Maestría en Ciencias de los Datos y Analítica · 2026

Medición de la accesibilidad al empleo en Bogotá mediante una función de costo generalizado

This study measures job accessibility in Bogotá using a generalized travel cost function that simultaneously integrates total travel time (access, waiting, in-vehicle time, and transfers), direct monetary cost, and the opportunity cost of time. Unlike previous studies that rely on a single measure, we propose a family of specifications with progressive addition of cost components, allowing the independent contribution of each dimension to be quantified. Two sources of travel times are contrasted: those reported by households in the 2023 Origin- Destination Survey and those computed using the Google Distance Matrix API, capturing both the subjective experience and the objective conditions of mobility. Results reveal strong territorial inequality following a center-periphery gradient, and show that travel time, not the fare, accounts for nearly nine out of ten monetary units of total cost in public transport, compared to less than half in private vehicles. To characterize user heterogeneity, an unsupervised classification algorithm based on a Gaussian Mixture Model (GMM) is applied, inferring four traveler archetypes per transport mode from a generalized cost feature vector with probabilistic membership assignment, whose territorial distribution confirms that users with the highest mobility burden are concentrated in the southern and south-western peripheries of the city.

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