Personalized Route Selection Methods in an Urban Computing Scenario

With population growth in urban areas, the more extensive city infrastructure faces several problems affecting the population’s health and quality of life. In this context, smart urban mobility solutions perform a ubiquitous way of sensing the population mobility and the local mobility context, such 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 and alternative modal transport recommendations. This work develops two pollutionaware route selection approaches, a multi-modal hybrid routes method and a multi-criteria personalized route selection method, for urban citizens’ mobility flow improvement, attending to the urban mobility overload and deficiency. The hybrid multi-modal solution surpasses the single-modal, offering less expensive and less polluted trip options. Considering all calculated route possibilities, the multi-criteria personalized profile solution outperforms the single-criterion choice in the same context.