Incremental unscented Kalman filter for real-time traffic estimation on motorways using multi-source data

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

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)


Better traffic estimation can be achieved by integrating multiple data sources. However, it is not an easy task due to many issues such as differences in formats, spatio-temporal resolutions, availability and reliability. In this study, we developed an incremental Unscented Kalman Filter (UKF) to effectively deal with data from multiple sources for a real-time motorway traffic estimation problem. The estimates produced by our model were compared with those from the incremental Extended Kalman Filter (EKF). The results showed similar performance between the incremental UKF and the incremental EKF, but our proposed framework proved to be more reliable due to smaller variance estimates, particularly during free-flow periods. The framework was also applied to estimate flow and speed in cases where data were incomplete. It has been shown that by combining multiple data sources, the filter can compensate for the deficiency of each source to produce more accurate estimates.

Original languageEnglish
Pages (from-to)1127-1153
Number of pages27
JournalTransportmetrica A: Transport Science
Issue number3
Publication statusPublished - 2022


  • data fusion
  • incremental UKF
  • macroscopic modeling
  • motorway traffic dynamics
  • Traffic state estimation

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