Tool: Multimodal trip planner

We propose a preference-based optimization tool for multimodal trip planning. This tool, developed by TU Delft, integrates public transport, ride-pooling services, and shared micro-mobility options (e.g., e-scooters and bikes), offering travelers flexible, low-emission alternatives that align with their individual preferences. At the core of the system is a mixed-integer programming model that embeds user preferences directly into the objective function, ensuring the recommended travel plans are not only efficient but also personally suitable. To handle real-time and dynamic mobility demands, we develop a meta-heuristic framework that combines a customized Adaptive Large Neighborhood Search (ALNS) algorithm with other tailored optimization techniques. A rolling horizon approach enables the system to adapt dynamically to incoming requests and changing availability. We validate our approach using real-world data from a suburban area of Rotterdam, the Netherlands. The results show that our algorithm efficiently generates near-optimal multimodal travel plans, balancing user preferences with system constraints.