Using fair division methods for allocating transportation funds

Current transportation funding allocation methods are very complex due to limited budgets and conflicting interests. In order to maintain an efficient infrastructure transportation system, agencies must balance the allocation of funds required either to increase the roadway capacity or to preserve the existing roads in service. Depending on the target objectives and specific constraints, the funding allocation process is approached using different methods. The funding allocation process is usually addressed using expert judgment which involves subjective criteria, weighted formulas with pre-established priorities, or a combination of both. However, formula-based funding allocation methods may lead to the public\u27s disagreement if final decisions are not perceived as fair or equitable. For example, if the funding allocation process is based on population or total highway miles, some agencies may perceive it as unfair. Obtaining consensus on the funding allocation criteria is very difficult due to multiple interests and different perspectives from each of the participants requesting the funds. The perception of a participant towards the final allocation of funds could result in the generation of envy if one or more participants believe that the allocation is unfair. An alternative approach for funding allocation is to use fair division methods. Fair division methods aims to result into a more effective and equitable practice. This thesis presents a Fair Division Transportation Funding Allocation Model (FDTFAM) as an alternative method to fairly distribute limited funds among participants. A mathematical formulation is developed to minimize the total envy based on the own priorities of each participant and the overall budget constraints. FDTFAM is solved using ranking or optimization methods. A case study was conducted to compare the results of both methods. The formulation of the fair division allocation mathematical model and the application of an optimization genetic algorithm to solve the fair division problem are the major contributions of this research