Algorithmic modelling is transforming mobility decisions in smart cities, yet new research warns that it may inadvertently deepen socioeconomic disparities. This study seeks to balance efficiency with equity, offering guidelines to help policymakers ensure fairness in urban and transportation planning.
This study reveals that human travel patterns are highly predictable, driven by basic needs and social influences, and shaped by factors from personal preferences to global disruptions like pandemics. By examining variations in time, activity, and location—known as "predictability states"—the research identifies unique contextual and activity signatures, which could improve short- and long-term mobility predictions, even with limited or incomplete data.
In this paper, we apply entropy and predictability measures to analyse and bound the predictive information of an individual's mobility pattern and the flow of that information from their top social ties and from their non-social colocators.
The scientific analysis of the regularities observed in individual and collective human movement trajectories is of fundamental relevance to a wide range of areas urban planning, the prevention of epidemics, and natural security issues such as detection of clandestine activity, to name but a few. The ubiquity of mobile phones and location-based social media has enabled the capture of comprehensive, time-resolved individual information, offering a unique opportunity to observe human activity on an unprecedented scale. Indeed, recent theoretical developments suggest that a perfect algorithm can predict a person's whereabouts with almost 90% certainty, given past observations of their location visits. Yet, major gaps remain in our understanding of human mobility dynamics.