A comparative study on filtering methods for online freeway traffic estimation using heterogeneous data

Xuan Sy Trinh, Dong Ngoduy, Mehdi Keyvan-Ekbatani, Blair Robertson

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

5 Citations (Scopus)


The paper addresses the problem of fusing multiple data sources in freeway networks. The incremental Unscented Kalman Filter (UKF) and the Unscented Information Filter (UIF) are developed based on cell-transmission model (CTM) of freeway traffic. The efficiency of the aforementioned methods are compared by applying to a toy network with the synthetic data obtained from microscopic traffic simulation. The results show that both are capable of fusing data with sufficient accuracy, even when only a small fraction of traffic information is provided in the data. However, the UKF works better in the case of correct noise covariances while the UIF has better performance when the covariances are incorrect.

Original languageEnglish
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538670248
Publication statusPublished - Oct 2019
Externally publishedYes
EventIEEE Conference on Intelligent Transportation Systems 2019 - Auckland, New Zealand
Duration: 27 Oct 201930 Oct 2019
Conference number: 22nd
https://ieeexplore.ieee.org/xpl/conhome/8907344/proceeding (Proceedings)

Publication series

Name2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019


ConferenceIEEE Conference on Intelligent Transportation Systems 2019
Abbreviated titleITSC 2019
Country/TerritoryNew Zealand
Internet address

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