Imputing informal workers for transportation modeling in Latin America by the use of machine learning techniques

César Maia de Souza , Roberto Ponce-Lopez , Gonzalo Gaudencio Peraza-Mues , Alejandro Antonio Dominguez-Cristerna , Eric J. Miller
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Abstract

The informal economy plays a critical role in the Global South, particularly in large urban areas such as Mexico City, where over 50 % of jobs belong to this segment. It is crucial to understand the travel behavior of informal workers and effectively integrate these patterns into advanced transportation models, such as activity-based models (ABMs). This study proposes a unique approach for identifying informal workers across distinct economic sectors in Mexico’s Monterrey metropolitan area, by utilizing an Origin-Destination survey and the National Occupation and Employment Survey (ENOE). Machine learning models, trained on the ENOE dataset and applied to the OD survey, first classified workers as formal or informal, and subsequently reassigned informal laborers who had initially been classified under “Other" to the construction or commerce categories. The use of the Gradient Boosted Trees (GBT) classifier emerged as the optimal method, yielding accuracies of 78.0 % and 70.7 % for the two stages. Differences between predicted results and observed values fall within an acceptable range, especially in sectors with high informal worker rates. The resulting estimate and characterization of informal workers can potentially be integrated into ABMs, thereby providing a foundation for assessing the responses of informal workers to infrastructure policy interventions.
通过使用机器学习技术为拉丁美洲的运输建模推算非正式工人
非正规经济在全球南方发挥着关键作用,特别是在墨西哥城等大城市地区,其中超过50% %的工作属于这一部门。了解非正式工人的出行行为,并将这些模式有效地整合到先进的交通模型中,如基于活动的模型(ABMs),这是至关重要的。本研究提出了一种独特的方法来识别墨西哥蒙特雷大都市区不同经济部门的非正式工人,通过利用起源-目的地调查和国家职业和就业调查(ENOE)。在ENOE数据集上训练并应用于OD调查的机器学习模型首先将工人分类为正式或非正式工人,随后将最初归类为“其他”类别的非正式工人重新分配到建筑或商业类别。使用梯度提升树(GBT)分类器是最优方法,在两个阶段的准确率分别为78.0 %和70.7 %。预测结果与观察值之间的差异在可接受的范围内,特别是在非正规工人率高的部门。由此产生的对非正规工人的估计和特征可以潜在地整合到ABMs中,从而为评估非正规工人对基础设施政策干预的反应提供基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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