The paper reports the first results of a research project for the definition of an advanced trip planner for transit networks. The project at the current stage has developed the module to support the user with personalised pre-trip information based on his/her preferences. The first part of the paper describes the user needs and the logical architecture of the trip planner. The second part deals with the theoretical aspects of the path choice model used to support the path choice set individuation, the path utility calculation and the user preference learning procedure. In order to apply the theoretical framework and to show the benefits of the proposed approach, some experimental results of a test case on the transit system of the metropolitan area of Rome are presented.
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