Personalized Experience-aware Multi-criteria Route Selection for Smart Mobility

Abstract

Smart urban mobility emerged from the urban citizen’s need for a fast urbanization environment, using personal devices and city infrastructure integration, data generation, and mobility services provided on congested and possibly dangerous urban roads. However, traditional routing services need to consider users’ experience, comfort and health because they usually choose only routes with the shortest paths or less traffic. This work proposes a route selection method based on a personalized preference for different user profiles, and essential geolocated factors from data collection, including crime occurrences and air quality factors. The suggestion method allows safer, healthier, and more pleasant paths for drivers and analytic data for city planners compared to single-criteria route selection approaches.