References of "Perrakis, Konstantinos"
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See detailBayesian inference for transportation origin–destination matrices: the Poisson–inverse Gaussian and other Poisson mixtures
Perrakis, Konstantinos; Karlis, Dimitris; Cools, Mario ULg et al

in Journal of the Royal Statistical Society. Series A Statistics in Society (in press)

Transportation origin–destination analysis is investigated through the use of Poisson mixtures by introducing covariate-based models which incorporate different transport modelling phases and also allow ... [more ▼]

Transportation origin–destination analysis is investigated through the use of Poisson mixtures by introducing covariate-based models which incorporate different transport modelling phases and also allow for direct probabilistic inference on link traffic based on Bayesian predictions. Emphasis is placed on the Poisson–inverse Gaussian model as an alternative to the commonly used Poisson–gamma and Poisson–log-normal models. We present a first full Bayesian formulation and demonstrate that the Poisson–inverse Gaussian model is particularly suited for origin–destination analysis because of its desirable marginal and hierarchical properties. In addition, the integrated nested Laplace approximation is considered as an alternative to Markov chain Monte Carlo sampling and the two methodologies are compared under specific modelling assumptions. The case-study is based on 2001 Belgian census data and focuses on a large, sparsely distributed origin–destination matrix containing trip information for 308 Flemish municipalities. [less ▲]

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See detailQuantifying Input Uncertainty in Traffic Assignment Models
Perrakis, Konstantinos; Cools, Mario ULg; Karlis, Dimitris et al

in Proceedings of the 91st Annual Meeting of the Transportation Research Board (DVD-ROM) (2012)

Traffic assignment methods distribute Origin-Destination (OD) flows throughout the links of a given network according to procedures related to specific deterministic or stochastic modeling assumptions. In ... [more ▼]

Traffic assignment methods distribute Origin-Destination (OD) flows throughout the links of a given network according to procedures related to specific deterministic or stochastic modeling assumptions. In this paper, we propose a methodology that enhances the information provided from traffic assignment models, in terms of delivering stochastic estimates for traffic flows on links. Stochastic variability is associated to the initial uncertainty related to the OD matrix used as input into a given assignment method, and therefore the proposed methodology is not constrained by the choice of the assignment model. The methodology is based on Bayesian estimation methods which provide a suitable working framework for generating multiple OD matrices from the corresponding predictive distribution of a given statistical model. Predictive inference for link flows is then straightforward to implement, either by assigning summarized OD information or by performing multiple assignments. Interesting applications arise in a natural way from the proposed methodology, as is the identification and evaluation of critical links by means of probability estimates. A real-world application is presented for the road network of the northern, Dutch-speaking region of Flanders in Belgium, under the assumption of a deterministic user equilibrium model. [less ▲]

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See detailA Bayesian approach for modeling origin–destination matrices
Perrakis, Konstantinos; Karlis, Dimitris; Cools, Mario ULg et al

in Transportation Research. Part A : Policy & Practice (2012), 46(1), 200212

The majority of origin destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel ... [more ▼]

The majority of origin destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel census studies, in contrast, covers the entire population of a specific study area of interest. In such cases where reliable historical data exist, statistical methodology may serve as a flexible alternative to traditional travel demand models by incorporating estimation of trip-generation, trip-attraction and trip-distribution in one model. In this research, a statistical Bayesian approach on OD matrix estimation is presented, where modeling of OD flows derived from census data, is related only to a set of general explanatory variables. A Poisson and a negative binomial model are formulated in detail, while emphasis is placed on the hierarchical Poisson-gamma structure of the latter. Problems related to the absence of closed-form expressions are bypassed with the use of a Markov Chain Monte Carlo method known as the Metropolis–Hastings algorithm. The methodology is tested on a realistic application area concerning the Belgian region of Flanders on the level of municipalities. Model comparison indicates that negative binomial likelihood is a more suitable distributional assumption than Poisson likelihood, due to the great degree of overdispersion present in OD flows. Finally, several predictive goodness-of-fit tests on the negative binomial model suggest a good overall fit to the data. In general, Bayesian methodology reduces the overall uncertainty of the estimates by delivering posterior distributions for the parameters of scientific interest as well as predictive distributions for future OD flows. [less ▲]

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See detailA Bayesian Approach for Modeling Origin-Destination Matrices
Perrakis, Konstantinos; Karlis, Dimitris; Cools, Mario ULg et al

in Proceedings of the 90th Annual Meeting of the Transportation Research Board (DVD-ROM) (2011)

The majority of Origin Destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel ... [more ▼]

The majority of Origin Destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel census studies, in contrast, covers the entire population of a specific study area of interest. In such cases where reliable historical data exist, statistical methodology may serve as a flexible alternative to traditional travel demand models by incorporating estimation of trip-generation, trip-attraction and trip-distribution in one model. In this research, a statistical Bayesian approach on OD matrix estimation is presented, where modeling of OD flows, derived from census data, is related only to a set of general explanatory variables. The assumptions of a Poisson model and of a Negative-Binomial model are investigated on a realistic application area concerning the region of Flanders on the level of municipalities. Problems related to the absence of closed-form expressions are bypassed with the use of a Markov Chain Monte Carlo algorithm, known as the Metropolis-Hastings algorithm. Additionally, a strategy is proposed in order to obtain predictions from the hierarchical, Poisson-Gamma structure of the Negative-Binomial model conditional on the posterior expectations of the mixing parameters. In general, Bayesian methodology reduces the overall uncertainty of the estimates by delivering posterior distributions for the parameters of scientific interest as well as predictive distributions for future OD flows. Predictive goodness-of-fit tests suggest a good fit to the data and overall results indicate that the approach is applicable on large networks, with relatively low computational and explanatory data-gathering costs. [less ▲]

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