TY - JOUR
T1 - Robust methods and conditional expectations for vehicular traffic count analysis
AU - Kilpi, Jorma
AU - Norros, Ilkka
AU - Kuusela, Pirkko
AU - Malin, Fanny
AU - Räty, Tomi
N1 - Funding Information:
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.
Publisher Copyright:
© 2020, The Author(s).
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/2/12
Y1 - 2020/2/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85079346051&partnerID=8YFLogxK
U2 - 10.1186/s12544-020-0399-8
DO - 10.1186/s12544-020-0399-8
M3 - Article
VL - 12
JO - European Transport Research Review
JF - European Transport Research Review
SN - 1867-0717
IS - 1
M1 - 10
ER -