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

TL;DR

This paper introduces a novel calibration method for agent-based urban freight simulators that uses only screenline counts, improving demand accuracy with minimal computational effort.

Contribution

The paper presents a new two-step calibration approach relying solely on screenline counts, enhancing freight demand calibration for complex agent-based models.

Findings

SLTC accurately replicates observed screenline counts

The method reduces calibration time and computational costs

It improves demand estimation in freight simulation models

Abstract

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.