The opportunities offered by telematics development and the features required by travellers moving on more complex multimodal transit networks push to investigate new methods for generating path advice in transit trip planners. First, the study focuses on characteristics and limits of the methods used by current trip planners for path generation and then analyses the new methods applied by a new generation of trip planners (most at the prototypical developing stage). These methods use a group or, better, an individual traveller utility function, which allows personal preferences to be pointed out. As the individual utility functions in the transport modelling literature has been largely neglected, the second part of the study reports the state‐of‐the‐art and some theoretical considerations on individual path utility function modelling, and recalls the approaches developed for including individual utility in new trip planners. Further, the results of individual utility function estimations, using surveys relative to the city of Rome, are presented and some aspects of the utility function calibration, including the info provider learning process of traveller preferences, are explored. The analyses show a significant improvement in using individual functions for path advice and a substantial influence of network complexity on the learning process.
Individual utility‐based path suggestions in transit trip planners
IET Intelligent Transport Systems ; 10 , 4 ; 219-226
01.05.2016
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
utility‐based path suggestion , group utility path function , transit trip planner , path utility function modelling , complex multimodal transit network , current trip planner , transport modelling literature , info provider learning process , path generation , travel industry , path choice , path utility function estimation , planning (artificial intelligence) , learning (artificial intelligence) , individual traveller utility function , telematics development , parameter estimation , utility function specification , path advice , personal preference , traveller preference , user tracing , intelligent transportation systems , prototypical developing stage
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