Abstract
In this paper, we present a novel approach for computing personalized itineraries for individual travel plans throughout one day, considering the wide variety of mobility preferences individuals consider when making itinerary choices. We extend the Traveling Salesman Problem with Time Windows (TSP-TW) by integrating multi-criteria optimization techniques, flexible activities, park-and-ride options, and various transport modes to provide a more comprehensive representation of transportation options. We assess travelers’ mobility preferences, selecting a relevant subset for a real-world itinerary optimization scenario, and employ choice experiments to identify the importance of these preferences for individual decision-makers. The utility functions derived from these experiments are then used for itinerary optimization. We validated our method through simulations in a medium-sized German city, which demonstrated a significant improvement of 16.19% in travel utility when incorporating a utility function into itinerary optimization compared to plans based solely on travel time.
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