Population spatialization for route planning in activity-based models

Activity-based and agent-based models are overcoming numerous limitations inherent in the trip-based model. However, this transition is unveiling new challenges that can influence the modeling outcomes. One notable obstacle in embracing activity-based models is the requirement for detailed, high-resolution input data at a disaggregate level, which are particularly important but difficult to gather. This article concentrates on spatializing populations and disaggregating travel data using sampling and interpolation methods. The Python-based toolkit developed herein is applied to a case study involving the population generation of Dresden in Germany. The outcome of the applied method comprises more than 2000 daily activity schedules with their respective points for each activity location. These results show the toolkit capabilities regarding population spatialization for activity-based models.