Exploring the potential of location-based social networks data as proxy variables in collective human mobility prediction models

The study of human mobility has gained much attention in recent years. To date, various models have been developed to predict human mobility patterns for intra- and/or inter-city cases. These models incorporate the populations as proxy variables in the place of real variables which cannot be observed easily. However, inaccuracies in predicting human mobility within cities are usually encountered. One source of inaccuracies in intra-city scenarios arises from the fact that cities’ populations are influenced by people from other areas. Therefore, population cannot be regarded as a good proxy variable for movement modeling. The objectives of this article are to introduce new proxy variables for use in current models for predicting human mobility patterns within cities, and to evaluate the accuracy of the predictions. In this study, we have introduced new proxy variables, namely, venues and check-ins, extracted from location-based social networks (LBSNs). In order to evaluate the models, we have compared our results with empirical data obtained from taxi vehicles, based on trip distances and destination population distributions. The Sørensen similarity index (SSI) and R-squared measures were also used to compare the performances of models using each variable. The results show that all models with LBSN variables can capture real human movements better within Manhattan, New York City. Our analytical results indicated that the predicted trips using LBSN data are more similar to the real trips, on average, by about 20% based on the SSI. Moreover, the R-squared measures obtained from regression analyses were enhanced significantly.