Multi-objective optimization of multimodal passenger transportation networks: coping with demand uncertainty

Robustness of optimal solutions when solving network design problems is of great importance because of uncertainty in future demand. In this research the optimization of infrastructure planning in a multimodal passenger transportation network is defined as a multiobjective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. In a case study in the city region of Amsterdam in The Netherlands, the location of park and ride facilities, train stations and the frequency of public transport lines are decision variables. The Pareto set is approximated by the Epsilon Non-dominated Sorting Genetic Algorithm (ε-NSGAII). In this case study, a demand forecast for 2030 is used, but the underlying demand model always contains uncertainty to a certain extent. Therefore, the differences are analyzed between Pareto sets resulting from solving the network design problem using two other demand scenarios as well: a 2020 demand prediction and a Transit-Oriented Development scenario. The Pareto solutions resulting from one demand scenario are assessed based on a different demand scenario to test whether they are still Pareto optimal under this different demand scenario. Furthermore, the values of the decision variables of the solutions in the sets are compared. Results indicate that a different transportation demand has a strong influence on the Pareto optimal performance of solutions in the set: 70% of the solutions do not perform Pareto optimal any more if assessed using a different transportation demand. However, the loss in objective function values is small (a 2% decrease in hypervolume value), so although performance is not optimal any more in most cases, loss in performance is limited. In addition, the resulting decision variables are relatively insensitive for transportation demand