Can I efficiently calibrate traffic models using road speed data?
On the Use of Abundant Road Speed Data for Travel Demand Calibration of Urban Traffic Simulators
This paper focuses on efficiently calibrating traffic simulation models using readily available road speed data, rather than sparse traffic counts. It formulates the calibration as an optimization problem, seeking the origin-destination (OD) travel demands that best match observed speeds. A novel "metamodel" combines a physics-based traffic flow model with a statistical model to make the optimization process efficient and differentiable, enabling the use of gradient-based solvers. While not explicitly about multi-agent systems, the calibration process could be relevant to LLM-based multi-agent simulations where agent behavior (e.g., route choice) needs to be calibrated against real-world data. The metamodel approach could potentially be adapted to calibrate agent decision-making parameters by using LLMs as the "physics-based" model to predict agent actions based on their internal state and environment, with a statistical model correcting for LLM inaccuracies. This allows efficient calibration of complex multi-agent simulations with real-world data.