Improving the generation of synthetic travel demand using origin–destination matrices from mobile phone data

Abstract

The dynamics of urban transportation can be captured using activity-based models, which rely on travel demand data to get a comprehensive understanding of urban mobility. This data is usually derived from population samples and Household Travel Surveys (HTSs), which can be expensive and as a result, are conducted only every 5 to 10 years. Moreover, due to their limited reach, they are not adapted to represent the spatio-temporal structure of the flows of the total population. This calls for complementary data sources that could be used to update old surveys to cut costs and to estimate the global spatial mobility behavior of the population. In this paper, we propose steps in the state-of-the-art pipeline for travel demand synthesis with an approach for the temporal calibration and the location attribution based on time-dependent origin–destination (OD) matrices. These matrices describe the flows between zones of a city. This methodology is illustrated on the city of Lyon, France, with OD matrices estimated from the mobile phone activity of the subscribers of French telecom operator Orange. We explore how the spatialization can be performed using various probabilistic graph models whose parameters are evaluated via the OD matrices. The structure of the models enforces the consistency of the locations with the chains of activities, such as the fact that two “home” activities must have the same location. Multiple models are proposed, corresponding to different compromises between the two potentially incompatible sources that are HTS and mobile data. We show that while a very naive spatialization approach allows the generation of synthetic travel demand that perfectly fits the flows described by the OD matrices without respecting the consistency of the locations, the other proposed approaches offer much more realistic agendas at the expense of only small discrepancies with the mobile data.