Traffic forecasting in complex urban networks: Leveraging big data and machine learning

Florin Schimbinschi, Xuan Vinh Nguyen, James Bailey, Chris Leckie, Hai Vu, Rao Kotagiri

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

19 Citations (Scopus)

Abstract

Accurate network-wide real time traffic forecasting is essential for next generation smart cities. In this context, we study a novel and complex traffic data set and explore the potential to apply big data and machine learning analysis. We evaluate several hypotheses and find that the availability of big data is able to facilitate more accurate predictions. Furthermore, we find that spatial aspects have more influence than temporal ones and that careful choice of thresholding parameters is crucial for high performance classification.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1019-1024
Number of pages6
ISBN (Electronic)9781479999255
DOIs
Publication statusPublished - 22 Dec 2015
Externally publishedYes
EventIEEE International Conference on Big Data (Big Data) 2015 - Santa Clara, United States of America
Duration: 29 Oct 20151 Nov 2015
Conference number: 3rd
http://cci.drexel.edu/bigdata/bigdata2015/

Conference

ConferenceIEEE International Conference on Big Data (Big Data) 2015
Abbreviated titleIEEE Bigdata 2015
CountryUnited States of America
CitySanta Clara
Period29/10/151/11/15
Internet address

Keywords

  • big data
  • time series prediction
  • traffic forecasting

Cite this

Schimbinschi, F., Nguyen, X. V., Bailey, J., Leckie, C., Vu, H., & Kotagiri, R. (2015). Traffic forecasting in complex urban networks: Leveraging big data and machine learning. In Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 (pp. 1019-1024). [7363854] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BigData.2015.7363854