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Calibration is an essential process to make an agent-based simulatoroperational. Especially, the calibration for freight demand is challenging dueto the model complexity and the shortage of available freight demand datacompared with passenger data. This paper proposes a novel calibration methodthat relies solely on screenline counts, named Screenline-based Two-stepCalibration (SLTC). SLTC consists of two parts: (1) tour-based demandadjustment and (2) model parameter updates. The former generatesscreenline-based tours by cloning/removing instances of the simulated goodsvehicle tours, aiming to minimize the gaps between the observed and thesimulated screenline counts. The latter updates the parameters of the commodityflow model which generates inputs to simulate goods vehicle tours. Todemonstrate the practicality of the proposed method, we apply it to anagent-based urban freight simulator, SimMobility Freight. The result shows thatSLTC allows the simulator to replicate the observed screenline counts withreasonable computational cost for calibration.

Screenline-based Two-step Calibration and its application to an agent-based urban freight simulator