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A Link-Based Stochastic Traffic Assignment Model for Travel Time Reliability Estimation

In: Network Reliability in Practice

Author

Listed:
  • Chong Wei

    (Tokyo Institute of Technology)

  • Yasuo Asakura

    (Tokyo Institute of Technology)

  • Takamasa Iryo

    (Kobe University)

Abstract

This study proposes a link-based stochastic traffic assignment model that aims to capture the stochastic nature of link traffic flow, and the output of the model is the probability distribution of link traffic flows. We consider the link traffic flow variables as random variables. The distribution of the random variables is formulated as a conditional probability distribution for a given assumption: the traffic network is in stochastic user equilibrium. The conditional probability distribution is deduced from a Bayesian theorem, referred to as posterior probability distribution. A Markov chain Monte Carlo(MCMC) method is applied to simulate samples from the posterior distribution. Characteristics such as the means and variances of link traffic flows as well as travel time reliability are estimated from the simulated samples.

Suggested Citation

  • Chong Wei & Yasuo Asakura & Takamasa Iryo, 2012. "A Link-Based Stochastic Traffic Assignment Model for Travel Time Reliability Estimation," Transportation Research, Economics and Policy, in: David M. Levinson & Henry X. Liu & Michael Bell (ed.), Network Reliability in Practice, edition 1, chapter 0, pages 209-221, Springer.
    • Handle: RePEc:spr:trachp:978-1-4614-0947-2_12
    DOI: 10.1007/978-1-4614-0947-2_12
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