Clustering of mobile subscriber’s location statistics for travel demand zones diversity

Abstract

Current knowledge on travel demand is necessary to keep a travel demand model up to date. However, the data gathering is a laborious and costly task. One of the approaches to this issues can be the utilisation of mobile data. In this work, we used mobile subscriber’s location statistics to define a daily characteristic of mobile events occurrences registered by Base Transceiver Stations (BTS). For types of preprocessed data were tested to create stable clusters of BTS according to registered routines. The obtained results were used to find similar travel demand zones from the Warsaw public transport demand model according to a daily activity of the citizens. The obtained results can be used to update the model or to plan a cohesive strategy of public transport development.