A Mobility Model for Synthetic Travel Demand from Sparse Individual Traces

Preprint, 2021

Knowing how much people travel is essential for lowering carbon emissions in the transport sector. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and social media data have been used widely to study mobility patterns. However, these traces suffer from sparsity, an issue that has largely been overlooked. In order to extend the use of these low-cost and easy-to-access data, this study proposes an individual-based mobility model to fill the gaps in sparse mobility traces. The proposed model applies the fundamental mechanisms of exploration and preferential return to synthesise mobility to generate trips, designed to accommodate the sparse individual traces of geolocated social media data. We validated our model and found good agreement on origin-destination matrices and trip distance distributions for Sweden, the Netherlands, and São Paulo, Brazil. The proposed model can be used to synthesise mobility at any geographic scale, and the results can later be applied to modelling travel demand. We further apply the model to characterise domestic trip distances for a mixture of cities and countries globally. The trip distance distributions from the model-synthesised trips using sparse geolocations from 22 regions largely follow lognormal distributions and they reflect reasonable characteristics of regional heterogeneity. Further exploration is needed to understand the regional differences between the 22 cities and countries tested.