Optimización de seguridad en TransMilenio: Aplicación móvil con red neuronal para análisis de incidentes de inseguridad

Abstract

The TransMilenio mass transportation system, considered the most important in Bogotá, faces constant security issues, mainly related to thefts and threats against passengers. The lack of effective tools to report and manage these incidents in real time has generated growing concern among users, who are frequently affected by new forms of theft. Likewise, the absence of specialized mechanisms for data analysis has hindered authorities from acting proactively, which has negatively impacted public confidence in the system’s security. Despite the efforts of the local government to strengthen security, no effective measure has been found to mitigate this problem, which directly affects the community and has been reflected in the increase in criminal activities in recent years. To address this challenge, the development of a mobile application is proposed, integrating a neural network model capable of collecting, analyzing, and evaluating data on security incidents in TransMilenio. This tool will make it possible to identify patterns, stations, and schedules with the highest incidence of theft, providing key information to implement preventive measures that enhance user safety.