The statistical properties of human mobility have been studied in the framework of complex systems physics. Taking advantage of the new datasets made available by the information and communication technologies, the distributions of mobility path lengths and of trip durations have been considered to discover the fingerprints of complexity characters, but the role of the different transportation means on the statistical properties of urban mobility has not been studied in depth. In this paper, we cope with the problem of the existence of universal features for pedestrian, bike and vehicular urban mobility. In particular, we propose the use of travel time as the universal energy for the mobility and we define a simple survival model that explains the travel time distribution of the different mobility types. The analysis is performed in the metropolitan area of Bologna (Italy), where GPS datasets were available on individual trips using different transport means. Our results could be helpful for the realization of multimodal sustainable mobility in future cities, compatible with the citizen’s propensities to use different transport means.

The statistical properties of human mobility have been studied in the framework of complex systems physics. Taking advantage from the new datasets made available by the information and communication technologies, the distributions of mobility path lengths and of trip duration have been considered to discover the fingerprints of complexity characters, but the role of the different transportation means on the statistical properties of urban mobility has not been studied in deep. In this paper we cope with the problem of pointing out the existence of universal features for different type of individual mobility: pedestrian, cycling and vehicular urban mobility.In particular, we propose the use of travel time as universal 'energy' for the mobility and we define a simple survival model that explains the travel time distribution of the different types of mobility. the analysis is performed in the metropolitan area of Bologna (Italy), where GPS datasets were available on individual trips using different transport means. Our results could suggest how to plan the different transportation networks to realize a multimodal mobility compatibly with the citizens propensities to use the different transport means.