Dynamic learning-based Search for Multi-criteria Itinerary Planning

Abstract

Travelers expect integrated and multimodal itinerary planning while addressing their individual expectations. Besides common preferences such as travel time and price, further criteria such as walking and waiting times are of importance as well. The competing features of these preferences yield a variety of Pareto-optimal itineraries. Finding the set of Pareto-optimal multimodal travel itineraries in efficient run time remains a challenge in case multiple traveler preferences are considered. In this work, we present a sampling framework to approximate the set of Pareto-optimal travel itineraries that scales well in terms of considered preferences. In particular, we guide the search process dynamically to uncertain areas of the complex multimodal solution space. To this end, we learn the structure of the Pareto front during the search with Gaussian Process Regression (GPR). The GPR sampling framework is evaluated integrating an extensive amount of real-world data on mobility services. We analyze long-distance trips between major cities in Germany. Furthermore, we take up to five traveler preferences into account. We observe that the framework performs well, revealing origin and destination specifics of Pareto fronts of multimodal travel itineraries.