Abstract:

Network design problems such as the optimization of road capacities, public transport lines and frequencies, mobility hub locations, shared mobility services and traffic-signal settings have been extensively studied in literature using bi-level optimization. However, due to the computational burden, the design and response spaces that have been considered so far are rather limited, resulting in suboptimal designs. In this paper we introduce a new framework for simultaneous bi-level optimization that contributes to the existing literature by optimizing designs and responses simultaneously rather than sequentially, and by using machine learning for the design and response optimization instead of only for the design optimization. Based on experiments for the city of Delft in the Netherlands in which parking lot locations and tariffs are optimized, this paper demonstrates that simultaneous design and response optimization is possible and that designs created with a small fixed response space, which is common practice, may be worse than expected, due to the fact that a population can respond in an unanticipated manner during design optimization. The proposed design and response optimization framework is able to automatically learn responses, leading to designs that are 21% to 53% better compared to designs created with a fixed response space, depending on the indicator chosen. In future work, the framework can be extended to a multi-level optimization framework that include multiple stakeholder groups and their preferences so that complex discussions may be supported by simultaneously running machine learning algorithms for each stakeholder group.