Identifying the best project alternative is a critical challenge facing major transportation projects (MTPs) at the front-end phase. The increasing complexity and dynamism of MTPs have imposed substantial uncertainties and subjectivities in the decision-making process. Despite the efforts made in previous studies, a stochastic framework to facilitate the comprehensive assessment is still missing. In this research, a stochastic decision support framework has been developed to cope with the considerable uncertainties in MTPs. The features of the proposed decision support framework are achieved by using the Bayesian belief network modeling technique to provide a comprehensive registry of the relevant decision factors, establish the interrelationships between these decision factors, and consequently quantify uncertainties of decision indicators. The calculated probabilities for decision indicators have been interpreted to a satisfaction level of stakeholders based on their constraints as a multi-criteria decision model. A Monte Carlo simulation has been conducted to simulate a real condition using the decision indicators probability as input. Finally, MTP alternatives prioritized according to the anticipated satisfactory gained among various stakeholders. The created framework is used in a preliminary alternative assessment for case study related to Detroit River International Crossing project. The case study investigates the decision-making of key stakeholders related to prioritization of alternative projects for a new access between Detroit, US and Winsdor, Canada. The project team verified applicability of the model. The developed framework and the case study highlight the significance of identification of a stochastic project alternative assessment method. The proposed framework provides decision-makers with a decision support tool to facilitate front-end phase of MTPs.