Simulating the effect of measurement errors on pedestrian destination choice model calibration

Accurately calibrated pedestrian destination choice models help explain and predict foot traffic in public places by describing how individuals choose locations to visit. Model calibration relies on empirical data, which is subject to measurement errors that can obfuscate calibration. This contribution adds errors to simulated data in a controlled and realistic way which can be applied to many model specifications, demonstrated on a pedestrian destination choice model. Results show that errors can cause calibrated models to generate dynamics that differ substantially from the true dynamics, along with causing bias in parameters and decreased prediction accuracy. By quantifying the size of errors and the impacts on calibration, this work aims to guide researchers in pedestrian destination choice modelling on what level of error is acceptable given the scope of their research.