Using Digital Footprints for a City-Scale Traffic Simulation

This article introduces a microsimulation of urban traffic flows within a large-scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys that only partly cover shopping, leisure, or recreational trips. To account for the latter, the presented traffic modeling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These datasets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals’ decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities, and a list of typical activity sequences of city dwellers collected within a national travel survey, the developed microsimulation reproduces not only the journey statistics such as peak travel periods but also the traffic volumes at main road segments with surprising accuracy.