Evaluating Potential E-Bike Routes in Valparaíso’s Historic Quarter, Chile: Comparative Human and AI Street Auditing and Local Scale Approaches

This study evaluates potential routes for electric bicycles (E-Bikes) in Valparaíso, Chile, using street audits performed by both humans and artificial intelligence (AI). Audit methods were compared to identify routes connecting the Puerto metro station with Avenida Alemania (a strategic city avenue), prioritizing criteria such as street infrastructure, habitability, and street coexistence. The results show that the human audit gives higher scores in subjective variables, such as the perception of security and urban dynamism, while AI penalizes infrastructure deficiencies more severely, especially in areas with steep slopes and low tree cover. Despite these differences, both methods highlight the inadequacy of current infrastructure to promote the use of E-Bikes in the city. This work provides a novel perspective by evaluating human and AI-assisted methodologies, suggesting that an integration between the two could improve accuracy and reduce subjectivity in urban audits. In addition, the results underline the need for public policies that prioritize accessibility, safety, and equity in urban mobility, especially in vulnerable areas. Future research should explore training AI algorithms with human audit data to strengthen AI’s ability to interpret contextual variables and dynamics in complex urban environments.