TY - JOUR

T1 - The posterior probability distribution of traffic flow

T2 - A new scheme for the assignment of stochastic traffic flow

AU - Wei, Chong

AU - Asakura, Yasuo

AU - Iryo, Takamasa

PY - 2013/9/1

Y1 - 2013/9/1

N2 - This study proposes a new scheme for assigning traffic flows that aims to capture the stochastic nature of route traffic flows. We consider the route traffic flows to be random variables. The distribution of these random variables is formulated as a conditional probability distribution for a given assumption: the traffic network is in stochastic user equilibrium. From a Bayesian perspective, we treat the conditional distribution as a posterior distribution of route traffic flows, which is obtained using Bayes' theorem. We develop a basic Metropolis-Hastings (M-H) sampling scheme, as well as a M-H within Gibbs sampling scheme, to draw samples from the posterior distribution. We estimate characteristics such as the means and variances of route traffic flows from simulated samples. The proposed model can directly output the route traffic flows, and has a highly flexible computation process.

AB - This study proposes a new scheme for assigning traffic flows that aims to capture the stochastic nature of route traffic flows. We consider the route traffic flows to be random variables. The distribution of these random variables is formulated as a conditional probability distribution for a given assumption: the traffic network is in stochastic user equilibrium. From a Bayesian perspective, we treat the conditional distribution as a posterior distribution of route traffic flows, which is obtained using Bayes' theorem. We develop a basic Metropolis-Hastings (M-H) sampling scheme, as well as a M-H within Gibbs sampling scheme, to draw samples from the posterior distribution. We estimate characteristics such as the means and variances of route traffic flows from simulated samples. The proposed model can directly output the route traffic flows, and has a highly flexible computation process.

KW - Bayes' theorem

KW - contemporaneous model

KW - Markov chain Monte Carlo

KW - posterior distribution

KW - stochastic traffic assignment

UR - http://www.scopus.com/inward/record.url?scp=84884496205&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84884496205&partnerID=8YFLogxK

U2 - 10.1080/18128602.2012.661799

DO - 10.1080/18128602.2012.661799

M3 - Article

AN - SCOPUS:84884496205

VL - 9

SP - 753

EP - 771

JO - Transportmetrica A: Transport Science

JF - Transportmetrica A: Transport Science

SN - 2324-9935

IS - 8

ER -