Fecha: 25/09/2026
Autores: Gonzalo Méndez-Vogel ab, Marcel Favereau cb
aUniversidad Andrés Bello, Department of Engineering Sciences, Autop.Concepción – Talcahuano, 7100, Concepción, Chile
bCenter for Advanced Transportation, Logistics, and Economic Competitiveness (CATLEC), ANID/CIN 250061, Santiago, Chile
cFacultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Chile, Santiago, Chile
Scientific journal / publisher: Computers & Industrial Engineering
Abstract o breve resumen: Urban transportation is a major source of greenhouse gas emissions, which motivates cities to promote shared micromobility systems such as electric bicycles. These systems can reduce emissions by replacing short car trips with e-bike trips; however, initial deployment decisions are often based on limited integration of user behavior and on operational uncertainty, which can negatively affect adoption and service reliability. This paper presents a behavior-aware optimization framework for planning station-based shared e-bike networks, with the aim of reducing urban CO2 emissions. Potential users are represented through a discrete choice model, from which adoption probabilities for pairs of candidate stations are estimated a priori, based on station accessibility, trip attributes, and monetary costs. Station locations are optimized to maximize expected avoided emissions, and a subsequent simulation-based sizing step evaluates operational reliability under stochastic demand. Using a case study in Santiago, Chile, the results indicate that a relatively small, well-located network can deliver a large share of the achievable emissions reduction. A 100-station configuration captures more than half of the maximum expected avoided emissions obtained with much larger deployments. The simulation further reveals marked heterogeneity in capacity needs and shows that shortages at a small set of high-demand hubs can disproportionately degrade service and threaten sustained mode shift. These findings support planning approaches that combine strategic location with reliability-oriented sizing to enable robust, low-risk investments in shared e-bike infrastructure.