Micromobility and Artificial Intelligence

Abstract

The intersection of micromobility and artificial intelligence (AI) is transforming urban transportation, offering sustainable and efficient alternatives to traditional vehicles. Micromobility, encompassing devices like electric scooters and bicycles, provides a flexible means of navigating urban environments, contributing to reduced traffic congestion and lower carbon emissions. AI enhances these solutions by introducing advanced navigation systems, automated safety measures, and adaptive technologies that personalize the user experience. AI-driven navigation systems leverage real-time data and machine learning algorithms to optimize routes, avoid traffic, and ensure safety. Safety is further improved through AI-powered obstacle detection and collision avoidance systems, which use sensors and real-time data processing to prevent accidents. The integration of AI also supports the maintenance of micromobility devices through predictive diagnostics, extending their lifespan and reliability. Moreover, AI-driven micromobility contributes to the development of smart cities by providing valuable data for urban planners, optimizing infrastructure, and improving traffic management. These advancements not only enhance the quality of life for urban residents but also promote social and economic benefits, such as reducing social inequalities and supporting economic growth. However, challenges such as data reliability, ethical concerns, and regulatory hurdles must be addressed to fully realize the potential of AI-enhanced micromobility in creating more connected and accessible urban environments.