Control-based integration of rejection rates into endogenous demand ride-pooling simulations

The potential of Mobility-on-Demand systems has frequently been studied in the literature, often in combination with dynamic demand models that respond to the obtained service levels. However, the endogenous demand usually does not respond to generated rejection rates. The paper discusses potential ways of integrating this dimension into a behavioral demand model. A linear control-based approach is proposed that penalizes choices for the on-demand service until a maximum desired rejection rate is obtained. The practical value of the method is demonstrated on a large-scale agent-based transport simulation and pathways for future improvements are provided.