  Calibration is an essential process to make an agent-based simulator
operational. Especially, the calibration for freight demand is challenging due
to the model complexity and the shortage of available freight demand data
compared with passenger data. This paper proposes a novel calibration method
that relies solely on screenline counts, named Screenline-based Two-step
Calibration (SLTC). SLTC consists of two parts: (1) tour-based demand
adjustment and (2) model parameter updates. The former generates
screenline-based tours by cloning/removing instances of the simulated goods
vehicle tours, aiming to minimize the gaps between the observed and the
simulated screenline counts. The latter updates the parameters of the commodity
flow model which generates inputs to simulate goods vehicle tours. To
demonstrate the practicality of the proposed method, we apply it to an
agent-based urban freight simulator, SimMobility Freight. The result shows that
SLTC allows the simulator to replicate the observed screenline counts with
reasonable computational cost for calibration.
