This article presents a methodology for improving the performance of a trip generation/attraction forecasting model by means of the Box–Cox transformation. The application of this transformation to a set of random variables aims at obtaining a set of transformed variables distributed normally. The methodology comprises two steps. First, the sample is transformed employing the parameters of the transformation that maximises the likelihood as a multivariate normal distribution. In the second step, the forecast of the dependent variable, that is, the number of trips, is produced by an analytical approximation for the mean value of that variable conditioned on the rest of the variables. The methodology is applied to three different samples consisting of road trips and of a series of socio-economic variables. The analysis of the results shows that the model based on the optimal Box–Cox transformation has a better forecasting performance than that based on the logarithmic transformation.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Improving trip forecasting models by means of the Box–Cox transformation


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    01.08.2013




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Trip Forecasting Models for Intelligent Community Vehicle Systems

    National Research Council (U.S.) | British Library Conference Proceedings | 2005





    Value of Life Cycle in Explaining Trip-Making Behavior and Improving Temporal Stability of Trip Generation Models

    Huntsinger, Leta F. / Rouphail, Nagui M. | Transportation Research Record | 2012