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Article dans une revue
Conditionality and risk for the pedestrian: modelling with the Bayesian networks
Abstract : The conditional script questionnaire (CSQ) makes possible to study the conditions under which drivers find it legitimate to transgress the Highway Code. In this paper, we propose to analyse conditional respect towards the pedestrian with a new methodology based on Bayesian networks (BN). This methodology is designed to give a useful decision support tool for the analyst. Starting from data encoded in the CSQ, we use structure learning algorithms in order to build a BN. Then, we exploit it for two purposes: to extract new knowledge about the main topics expressed in the CSQ and to make inferences. This methodology helps to better understand the behaviour of drivers interacting with pedestrians and what might be the cause of their decisions of legitimate transgressions. The efficiency of the methodology proposed here is illustrated and a context-dependent 'mapping' of the legitimate transgressions established.
https://hal.univ-angers.fr/hal-02511493
Contributeur : Marie-Françoise Gerard Connectez-vous pour contacter le contributeur
Soumis le : mercredi 18 mars 2020 - 18:57:00
Dernière modification le : mercredi 27 avril 2022 - 04:28:00
Contributeur : Marie-Françoise Gerard Connectez-vous pour contacter le contributeur
Soumis le : mercredi 18 mars 2020 - 18:57:00
Dernière modification le : mercredi 27 avril 2022 - 04:28:00