Ontology-induced bias in video-based vehicle counting and its propagation into a road traffic noise model — Ciudad Juárez, Mexico

Abstract

Data, field notebooks and code supporting a study of two video-based vehicle counting campaigns on a single arterial corridor of Ciudad Juárez, Mexico, and of how a classification error propagates into a road traffic noise model of the FHWA/REMEL family.The deposit contains the full adjudication register of the 105 image crops that a pretrained detector flagged as heavy vehicles, adjudicated twice and independently — once with the assistance of a large language model and once by the author under blind conditions, in two tranches — together with the blind answer sheets and keys, the measured-versus-modelled evaluation over 126 pairs from six continuously monitored sites, the vehicle registry tabulations used to characterise the local fleet, the calibration notebooks of both campaigns, and the code that reproduces every figure.The author's labels are the reference throughout. Agreement between the two passes was 30 of 30 on a pre-registered random subsample (Cohen's κ = 1.00) and 98 of 105 over the full census (93.3 %, κ = 0.586); the seven discrepancies are itemised in the accompanying paper.Video material and the original crops are deliberately not deposited: the capture protocol prohibits retaining legible licence plates, and the crops contain them. Every image reproduced in the paper had its plates blurred. The adjudication register makes each verdict auditable without distributing any image with a plate.