Movilidad urbana sostenible en bogotá: integración de its, políticas públicas y modelado predictivo basado en aprendizaje automático con enfoque en eficiencia y equidad

Abstract

Bogotá, like many major cities worldwide, faces significant challenges in urban mobility, including traffic congestion, air pollution, and inefficiencies in public transportation systems. This research addresses these issues through the development of an artificial intelligence–based simulation model aimed at improving sustainability and efficiency. Using a high-demand vehicular zone as the analytical unit, the study integrates three core dimensions: the implementation of sustainable mobility policies, the optimization of route planning, and the promotion of alternative transportation modes. Furthermore, it evaluates the potential of emerging technologies such as Intelligent Transportation Systems (ITS) and Mobility as a Service (MaaS) to transform urban transport management. The findings offer technically feasible strategies tailored to the local context, contributing to the development of a more efficient, equitable, and sustainable urban mobility system in Bogotá.