Abstract This paper proposes a novel approach that combines theory- and data-driven choice models using Artificial Neural Networks (ANNs). In particular, we use continuous vector representations, called embeddings, for encoding categorical or discrete explanatory variables with a special focus on interpretability and model transparency. Although embedding representations within the logit framework have been conceptualized by Pereira (2019), their dimensions do not have an absolute definitive meaning, hence offering limited behavioral insights in this earlier work. The novelty of our work lies in enforcing interpretability to the embedding vectors by formally associating each of their dimensions to a choice alternative. Thus, our approach brings benefits much beyond a simple parsimonious representation improvement over dummy encoding, as it provides behaviorally meaningful outputs that can be used in travel demand analysis and policy decisions. Additionally, in contrast to previously suggested ANN-based Discrete Choice Models (DCMs) that either sacrifice interpretability for performance or are only partially interpretable, our main model (E-MNL) preserves interpretability of the utility coefficients for all the input variables despite being based on ANN principles. The proposed models are tested on two real world datasets and evaluated against benchmark and baseline models that use dummy-encoding. The results of the experiments indicate that our models deliver state-of-the-art predictive performance, outperforming existing ANN-based models while drastically reducing the number of required network parameters.

    Highlights Interpretable embedding representations for categorical or discretized variables. State-of-the-art predictive performance with fewer required parameters. Imposed constraints formally associate embeddings dimensions to choice alternatives. Behaviorally meaningful outputs that can be used for diagnostic & analysis purposes.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Combining discrete choice models and neural networks through embeddings: Formulation, interpretability and performance


    Beteiligte:


    Erscheinungsdatum :

    2023-06-13




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability

    Han, Yafei / Pereira, Francisco Camara / Ben-Akiva, Moshe et al. | Elsevier | 2022



    Transportation networks equilibration with discrete choice models.

    Sheffi, Yosef, 1948- | DSpace@MIT | 1978

    Freier Zugriff


    Contextual Semantic Interpretability

    Marcos, Diego / Fong, Ruth / Lobry, Sylvain et al. | British Library Conference Proceedings | 2021