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

Abstract

Route choice in multimodal networks shows a considerable variation between different individuals as well as 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 traveltime 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 crucially depends on personal characteristics and exogenous factors like 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 is used to encode the prior knowledge on preferences from the class identified in step one. And 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 betterquality recommendations w.r.t. alternative learning algorithms from the literature. In particular the definition of the mass preference prior for initialization of step two is shown to provide better predictions than a number of alternatives from the literature.

Integrated Information System for Disaster Management: A Creative Case Study

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Path Planning Strategy for Vehicle Navigation Based on User Habits

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“…Moreover, significant attention has been directed toward personalized path guidance systems. Campigotto P [20] introduces the favorite route recommendation (FAVOUR) approach to provide a personalized, situation-aware route based on the information updating through Bayesian learning, which was obtained from initial configuration files (home location, work place, mobility options, etc.). A personalized fuzzy path planning algorithm based on the fuzzy sorting of the center of gravity was proposed by Nadi [21], in which a optimization route according to user standard types was formulated through analyzing the uncertainty of user preferences through the expression of fuzzy linguistic preference relations.…”

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“…), restrições de tempo e preferências do usuário para oferecer recomendações de trajeto personalizadas [Chen et al 2015]. Esses trabalhos, em geral, consideram como interesse elementos variados, relacionados às condições de trânsito e das vias, por exemplo, distância, tempo, custo, tráfego, consumo de combustível, entre outros [Campigotto et al 2016].…”