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Understanding holistic impact of planned transportation solutions and interventions on urbansystems is challenged by their complexity but critical for decision making. The cornerstone for such impact assessments is estimating the transportation mode-shift resulting from the intervention. And while transportation planning has well-established models for the mode-choice assessment such as the nested multinomial logit model, an individual choice simulation could be better suited for addressing the mode-shift allowing to consistently account for individual preferences. In addition, no model perfectly represents the reality while the available ground truth data on the actual transportation choices needed to infer the model is often incomplete or inconsistent. The present paper addresses those challenges by offering an individual mode-choice and mode-shift simulation model and the Bayesian inference framework. It accounts for uncertainties in the data as well as the model estimate and translates them into uncertainties of the resulting mode-shift and the impacts.The framework is evaluated on the two intervention cases: introducing ride-sharing for-hire-vehicles in NYC as well the recent introduction of the Manhattan Congestion Surcharge. Being successfully evaluated on the cases above, the framework can be used for assessing mode-shift and resulting economic, social and environmental implications for any future urban transportation solutions and policies being considered by decision-makers or transportation companies.

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