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  • 1.
    Flötteröd, Gunnar
    et al.
    École Polytechnique Fédérale de Lausanne (EFPL), School of Architecture, Civil and Environmental Engineering (ENAC), Lausanne, Switzerland.
    Bierlaire, Michel
    Nagel, Kai
    Bayesian demand calibration for dynamic traffic simulations2011In: Transportation Science, ISSN 0041-1655, E-ISSN 1526-5447, Vol. 45, no 4, p. 541-561Article in journal (Refereed)
    Abstract [en]

    We present an operational framework for the calibration of demand models for dynamic traffic simulations, where calibration refers to the estimation of a structurally predefined model's parameters from real data. Our focus is on disaggregate simulators that represent every traveler individually. We calibrate, also at an individual level, arbitrary choice dimensions within a Bayesian framework, where the analyst's prior knowledge is represented by the dynamic traffic simulator itself and the measurements are comprised of time-dependent traffic counts. The approach is equally applicable to an equilibrium-based planning model and to a telematics model of spontaneous and imperfectly informed drivers. It is based on consistent mathematical arguments, yet it is applicable in a purely simulation-based environment and, as our experimental results show, is capable of handling large scenarios.

  • 2.
    Lindberg, Per Olov
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Computer Science and Communication (CSC), Numerical Analysis, NA (closed 2012-06-30).
    Mitradjieva, Maria
    Linköpings Universitet.
    The Stiff is Moving - Conjugate Direction Frank-Wolfe Methods with Applications to Traffic Assignment2012In: Transportation Science, ISSN 0041-1655, E-ISSN 1526-5447, Vol. 47, no 2, p. 280-293Article in journal (Refereed)
    Abstract [en]

    We present versions of the Frank-Wolfe method for linearly constrained convex programs, in which consecutive search directions are made conjugate. Preliminary computational studies in a MATLAB environment applying pure Frank-Wolfe, Conjugate direction Frank-Wolfe (CFW), Bi-conjugate Frank-Wolfe (BFW) and ”PARTANized” Frank-Wolfe methods to some classical Traffic Assignment Problems show that CFW and BFW compare favorably to the other methods. This spurred a more detailed study, comparing our methods to Bar-Gera’s origin-based algorithm. This study indicates that our methods are competitive for accuracy requirements suggested by Boyce et al. We further show that CFW is globally convergent. We moreover point at independent studies by other researchers that show that our methods compare favourably with recent bush-based and gradient projection algorithms on computers with several cores.

  • 3. Osorio, Carolina
    et al.
    Flötteröd, Gunnar
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model2015In: Transportation Science, ISSN 0041-1655, E-ISSN 1526-5447, Vol. 49, no 2, p. 420-431Article in journal (Refereed)
    Abstract [en]

    This work adds realistic dependency structure to a previously developed analytical stochastic network loading model. The model is a stochastic formulation of the link-transmission model, which is an operational instance of Newell's simplified theory of kinematic waves. Stochasticity is captured in the source terms, the flows, and, consequently, in the cumulative flows. The previous approach captured dependency between the upstream and downstream boundary conditions within a link (i.e., the respective cumulative flows) only in terms of time-dependent expectations without capturing higher-order dependency. The model proposed in this paper adds an approximation of full distributional stochastic dependency to the link model. The model is validated versus stochastic microsimulation in both stationary and transient regimes. The experiments reveal that the proposed model provides a very accurate approximation of the stochastic dependency between the link's upstream and downstream boundary conditions. The model also yields detailed and accurate link state probability distributions.

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