Citation

King C, Koltsova O, Bode NWF. Transportmetrica A: Transp. Sci. 2023; 19(1): e2017510.

Copyright

(Copyright © 2023, Informa - Taylor and Francis Group)

DOI

10.1080/23249935.2021.2017510

PMID

unavailable

Abstract

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.

Language: en

Keywords

Crowd simulation; DC: destination coverage; destination choice modelling; GPS: global positioning system; IR: infra-red; KS: Kolmogorov–Smirnov; measurement error; MLE: maximum likelihood estimation; MNL: multinomial logit; NLL: negative log-likelihood; OIQR: occupancy interquartile range; pedestrian dynamics; PP: predictive power; RMKS: relative median Kolmogorov–Smirnov; statistical model calibration