TY - JOUR
T1 - Efficient evaluation of stochastic traffic flow models using Gaussian process approximation
AU - Storm, Pieter Jacob
AU - Mandjes, Michel
AU - van Arem, Bart
PY - 2022
Y1 - 2022
N2 - This paper studies a Gaussian process approximation for a class of stochastic traffic flow models. It can be used to efficiently and accurately evaluate the joint (in the spatial and temporal sense) distribution of vehicle-density distributions in road traffic networks of arbitrary topology. The Gaussian approximation follows, via a scaling-limit argument, from a Markovian model that is consistent with discrete-space kinematic wave models. We describe in detail how this formal result can be converted into a computational procedure. The performance of our approach is demonstrated through a series of experiments that feature various realistic scenarios. Moreover, we discuss the computational complexity of our approach by assessing how computation times depend on the network size. We also argue that the (debatable) assumption that the vehicles’ headways are exponentially distributed does not negatively impact the accuracy of our approximation.
AB - This paper studies a Gaussian process approximation for a class of stochastic traffic flow models. It can be used to efficiently and accurately evaluate the joint (in the spatial and temporal sense) distribution of vehicle-density distributions in road traffic networks of arbitrary topology. The Gaussian approximation follows, via a scaling-limit argument, from a Markovian model that is consistent with discrete-space kinematic wave models. We describe in detail how this formal result can be converted into a computational procedure. The performance of our approach is demonstrated through a series of experiments that feature various realistic scenarios. Moreover, we discuss the computational complexity of our approach by assessing how computation times depend on the network size. We also argue that the (debatable) assumption that the vehicles’ headways are exponentially distributed does not negatively impact the accuracy of our approximation.
KW - Efficient evaluation
KW - Gaussian approximation
KW - Road traffic networks
KW - Stochastic traffic flow models
KW - Traffic flow theory
UR - http://www.scopus.com/inward/record.url?scp=85137621861&partnerID=8YFLogxK
U2 - 10.1016/j.trb.2022.08.003
DO - 10.1016/j.trb.2022.08.003
M3 - Article
AN - SCOPUS:85137621861
SN - 0191-2615
VL - 164
SP - 126
EP - 144
JO - Transportation Research Part B: Methodological
JF - Transportation Research Part B: Methodological
ER -