arXiv subjects

Giuliano Cornacchia

Publications and source records attributed to Giuliano Cornacchia.

9 recordsLinked to original sources

arXiv2026

The traffic concentration effects of urban navigation services

The collective impact of navigation services remains unclear: while often beneficial to individual drivers, they can unintentionally reshape urban traffic patterns. We simulate their impact in Florence, Milan, and Rome (Italy), integrating GPS data, road networks, and route recommendations from leading providers. We identify a concentration effect: as adoption increases, route diversity declines, and traffic and emissions converge onto fewer roads. At full adoption, route diversity decreases by up to 14% compared to a baseline where recommendations are ignored. Moreover, navigation services reduce CO2 emissions at low adoption levels, but these benefits diminish, disappear, or even reverse beyond a city- and service-specific threshold. We replicate our experiments in an abstract setting, obtaining results consistent with those observed in real-world cities.

cs.MA↗

arXiv2025

A survey on the impacts of recommender systems on users, items, and human-AI ecosystems

Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on users' preferences or requests. This survey systematically reviews, categories, and discusses the impact of recommenders in four human-AI ecosystems -- social media, online retail, urban mapping and generative AI ecosystems. Its scope is to systematise a fast-growing field in which terminologies employed to classify methodologies and outcomes are fragmented and unsystematic. This is a crucial contribution to the literature because terminologies vary substantially across disciplines and ecosystems, hindering comparison and accumulation of knowledge in the field. We follow the customary steps of qualitative systematic review, gathering 154 articles from different disciplines to develop a parsimonious taxonomy of methodologies employed (empirical, simulation, observational, controlled), outcomes observed (concentration, content degradation, discrimination, diversity, echo chamber, filter bubble, homogenisation, polarisation, radicalisation, volume), and their level of analysis (individual, item, and ecosystem). We systematically discuss substantive and methodological commonalities across ecosystems, and highlight potential avenues for future research. The survey is addressed to scholars and practitioners interested in different human-AI ecosystems, policymakers and institutional stakeholders who want to understand better the measurable outcomes of recommenders, and tech companies who wish to obtain a systematic view of the impact of their recommenders.

cs.IR↗

arXiv2025

A computational framework for quantifying route diversification in road networks

The structure of road networks impacts various urban dynamics, from traffic congestion to environmental sustainability and access to essential services. Recent studies reveal that most roads are underutilized, faster alternative routes are often overlooked, and traffic is typically concentrated on a few corridors. In this article, we examine how road network structure, and in particular the presence of mobility attractors (e.g., highways and ring roads), shapes the counterpart to traffic concentration: route diversification. To this end, we introduce DiverCity, a measure that quantifies the extent to which traffic can potentially be distributed across multiple, loosely overlapping near-shortest routes. Analyzing 56 diverse global cities, we find that DiverCity is influenced by network characteristics and is associated with traffic efficiency. Within cities, DiverCity increases with distance from the city center before stabilizing in the periphery, but declines in the proximity of mobility attractors. We demonstrate that strategic speed limit adjustments on mobility attractors can increase DiverCity while preserving travel efficiency. We isolate the complex interplay between mobility attractors and DiverCity through simulations in a controlled setting, confirming the patterns observed in real-world cities. DiverCity provides a practical tool for urban planners and policymakers to optimize road network design and balance route diversification, efficiency, and sustainability. We provide an interactive platform (https://divercitymaps.github.io) to visualize the spatial distribution of DiverCity across all considered cities.

physics.soc-ph↗

arXiv2025

The Path is the Goal: a Study on the Nature and Effects of Shortest-Path Stability Under Perturbation of Destination

This work examines the phenomenon of path variability in urban navigation, where small changes in destination might lead to significantly different suggested routes. Starting from an observation of this variability over the city of Barcelona, we explore whether this is a localized or widespread occurrence and identify factors influencing path variability. We introduce the concept of "path stability", a measure of how robust a suggested route is to minor destination adjustments, define a detailed experimentation process and apply it across multiple cities worldwide. Our analysis shows that path stability is shaped by city-specific factors and trip characteristics, also identifying some common patterns. Results reveal significant heterogeneity in path stability across cities, allowing for categorization into "stable" and "unstable" cities. These findings offer new insights for urban planning and traffic management, highlighting opportunities for optimizing navigation systems to enhance route consistency and urban mobility.

cs.CY↗

arXiv2024

Popularity-based Alternative Routing

Alternative routing is crucial to minimize the environmental impact of urban transportation while enhancing road network efficiency and reducing traffic congestion. Existing methods neglect information about road popularity, possibly leading to unintended consequences such as increasing emissions and congestion. This paper introduces Polaris, an alternative routing algorithm that exploits road popularity to optimize traffic distribution and reduce CO2 emissions. Polaris leverages the novel concept of K-road layers, which mitigates the feedback loop effect where redirecting vehicles to less popular roads could increase their popularity in the future. We conduct experiments in three cities to evaluate Polaris against state-of-the-art alternative routing algorithms. Our results demonstrate that Polaris significantly reduces the overuse of highly popular road edges and traversed regulated intersections, showcasing its ability to generate efficient routes and distribute traffic more evenly. Furthermore, Polaris achieves substantial CO2 reductions, outperforming existing alternative routing strategies. Finally, we compare Polaris to an algorithm that coordinates vehicles centrally to distribute them more evenly on the road network. Our findings reveal that Polaris performs comparably well, even with much less information, highlighting its potential as an efficient and sustainable solution for urban traffic management.

cs.MA↗

arXiv2023

One-Shot Traffic Assignment with Forward-Looking Penalization

Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient route assignments. This paper introduces METIS, a cooperative, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of METIS against state-of-the-art one-shot methods. Compared to the best baseline, METIS significantly reduces CO2 emissions by 18% in Milan, 28\% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes METIS as a promising solution for optimizing TA and urban transportation systems.

cs.MA↗

arXiv2022

How Routing Strategies Impact Urban Emissions

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., carbon dioxide emissions and population exposure to 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.

cs.CY↗

arXiv2020

A dataset to assess mobility changes in Chile following local quarantines

Fighting the COVID-19 pandemic, most countries have implemented non-pharmaceutical interventions like wearing masks, physical distancing, lockdown, and travel restrictions. Because of their economic and logistical effects, tracking mobility changes during quarantines is crucial in assessing their efficacy and predicting the virus spread. Chile, one of the worst-hit countries in the world, unlike many other countries, implemented quarantines at a more localized level, shutting down small administrative zones, rather than the whole country or large regions. Given the non-obvious effects of these localized quarantines, tracking mobility becomes even more critical in Chile. To assess the impact on human mobility of the localized quarantines in Chile, we analyze a mobile phone dataset made available by Telefónica Chile, which comprises 31 billion eXtended Detail Records and 5.4 million users covering the period February 26th to September 20th, 2020. From these records, we derive three epidemiologically relevant metrics describing the mobility within and between comunas. The datasets made available can be used to fight the COVID-19 epidemics, particularly for localized quarantines' less understood effect.

physics.soc-ph↗

arXiv2020

Modelling Human Mobility considering Spatial,Temporal and Social Dimensions

Modelling human mobility is crucial in several areas, from urban planning to epidemic modeling, traffic forecasting, and what-if analysis. On the one hand, existing models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. On the other hand, those models that capture some social aspects of human mobility have trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose STS-EPR, a modeling framework that embeds mechanisms to capture the spatial, temporal, and social aspects together. Our experiments show that STS-EPR outperforms existing spatial-temporal or social models on a set of standard mobility metrics and that it can be used with a limited amount of information without any significant loss of realism. STS-EPR, which is open-source and tested on open data, is a step towards the design of mechanistic models that can capture all the aspects of human mobility in a comprehensive way.

cs.SI↗