A Framework for Simulating Cyclists in SUMO
summary
Proposes a guideline approach that links SUMO’s sublane car-following framework with a two-dimensional social-force style controller (uses the Nomad model), implements a separation of speed and direction, calibrates parameters on Munich trajectory clusters, and releases an open Python package CyclistModel that runs inside SUMO. Results show better spatial dispersion and speed profiles than sublane-only simulations but still underrepresent real-world scatter; validation data needs and hybrid deployment strategies remain open.
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