Urban traffic prediction through the second use of inexpensive big data from buildings

Zimu Zheng, Dan Wang, Jian Pei, Yi Yuan, Cheng Fan, Fu Xiao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

18 Citations (Scopus)

Abstract

Traffic prediction, particularly in urban regions, is an important application of tremendous practical value. In this paper, we report a novel and interesting case study of urban traffic prediction in Central, Hong Kong, one of the densest urban areas in the world. The novelty of our study is that we make good second use of inexpensive big data collected from the Hong Kong International Commerce Centre (ICC), a 118-story building in Hong Kong where more than 10,000 people work. As building environment data are much cheaper to obtain than traffic data, we demonstrate that it is highly effective to estimate building occupancy information using building environment data, and then to further use the information on occupancy to provide traffic predictions in the proximate area. Scientifically, we investigate how and to what extent building data can complement traffic data in predicting traffic. In general, this study sheds new light on the development of accurate data mining applications through the second use of inexpensive big data.
Original languageEnglish
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1363-1372
Number of pages10
Volume24-28-October-2016
ISBN (Electronic)9781450340731
DOIs
Publication statusPublished - 24 Oct 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: 24 Oct 201628 Oct 2016

Conference

Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States
CityIndianapolis
Period24/10/1628/10/16

Keywords

  • Building occupancy
  • Traffic prediction

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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