The practice of estimating origin–destination (OD) demand usually requires large-scale travel surveys. To reduce the cost and time spent on surveys, individual trajectory data obtained from mobile devices has been used as an alternative dataset since the last two decades for OD estimation but also constrained in practice in some countries. To estimate OD matrices while protecting privacy, this study uses aggregated data of mobile phone traces to estimate work-related trips. The proposed approach is a sequential updater based on the maximum entropy principle. Trip production and attraction are firstly calculated by a non-linear programming problem followed by a matrix fitting problem to distribute trips to each OD pair. Numerical study shows that updated values are much closer to the synthesize real values than the referred ones. The case study in Tokyo further demonstrates that the proposed updating approach can track the change of travel pattern.
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