Robust methods and conditional expectations for vehicular traffic count analysis

Jorma Kilpi (Corresponding Author), Ilkka Norros, Pirkko Kuusela, Fanny Malin, Tomi Räty

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)


We study the problem of making algorithmic statistical inferences about the dynamics of city traffic. Our data is based on loop detector counts of observed vehicles in various roads in the city of Tampere, Finland. We show that meaningful correlations can be found between traffic asymmetries at different measurement locations. The traffic asymmetry is the difference of the traffic counts of the opposite directions of a road. The correlations can be further quantified by estimating how much they effect on the average values of the traffic asymmetries at the neighbouring locations. Conditional expectations, both sample and binormal model-based versions are useful tools for quantifying this effect. The uncertainty bounds of conditional expectations of the binormal model distribution are extremely useful for outlier detection. Furthermore, conditional expectations of the multinormal distribution model can be used to recover missing data with bounds to uncertainty.
Original languageEnglish
Article number10
Number of pages11
JournalEuropean Transport Research Review
Issue number1
Early online date12 Feb 2020
Publication statusPublished - 12 Feb 2020
MoE publication typeA1 Journal article-refereed


This study was funded by the Academy of Finland project 294763 Stomograph. The authors have also been supported by ECSEL MegaMaRT2 Project and by the European Union’s Horizon 2020 project Transforming Transport, grant agreement No 731932.


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