Hybrid traffic state estimation and prediction using pattern recognition

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Abstract

Traffic state estimation is an important task that has attracted a lot of research effort in recent decades. The main goal of traffic state estimation is to turn measured data, which is normally noisy and incomplete, into meaningful information for further investigation, either offline or online (e.g. traffic management and control).
Original languageEnglish
Title of host publicationhEART 2017
Subtitle of host publication6th Symposium of the European Association for Research in Transportation
Number of pages3
Publication statusPublished - 2017
EventhEART 2017: 6th Symposium of the European Association for Research in Transportation - Technion Institute of Technology, Haifa, Israel
Duration: 12 Sep 201714 Sep 2017
https://heart2017.net.technion.ac.il/
https://heart2017.net.technion.ac.il/files/2017/09/hEART2017FullProgram_v3.pdf

Conference

ConferencehEART 2017
CountryIsrael
CityHaifa
Period12/09/1714/09/17
Internet address

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  • Cite this

    Nguyen, T., Calvert, S., & van Lint, H. (2017). Hybrid traffic state estimation and prediction using pattern recognition. In hEART 2017: 6th Symposium of the European Association for Research in Transportation