An expectation maximization methodology of inferring true OD and utility-based travel preferences is developed and applied. True OD is considered as the unknown latent variable and traveler preference is considered as the unknown variable. The methodology observes route/mode choice changes of users due to perturbations (pricing) in the share mobility system with repeated observations.

The Selective Set Expectation Maximization (SSEM) is developed for data sets with repeated observation. SSEM only searches over choices consistent with all the repeated observations which increases the accuracy of inference results.

A simulation framework is developed for bike sharing system analysis with heterogeneous travelers in a multi-modal travel environment.

Promising computation results are obtained in estimating both true ODs and traveler preference distribution with disaggregate data.

The inferred quantities can inform bike sharing system operations, facilitating inventory rebalancing.