How predictable are macroscopic traffic states: a perspective of uncertainty quantification

Traffic condition forecasting is fundamental for Intelligent Transportation Systems. Besides accuracy, many services require an estimate of uncertainty for each prediction. Uncertainty quantification must consider the inherent randomness in traffic dynamics, the so-called aleatoric uncertainty, and the additional distrust caused by data shortage, the so-called epistemic uncertainty. They together depict how predictable macroscopic traffic is. This study uses deep ensembles of graph neural networks to estimate both types of uncertainty in network-level speed forecasting. Experimental results given by the used model reveal that, although rare congestion patterns arise randomly, the short-term predictability of traffic states is mainly restricted by the irreducible stochasticity in traffic dynamics. The predicted future state bifurcates into congested or free-flowing cases. This study suggests that the potential for improving prediction models through expanding speed and flow data is limited while diversifying data types is crucial.</p

Additional file 1: of Genome-wide comparative analysis of putative Pth11-related G protein-coupled receptors in fungi belonging to Pezizomycotina

Chromosomal distribution of putative F. graminearum Pth11-related GPCR genes. Chromosome numbers are shown at the top of the chromosomes. (TIFF 39 kb

Magnetic sensor’s ideal and real coordinates.

<p>Magnetic sensor’s ideal and real coordinates.</p

Differential value before & after the signal compensation.

<p>Differential value before & after the signal compensation.</p

Strength of geomagnetic field before & after compensation.

<p>Strength of geomagnetic field before & after compensation.</p

Estimation result of the <i>b</i>_<i>z</i> filter.

<p>Estimation result of the <i>b</i>_<i>z</i> filter.</p

Magnetic field produced by induced magnetic moment at <i>p</i> point.

<p>(A) Induction field produced by magnetic moment <i>M</i>_<i>x</i> at point <i>p</i>. (B) Induction field produced by magnetic moment <i>M</i>_<i>y</i> at point <i>p</i>. (C) Induction field produced by magnetic moment <i>M</i>_<i>z</i> at point <i>p</i>.</p

Estimation result of the <i>b</i>_<i>x</i> filter.

<p>Estimation result of the <i>b</i>_<i>x</i> filter.</p

Differential value before & after the signal compensation.

<p>Differential value before & after the signal compensation.</p

Estimation result of the <i>b</i>_<i>y</i> filter.

<p>Estimation result of the <i>b</i>_<i>y</i> filter.</p