A Data Driven Approach to Match Demand and Supply for Public Transport Planning

The estimation of OD flows from mobile phone and GPS positioning data is an important application that can naturally support urban and transport studies. In this work, we first present an approach to generate OD matrices from mobile phone positioning and GPS data, and scale them with traffic counts. Then we compare these matrices, that we consider as an estimate of the potential demand for public transport, with matrices describing the actual routes of public transportation services, that represent the supply. Finally, we present a data driven approach to identify
where
and
when
the demand for transport is not satisfied. We run experiments with different mobility datasets and the actual public transportation routes in a mid-sized Italian city. In this scenario, our approach allows to detect similar areas of unmatched demand with both such datasets. In particular, two case studies show that the proposed methodology is able to identify two existing bus lines that were recently introduced by the public transport company and local government. Finally, we show an upper bound for the reduced impact of
$CO_{2}$
emissions, if the unmet demand for transport is entirely shifted to public transport.