Urban mobility congestion is a serious threat to the economic prosperity and way of life for individuals integrated into city infrastructures. Solutions for this problem require rethinking the time and spatial redesign of urban locations. In recent work, we developed a systemic and bi-disciplinary methodology to predict people’s willingness and flexibility to shift their working hours based on sociological criteria. This paper extends this work by developing a new approach to analyze traffic jams and proposing the best departure time for users to avoid traffic congestion and contribute to reducing urban mobility congestion. The developed methodology relies on three phases. First, using the Open Source Routing Machine (OSRM), the routes that an individual passes by to go to their workplace are generated. Second, the traffic of each route is analyzed according to different parameters (daytime, weekday, month, built environment, weather conditions, etc.) using Waze data; the time series model FB Prophet then predicts the congestion factor of different timings. Finally, the best (i.e., shortest spending travel time) departure time is extracted.