Efficient route planning is crucial for modern navigation systems, yet traditional methods face challenges in scenarios with unknown or frequently changing traffic dynamics. This paper introduces a general labeling framework based on the 2-hop cover property, enabling robust, metric-independent preprocessing. Using this framework, we propose Customizable Tree Labeling (CTL), a tree-based method combining three key components: metric-independent preprocessing with tree hierarchies, metric customization for dynamic updates, and efficient query algorithms for fast route computation. To allow trade-offs between customization time, labeling size, and query performance, we further develop a parameterized customization technique by dynamically combining tree labels and shortcut graphs. Our key contributions include the introduction of a customizable labeling framework, a novel tree hierarchy for compact and scalable representation, and a hybrid query algorithm that integrates labels and shortcuts for fast and accurate route computation. We conduct extensive experiments on ten large-scale real-world road networks and a case study on the traffic assignment problem. Our algorithms achieve query response times significantly faster than the state-of-the-art methods, while maintaining competitive customization times and labeling size, making it well-suited for real-time and dynamic routing applications.

We introduce an improved algorithm for the dynamic taxi sharing problem, i.e. a dispatcher that schedules a fleet of shared taxis as it is used by services like UberXShare and Lyft Shared. We speed up the basic online algorithm that looks for all possible insertions of a new customer into a set of existing routes, we generalize the objective function, and we efficiently support a large number of possible pick-up and drop-off locations. This lays an algorithmic foundation for taxi sharing systems with higher vehicle occupancy - enabling greatly reduced cost and ecological impact at comparable service quality. We find that our algorithm computes assignments between vehicles and riders several times faster than a previous state-of-the-art approach. Further, we observe that allowing meeting points for vehicles and riders can reduce the operating cost of vehicle fleets by up to 15% while also reducing rider wait and trip times.