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.

trip distance distribution

sparse mobility traces

travel demand

origin-destination estimation

social media data


Yuan Liao

Chalmers, Space, Earth and Environment, Physical Resource Theory

Kristoffer Ek

Chalmers, Computer Science and Engineering (Chalmers)

Eric Wennerberg

Chalmers, Computer Science and Engineering (Chalmers)

Sonia Yeh

Chalmers, Space, Earth and Environment, Physical Resource Theory

Jorge Gil

Chalmers, Architecture and Civil Engineering, Urban Design and Planning

Sustainable cities: the use of large amounts of data to understand and handle movement patterns and congestion

Formas (2016-01326), 2017-01-01 -- 2019-12-31.

Subject Categories

Other Computer and Information Science

Transport Systems and Logistics

Human Geography

Areas of Advance

Information and Communication Technology



Driving Forces

Sustainable development

Related datasets

Source code for the proposed individual mobility model. [dataset]

URI: https://github.com/TheYuanLiao/individual_mobility_model

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