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Understanding holistic impact of planned transportation solutions andinterventions on urban systems is challenged by their complexity but criticalfor decision making. The cornerstone for such impact assessments is estimatingthe transportation mode-shift resulting from the intervention. And whiletransportation planning has well-established models for the mode-choiceassessment such as the nested multinomial logit model, an individual choicesimulation could be better suited for addressing the mode-shift allowing toconsistently account for individual preferences. In addition, no modelperfectly represents the reality while the available ground truth data on theactual transportation choices needed to infer the model is often incomplete orinconsistent. The present paper addresses those challenges by offering anindividual mode-choice and mode-shift simulation model and the Bayesianinference framework. It accounts for uncertainties in the data as well as themodel estimate and translates them into uncertainties of the resultingmode-shift and the impacts. The framework is evaluated on the two interventioncases: introducing ride-sharing for-hire-vehicles in NYC as well the recentintroduction of the Manhattan Congestion Surcharge. Being successfullyevaluated on the cases above, the framework can be used for assessingmode-shift and resulting economic, social and environmental implications forany future urban transportation solutions and policies being considered bydecision-makers or transportation companies.

Transportation Interventions Reshaping NYC Commute: the Probabilistic  Simulation Framework Assessing the Impacts of Ridesharing and Manhattan  Congestion Surcharge