Personalized Route wselection methods in a urban computing scenario

Abstract

With population growth in urban areas, the more extensive city infrastructure faces several problems affecting the population’s health and quality of life. Urban mobility changes became intense since the worldwide technological revolution brought many tools and methods to prevent harmful tendencies regarding urban transportation. In this context, Internet of Things (IoT) solutions perform a ubiquitous way of sensing the population mobility and the local mobility context as criminality, accidents, and air quality near the road infrastructure, complementing the city mobility. Likewise, Location-based Social Networks (LBSN) dispose of users’ geolocated data, allowing the identification of mobility patterns, traffic flows, and alternative modal transport recommendations. In this matter, novel mobility solutions must attend city issues regarding public transportation, criminality, traffic influencing factors, and air quality compromising. Also, route selection methods must consider comfort features, making more pleasant urban trips. This mas ter’s dissertation proposes and evaluates two pollution-aware route selection approaches,a multi-modal hybrid routes method and a multi-criteria personalized route selection method, for urban citizens’ mobility flow improvement. The hybrid multi-modal solutionsurpasses the single-modal, offering less expensive and less polluted trip options. The multi-criteria solution personalized profiles outperform the single-criterion choice in the same context considering all calculated route possibilities