Comparison of interpolation techniques for state estimation on urban networks

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    State estimation is an important instrument for understanding the daily urban system and its spatial and temporal dynamics. With these insights we are able to better predict future traffic states and improve the demand-supply match. If state estimation is performed real-time it can be used for short term prediction and virtual patrolling. Most earlier research focuses on highways, while less is known about urban networks because of less available measurements and urban networks are more complex. Therefore we compared two promising slightly adapted but relatively simple, scalable and fast spatial interpolation methods, respectively a simplified form of Variance-Based Interpolation (VBI) and Learning-Database Interpolation (LDI), for an urban network using floating car data based
    on a micro simulation providing the ground truth. The performance of these methods was assessed depending on penetration rate compared with a reference situation of no interpolation. The results show that the VBI method performs reasonably well up to 9% coverage, but at higher penetration rates performs worse than the reference situation. The performance of LDI is much more promising, at low penetration rates it already shows large improvement and it continues to outperform the reference situation up to 40% of FCD coverage.
    Original languageEnglish
    Number of pages11
    Publication statusPublished - 2017
    EventXIV NECTAR International Conference 2017: Transport in a networked society - Polytechnic University of Madrid (UPM), Madrid, Spain
    Duration: 31 May 20172 Jun 2017
    Conference number: 14


    ConferenceXIV NECTAR International Conference 2017
    Abbreviated titleNECTAR 2017
    Internet address


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