Profiling workers’ activity-travel behavior based on mobile phone data

Abstract

Activity-based micro-simulation models typically predict 24-hour activity-travel patterns for each individual in a study area. These patterns reflect the characteristics of the available transportation infrastructure and land-use system as well as individuals’ lifestyles and needs. However, the lack of a reliable benchmark to evaluate the generated patterns has been a major concern. To address this issue, we explore the possibility of using mobile phone data to build such a validation measure. Our investigation consists of three steps. First, the daily trajectory of locations, where a user performed activities, is constructed from the mobile phone records. To account for the discrepancy between the movements revealed by the call data and the real traces that the user has made, the daily trajectories are then transformed into travel sequences. Finally, all the inferred travel sequences are classified into typical activity-travel patterns which, in combination with their relative frequencies, define a profile. The established profile represents the activity-travel behavior in the study area, and thus can be used as a benchmark for the validation of the activity-based models. By comparing the benchmark profiles derived from the call data with statistics that stem from activity-travel surveys, the validation potential is demonstrated. In addition, a sensitivity analysis is carried out to assess how the results are affected by the different parameter settings defined in the profiling process.