Bonchi, Foster Provost, Tina Eliassi-Rad, Wei Wang, Ciro Cattuto, Rayid Ghani
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages460-469
Number of pages10
ISBN (Electronic)9781538650905
DOIs
Publication statusPublished - 2 Jul 2018
Event5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018 - Turin, Italy
Duration: 1 Oct 20184 Oct 2018

Publication series

NameProceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018

Conference

Conference5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018
Country/TerritoryItaly
CityTurin
Period1/10/184/10/18

Keywords

  • APC
  • Big data
  • Bus
  • GPS
  • Public transportation

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