  In this chapter, we discuss urban mobility from a complexity science
perspective. First, we give an overview of the datasets that enable this
approach, such as mobile phone records, location-based social network traces,
or GPS trajectories from sensors installed on vehicles. We then review the
empirical and theoretical understanding of the properties of human movements,
including the distribution of travel distances and times, the entropy of
trajectories, and the interplay between exploration and exploitation of
locations. Next, we explain generative and predictive models of individual
mobility, and their limitations due to intrinsic limits of predictability.
Finally, we discuss urban transport from a systemic perspective, including
system-wide challenges like ridesharing, multimodality, and sustainable
transport.
