Abstract Location-based recommender systems are increasingly embedded in location-based services, shaping individual mobility decisions in urban environments. While their predictive accuracy has been extensively studied, less attention has been paid to their systemic impact on urban dynamics. In this work, we introduce a simulation framework to model the human–AI feedback loop underpinning next-venue recommendation, capturing how algorithmic suggestions influence individual behaviour, which in turn reshapes the data used to retrain the models. Our simulations, grounded in real-world mobility data, systematically explore the effects of algorithmic adoption across a range of recommendation strategies. We find that while recommender systems consistently increase individual-level diversity in visited venues, they may simultaneously amplify collective inequality by concentrating visits on a limited subset of popular places. This divergence extends to the structure of social co-location networks, revealing broader implications for urban accessibility and spatial segregation. Our framework operationalizes the feedback loop in next-venue recommendation and offers a novel lens through which to assess the societal impact of AI-assisted mobility, providing a computational tool to anticipate future risks, evaluate regulatory interventions, and inform the design of ethic algorithmic systems.

This study investigates the application of Recom-mender Systems (RS) to predict future Point of Interest (POI) visits based on check-in data, with a particular focus on biases related to individual mobility patterns and POI popularity. We conduct a comprehensive analysis by training and evaluating three RS models based on different architectures: a Convo-lutional Neural Network, an Attention-based Neural Network, and a Markov-based predictor. Our analysis reveals that POI recommenders: do not show bias in terms of the typical distance traveled by users but tend to favor less exploratory users, and are biased towards more popular POIs. Our findings highlight the potential of RS in capturing and forecasting user behavior, while also underscoring the need to mitigate these biases, thereby advancing the understanding of RS and their broader social impact.

Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., CO2 emissions and pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.