A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France)

Spatiotemporal data, and more specifically origin–destination matrices, are critical inputs to mobility studies for transportation planning and urban management purposes. Traditionally, high-cost and hard-to-update household travel surveys are used to produce large-scale origin–destination flow information of individuals’ whereabouts. In this paper, we propose a methodology to estimate origin–destination (O–D) matrices based on passively-collected cellular network signalling data of millions of anonymous mobile phone users in the Rhône-Alpes region, France. Unlike Call Detail Record (CDR) data which rely only on phone usage, signalling data include all network-based records providing higher spatiotemporal granularity. The explored dataset, which consists of time-stamped traces from 2G and 3G cellular networks with users’ unique identifier and cell tower locations, is used to first analyse the cell phone activity degree indicators of each user in order to qualify the mobility information involved in these records. These indicators serve as filtering criteria to identify users whose device transactions are sufficiently distributed over the analysed period to allow studying their mobility. Trips are then extracted from the spatiotemporal traces of users for whom the home location could be detected. Trips have been derived based on a minimum stationary time assumption that enables to determine activity (stop) zones for each user. As a large, but still partial, fraction of the population is observed, scaling is required to obtain an O–D matrix for the full population. We propose a method to perform this scaling and we show that signalling data-based O–D matrix carries similar estimations as those that can be obtained via travel surveys.