Statistical physics of urban mobility

Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and urban planning. The predictive performance of models in the literature varies quite broadly, from over 90% to under 40%. In the first part of my talk, I review the role of data and mobility metrics in the accuracy of mobility predictions. We discuss how a few mechanisms are important factors in our ability to predict human mobility. 

Then I combine two well-known approaches in network and transportation science: (i) The macroscopic fundamental diagram (MFD), which examines the characteristics of urban traffic flow at the network level, including the relationship between flow, density, and speed. (ii) Percolation theory, which investigates the topological and dynamical aspects of complex networks, including traffic networks. Combining these two approaches, we find that the maximum number of congested clusters and the maximum MFD flow occur at the same moment, precluding network percolation (i.e. traffic collapse). These insights describe the transition of the average network flow from the uncongested phase to the congested phase in parallel with the percolation transition from sporadic congested links to a large, congested cluster of links. 

Lastly, I use individual mobility metrics to measure the urban spatial structure. While it usually evolves slowly, it can change fast during large-scale emergency events, as well as due to urban renewal in rapidly developing countries. This work presents an approach to delineate such urban dynamics in quasi-real-time through a human mobility metric, the mobility centrality index. As a case study, we tracked the urban dynamics of eleven Spanish cities during the COVID-19 pandemic. Results revealed that their structures became more monocentric during the lockdown in the first wave, but kept their regular spatial structures during the second wave. To provide a more comprehensive understanding of mobility from home, we also introduce a dimensionless metric, which measures the extent of home-based travel and provides statistical insights into the transmission of COVID-19. By utilizing individual mobility data, our metrics enable the detection of changes in the urban spatial structure.