Introducing a novel Location-Assignment Algorithm for Activity-Based Transport Models: CARLA

Abstract: This paper introduces CARLA (spatially Constrained Anchor-based Recursive Location Assignment), a recursive algorithm for assigning secondary or any activity locations in activity-based travel models. CARLA minimizes distance deviations while integrating location potentials, ensuring more realistic activity distributions. The algorithm decomposes trip chains into smaller subsegments, using geometric constraints and configurable heuristics to efficiently search the solution space. Compared to a state-of-the-art relaxation-discretization approach, CARLA achieves significantly lower mean deviations, even under limited runtimes. It is robust to real-world data inconsistencies, such as infeasible distances, and can flexibly adapt to various priorities, such as emphasizing location attractiveness or distance accuracy. CARLA's versatility and efficiency make it a valuable tool for improving the spatial accuracy of activity-based travel models and agent-based transport simulations. Our implementation is available at https://github.com/tnoud/carla.