Abstract
Abstract. Mobility data of cyclists and pedestrians are fundamental for design and planning strategies of sustainable smart cities. However, adequate data is commonly scarce, expensive to acquire, or hardly accessible. For overcoming this shortcoming and providing support in planning processes, we propose an agent-based model that simulates bicycle and pedestrian traffic flows at a regional scale over one day. The bottom-up approach allows to set individual behaviour that generates system-level patterns. The uncertainty analysis of model results shows moderate and strong correlations with the observational data in terms of spatial and temporal distribution of traffic volumes. The model produces traffic flows at a high spatial (road segment) and temporal (minute) resolution. The model can be used as a scenario-based solution for simulating traffic in different conditions of a physical environment and travel behaviour.
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