  Understanding holistic impact of planned transportation solutions and
interventions on urban systems 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.
