References of "Vanhoof, Koen"
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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 detailAn integrated micro-simulation modeling framework to measure and predict emissions and dynamic exposure
Janssens, Davy; Cools, Mario ULg; Vanhoof, Koen et al

Conference (2010)

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See detailThe presentation of an integrated microsimulation modeling framework to measure and predict emissions and dynamic exposure
Janssens, Davy; Beckx, C.; Cools, Mario ULg et al

in Proceedings of the Next Generation Data Summit 2009 (2009)

In this paper, an integrated modelling methodology for the assessment of population exposure to air pollution, involving all compartments of the DPSIR-concept, is illustrated by an application in The ... [more ▼]

In this paper, an integrated modelling methodology for the assessment of population exposure to air pollution, involving all compartments of the DPSIR-concept, is illustrated by an application in The Netherlands. The application demonstrates the advantages of an activity-based approach by presenting three kinds of applications: the calculation of vehicle emissions, the simulation of pollutant concentration patterns and the assessment of the population exposure to air population. Understanding exposure variations among activities and subpopulations can be very useful for scientific and policy purposes: it can provide information on locations or population groups most at risk, or can indicate where and when the largest exposure values occur. [less ▲]

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