TY  - JOUR
PY  - 2020//
TI  - Robust methods and conditional expectations for vehicular traffic count analysis
JO  - European transport research review
A1  - Kilpi, Jorma
A1  - Norros, Ilkka
A1  - Kuusela, Pirkko
A1  - Malin, Fanny
A1  - Räty, Tomi
SP  - e10
EP  - e10
VL  - 12
IS  - 1
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.<p />  <p>Language: en</p>
LA  - en
SN  - 1867-0717
UR  - http://dx.doi.org/10.1186/s12544-020-0399-8
ID  - ref1
ER  -