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

Abstract

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.

Measuring the impact of COVID-19 confinement measures on human mobility using mobile positioning data. A European regional analysis

“…In compliance with the ‘Guidelines on the use of location data and contact tracing tools in the context of the COVID-19 outbreak’ by the European Data Protection Board ( EDPB, 2020 ), these data do not provide information about the behaviour of individuals; it can, however, give valuable insights into mobility patterns of population groups. These data received by the JRC are in the form of Origin-Destination Matrix (ODM) ( Mamei et al, 2019 , Fekih et al, 2020 ). This section briefly describes what an ODM is; the following section illustrates how the Joint Research Centre builds mobility indicators from the ODMs.…”

Human mobility and COVID-19 initial dynamics

“…Data from MNOs are provided to JRC in the form of origin-destination matrices (ODMs) [7,20]. Each cell [i − j] of the ODM shows the overall number of 'movements' (also referred to as 'trips' or 'visits') that have Despite the fact that the ODMs provided by different MNOs have similar structure, they are often very heterogeneous.…”

“…Data from MNOs are provided to JRC in the form of origin–destination matrices (ODMs) [ 7 , 20 ]. Each cell of the ODM shows the overall number of ‘movements’ (also referred to as ‘trips’ or ‘visits’) that have been recorded from the origin geographical reference area i to the destination geographical reference area j over the reference period.…”

“…The authors would also like to acknowledge the GSMA 4 (GSMA is the GSM Association of mobile network operators. ), colleagues from DG CONNECT 5 (DG Connect: The Directorate-General for Communications Networks, Content and Technology is the European Commission department responsible to develop a digital single market to generate smart, sustainable and inclusive growth in Europe) for their support and colleagues from Eurostat 6 (Eurostat is the Statistical Office of the European Union) and ECDC 7 (ECDC: European Centre for Disease Prevention and Control. An agency of the European Union) for their input in drafting the data request.…”

Anomaly detection of mobile positioning data with applications to COVID-19 situational awareness

“…The data received by the JRC are in the form of ODM: Origin-Destination Matrix (Mamei et al 2019;Fekih et al 2020;Kishore et al 2020). Although the concept is somehow known to the general public, it is important to describe their nature to justify why the anomaly detection system of Sect.…”