Personalized and Situation-Aware Multimodal Route Recommendations: The FAVOUR Algorithm

• Published in: IEEE transactions on intelligent transportation systems 2017-01, Vol.18 (1), p.92-102
• Main Authors: Campigotto, Paolo, Rudloff, Christian, Leodolter, Maximilian, Bauer, Dietmar
• Format: Article
• Language: English
• Subjects: Algorithms; Bayes methods; Bayesian analysis; Bayesian learning; Context; Machine learning; multimodal routing; Personalized-route recommendations; preference elicitation; Preferences; Proposals; Recommender systems; Route selection; Routing; Situational awareness; transfer learning; Travel time; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2016.2565643

Route choice in multimodal networks shows a considerable variation between different individuals and the current situational context. Personalization and situation awareness of recommendation algorithms are already common in many areas, e.g., online retail. However, most online routing applications still provide shortest distance or shortest travel-time routes only, neglecting individual preferences, as well as the current situation. Both aspects are of particular importance in a multimodal setting as attractivity of some transportation modes, such as biking, which crucially depend on personal characteristics and exogenous factors, such as the weather. As an alternative, this paper introduces the FAVourite rOUte Recommendation (FAVOUR) approach to provide personalized, situation-aware route proposals based on three steps: first, at the initialization stage, the user provides limited information (home location, work place, mobility options, sociodemographics) used to select one out of a small number of initial profiles. Second, based on this information, a stated preference survey is designed in order to sharpen the profile. In this step, a mass preference prior (MPP) is used to encode the prior knowledge on preferences from the class identified in step one. Third, subsequently, the profile is continuously updated during usage of the routing services. The last two steps use Bayesian learning techniques in order to incorporate information from all contributing individuals. The FAVOUR approach is presented in detail and tested on a small number of survey participants. The experimental results on this real-world dataset show that FAVOUR generates better quality recommendations w.r.t. alternative learning algorithms from the literature. In particular, the definition of the MPP for initialization of step two is shown to provide better predictions than a number of alternatives from the literature.