FitFun: A Modelling Framework for Successfully Capturing the Functional
Form and Noise of Observed Traffic Flow-Density-Speed Relationships
Abstract
Measurements of the average properties of vehicular traffic are
inherently noisy. The distributions of flow and speed measurements at
any particular density are non-Gaussian with density-dependent variance,
skewness, and kurtosis. Previous studies have failed to properly account
for these complicated noise properties. In remediation, we present
FitFun, a general framework for modelling any observed
flow-density-speed relationship. Models specified within FitFun
incorporate components for both the functional form and the noise. We
define three flexible noise model components and we fit 200 different
models to a high-quality sample of 10,150 observed urban flow-occupancy
relationships. We compare the fits using information criteria and assess
fit quality through analysis of the residuals. We find that the
non-parametric Sun model for the functional form component combined with
a Skew Exponential Power Type III noise component significantly
outperforms all of the other models. Interestingly, we find that the
city, country, road topology, and detector location have virtually no
impact on model performance and fit quality, which is very convenient
for model selection. The only factor of relevance from those that we
studied is the effective occupancy coverage of the data. We conclude
that certain models specified judiciously within FitFun can successfully
capture the functional form and noise of observed flow-density-speed
relationships without the need to discard data taken during
non-stationary conditions. This is particularly advantageous for urban
data where stationary traffic conditions are rarely observed.
Accepted by Transportation Research Part C on 16th Feb 2023.Jun 2023Published in Transportation Research Part C: Emerging Technologies volume 151 on pages 104068. 10.1016/j.trc.2023.104068