A simulation-based method for optimizing remote park-and-ride schemes

Urbanization places greater demand on the link between downtown areas and suburbs, due to commuters’ long-distance and diverse trips. As an emerging form of park-and-ride (PNR) services, remote PNR (RPR) facilities have proved to be more economical and environmentally friendly, allowing travelers to park in a suburban area and travel to a rail station via bus. In this regard, a generalized simulation-based bilevel model for optimizing the locations and capacities of RPR facilities is developed in this article. A hybrid algorithm integrating Bayesian optimization, branch and bound, and trust region sequential quadratic programming is proposed to achieve an optimal solution. The proposed integrated method balances the desired efficiency and accuracy through the combination of machine learning-based technology and mathematical optimization methodology. The validity of the proposed model is tested on a large-scale real-world transportation network in Halle, Germany. Modeling and analyzing RPR schemes using the proposed framework may provide new insights into improving social welfare.