arXiv subjects

Massimiliano Luca

Publications and source records attributed to Massimiliano Luca.

At least 19 recordsLinked to original sources

arXiv2026

Deal Me Maybe: The Role of Emotions in Multi-Agent Negotiation

Negotiation is a demanding social task for LLM agents, requiring strategic reasoning, persuasion, and interpersonal adaptation. Yet existing benchmarks often treat agents as emotionally neutral, overlooking a key driver of human bargaining behavior. We study how prompt-conditioned emotions affect LLM-based price negotiation. In a controlled framework, buyer and seller agents are independently assigned one of six emotional states and negotiate over 350 real consumer products under two budget conditions. Across 36 emotion-pair settings and five widely used LLMs, we find that emotions strongly shape outcomes. Angry buyers almost never reach agreement (0.39% deal rate), while happy buyers agree most often (28.91%), but obtain worse prices than fearful buyers. Emotion effects are role-dependent: buyer emotion mainly drives acceptance and rejection, whereas seller emotion shapes concession dynamics. These effects influence not only language, but also termination behavior and price trajectories, raising concerns for emotion-conditioned agents in commerce.

cs.AI↗

arXiv2026

Mirror, Mirror on the Wall: Can VLM Agents Tell Who They Are at All?

In the animal kingdom, mirror self-recognition is a canonical probe of higher-order cognition, emerging only in some species. We ask whether an analogous functional capability emerges in embodied vision-language model (VLM) agents: can they recognize themselves in a mirror? We introduce a controlled 3D benchmark where a first-person VLM agent must infer a hidden body attribute from its reflection and select the matching target, while avoiding self-other misattribution. To separate mirror-grounded self-identification from shortcuts, we test mirror removal, misleading cues, and occluded reflections. We also evaluate the decision process through mirror seeking, temporal ordering, self-attribution, and reasoning-action consistency. Our experiments show that mirror-based self-identification emerges mainly in stronger VLMs. These models can use reflected evidence for action, whereas weaker models often inspect the mirror but fail to extract self-relevant information or misattribute their reflection. Language-vision conflict further shows that self-referential language alone is not evidence of grounded self-identification. Overall, mirror-based evaluation provides a diagnostic for whether embodied self-grounding is causally rooted in perception and action rather than priors, prompt compliance, or confabulation.

cs.AI↗

arXiv2026

A City-Scale Dataset of Traffic Flows, Travel Times, and Urban Context

We present a multi-source traffic dataset derived from Automatic Vehicle Identification (AVI) recordings in Padua, Italy, spanning from February 2026 to August 2026. The dataset combines traffic volume time series, aggregated at 10-minute intervals, with time-varying trajectory-based flow statistics including transition probability matrices, average travel times, and flow residuals. To enrich the traffic measurements with urban contextual information, we integrate Points Of Interest (POIs), demographic data, meteorological variables, and road infrastructure data. All components are accessible through a Python class that loads temporal and contextual data exploiting a spatio-temporal graph representation. Validation analyses confirm that the dataset captures expected traffic patterns, such as morning and evening rush hours, as well as weekdays vs. weekend days traffic routines.

physics.soc-ph↗

arXiv2025

TS-Mob: Social and Geographical-Aware Time Series Foundation-Model Framework for Human Mobility Prediction

Short-term forecasting of aggregated human mobility flows supports urban planning, intelligent transportation systems, and emergency response, yet existing models often require substantial mobility history and learn spatial structure implicitly through grids or graphs. Time series foundation models provide strong temporal priors but typically lack explicit geographic and social conditioning for origin-destination interactions. We introduce TS-Mob, a framework that conditions a fine-tuned time series foundation model (TimesFM) forecaster on a gravity-inspired destination-attractiveness index that encodes geographic and social signals computed from open data (living population, centroid distances, and Overture POI counts), together with weather covariates. Evaluated on commonly used benchmarks like Bike New York City, Taxi Beijing, and a nation-scale Spain origin-destination matrix estimated through mobile phone data, TS-Mob outperforms classical, deep spatio-temporal, and a set of foundation model-based baselines across RMSE, MAE, and CPC, with gains up to 78.71% lower MAE and 137.93% higher CPC over the best classical baseline, and up to about 4.27% lower RMSE over the strongest foundation baseline. Stratified analyses further show robustness across different temporal regimes, like weekdays/weekends.

cs.LG↗

arXiv2025

AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario

Diffusion-based text-to-image models are increasingly used for urban analysis and scenario generation, but their geographic knowledge and representational biases remain poorly understood. We evaluate FLUX 1-schnell and Stable Diffusion 3.5-Large in the United States by generating 150 street-view images for each state, each state capital, and a generic "USA" prompt. Images are embedded with DINO-v2 ViT-S/14 and compared with Fr\'echet Inception Distance (FID). Pairwise FID clustering shows that geographically proximate states and capitals often group together, indicating implicit geographic structure. However, the generic ``USA'' prompt collapses this diversity into a metropolitan stereotype: frontier, desert, tropical, rural, and small-city environments are underrepresented or distant in FID space. These results show that diffusion models can encode fine-grained geography while still reproducing narrow national-scale visual stereotypes.

cs.AI↗

arXiv2025

pySpainMobility: Unlocking Spanish Open Mobility Data for Spatial Inequality Research

Human mobility shapes access to resources, opportunities, and services, making movement data a powerful lens for studying spatial and social inequality. Yet despite the growing availability of official open mobility datasets, their research potential is rarely realized because the technical overhead of retrieving, harmonizing, and processing them often crowds out substantive analysis. To address this, we introduce pySpainMobility, a Python package that automates the retrieval and harmonization of Spain's open mobility data across spatial resolutions and demographic strata, streamlining national-scale, reproducible analysis. Using the package, we study income-stratified mobility inequality across Spain's inter-province network, drawing on district-level origin-destination flows for four representative weeks spanning the seasons of 2023. We construct income-specific mobility layers and show that socioeconomic stratification is deeply embedded in the structure of the national mobility system: low-income mobility is disproportionately concentrated in a narrow set of destinations and shorter in spatial reach, while high-income groups access a broader and more distant hierarchy of destinations. Low- and high-income layers consistently follow weakly aligned destination hierarchies across seasons, indicating that income groups navigate distinct mobility geographies rather than a shared one at different volumes. We further show that destination provinces themselves differ systematically in the income composition of the travelers they receive, with several provinces attracting arrivals disproportionately skewed toward one income group relative to the national seasonal baseline. These results demonstrate how official open mobility data, combined with accessible tooling, can be operationalized to reveal spatial inequality as a structural property of national mobility networks.

cs.CY↗

arXiv2025

The LLM Wears Prada: Analysing Gender Bias and Stereotypes through Online Shopping Data

With the wide and cross-domain adoption of Large Language Models, it becomes crucial to assess to which extent the statistical correlations in training data, which underlie their impressive performance, hide subtle and potentially troubling biases. Gender bias in LLMs has been widely investigated from the perspectives of works, hobbies, and emotions typically associated with a specific gender. In this study, we introduce a novel perspective. We investigate whether LLMs can predict an individual's gender based solely on online shopping histories and whether these predictions are influenced by gender biases and stereotypes. Using a dataset of historical online purchases from users in the United States, we evaluate the ability of six LLMs to classify gender and we then analyze their reasoning and products-gender co-occurrences. Results indicate that while models can infer gender with moderate accuracy, their decisions are often rooted in stereotypical associations between product categories and gender. Furthermore, explicit instructions to avoid bias reduce the certainty of model predictions, but do not eliminate stereotypical patterns. Our findings highlight the persistent nature of gender biases in LLMs and emphasize the need for robust bias-mitigation strategies.

cs.AI↗

arXiv2025

Understanding Urban-Rural Disparities in Mobility Inefficiency for Colombia, Mexico, and India

Rural and urban areas exhibit distinct mobility patterns, yet a systematic understanding of how these trends differ across regions and contexts remains underexplored. By using origin-destination matrices from Location-Based Services data in Colombia, India, and Mexico, we delineate urban and rural boundaries through network percolation, reducing reliance on conventional urbanisation metrics tied to the built environment. We gauge mobility dynamics across regions developing a measure for routing inefficiency, which measures how much longer empirical trips are than their optimal shortest path. Our findings reveal that rural areas experience greater inefficiencies, particularly for longer trips made later in the day. At the urban level, we determine the misalignment between urban mobility efficiency and public transit accessibility, by measuring the difference between their respective vector fields. We observe that most cities experience misalignment during regular commuting hours, with Colombian cities exhibiting particularly high alignment. Meanwhile, mobility inefficiency in rural areas are associated with their orientation around their most proximate city. City-level analyses uncover disparities in the functions of rural and urban areas, with significant variations between weekdays and weekends, reflecting distinct roles in commuting and access to services. These findings highlight the importance of tailored, context-sensitive approaches to improving connectivity and reducing disparities. This study offers new insights into the spatial and temporal dynamics of mobility inefficiency, contributing to equitable regional planning and sustainable mobility solutions.

physics.soc-ph↗

arXiv2025

Multimodal Large Language Models Predict Urban Safety Perception but Encode Non-Neutral Demographic Priors

Understanding how people perceive urban environments is essential for inclusive planning, yet conventional surveys are costly and difficult to scale. We investigate whether Multimodal Large Language Models (MLLMs) can assess perceived urban safety from street-view imagery while accounting for the observer-dependent nature of perception. Using Place Pulse 2.0, we evaluate four open and proprietary MLLMs across 56 cities under a Neutral prompt and socio-demographic personas defined by gender, age, and race or ethnicity. We also analyse the keywords generated to justify each classification. All four models display comparable zero-shot capability, with city-macro F1 scores of 65--69%, and preserve meaningful cross-city variation. However, they systematically favour the Safe class, underpredict unsafety, and compress differences between cities. Their explanations converge on a shared visual lexicon: maintenance, greenery, order, and residential character support Safe judgements, whereas deterioration, isolation, poor lighting, and limited pedestrian activity support Unsafe judgements. Persona prompting produces substantial and structured shifts while holding the image fixed. Female personas yield more Unsafe classifications than Male personas across all models; age effects are model-dependent, although Middle-aged personas generally remain closest to Neutral. Black/African American and Native American personas frequently show the largest departures, while the closest race or ethnicity match varies by model. These findings show that MLLMs can provide scalable signals of perceived urban safety, but not from a demographically neutral standpoint.

cs.CY↗

arXiv2024

Towards Civic Digital Twins: Co-Design the Citizen-Centric Future of Bologna

We introduce Civic Digital Twin (CDT), an evolution of Urban Digital Twins designed to support a citizen-centric transformative approach to urban planning and governance. CDT is being developed in the scope of the Bologna Digital Twin initiative, launched one year ago by the city of Bologna, to fulfill the city's political and strategic goal of adopting innovative digital tools to support decision-making and civic engagement. The CDT, in addition to its capability of sensing the city through spatial, temporal, and social data, must be able to model and simulate social dynamics in a city: the behavior, attitude, and preference of citizens and collectives and how they impact city life and transform transformation processes. Another distinctive feature of CDT is that it must be able to engage citizens (individuals, collectives, and organized civil society) and other civic stakeholders (utilities, economic actors, third sector) interested in co-designing the future of the city. In this paper, we discuss the motivations that led to the definition of the CDT, define its modeling aspects and key research challenges, and illustrate its intended use with two use cases in urban mobility and urban development.

cs.CY↗

arXiv2024

Large Language Models are Zero-Shot Next Location Predictors

Predicting the locations an individual will visit in the future is crucial for solving many societal issues like disease diffusion and reduction of pollution. However, next-location predictors require a significant amount of individual-level information that may be scarce or unavailable in some scenarios (e.g., cold-start). Large Language Models (LLMs) have shown good generalization and reasoning capabilities and are rich in geographical knowledge, allowing us to believe that these models can act as zero-shot next-location predictors. We tested more than 15 LLMs on three real-world mobility datasets and we found that LLMs can obtain accuracies up to 36.2%, a significant relative improvement of almost 640% when compared to other models specifically designed for human mobility. We also test for data contamination and explored the possibility of using LLMs as text-based explainers for next-location prediction, showing that, regardless of the model size, LLMs can explain their decision.

cs.CY↗

arXiv2024

Mixing Individual and Collective Behaviours to Predict Out-of-Routine Mobility

Predicting human displacements is crucial for addressing various societal challenges, including urban design, traffic congestion, epidemic management, and migration dynamics. While predictive models like deep learning and Markov models offer insights into individual mobility, they often struggle with out-of-routine behaviours. Our study introduces an approach that dynamically integrates individual and collective mobility behaviours, leveraging collective intelligence to enhance prediction accuracy. Evaluating the model on millions of privacy-preserving trajectories across three US cities, we demonstrate its superior performance in predicting out-of-routine mobility, surpassing even advanced deep learning methods. Spatial analysis highlights the model's effectiveness near urban areas with a high density of points of interest, where collective behaviours strongly influence mobility. During disruptive events like the COVID-19 pandemic, our model retains predictive capabilities, unlike individual-based models. By bridging the gap between individual and collective behaviours, our approach offers transparent and accurate predictions, crucial for addressing contemporary mobility challenges.

cs.CY↗

arXiv2023

Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation

Optimizing traffic dynamics in an evolving transportation landscape is crucial, particularly in scenarios where autonomous vehicles (AVs) with varying levels of autonomy coexist with human-driven cars. While optimizing Reinforcement Learning (RL) policies for such scenarios is becoming more and more common, little has been said about realistic evaluations of such trained policies. This paper presents an evaluation of the effects of AVs penetration among human drivers in a roundabout scenario, considering both quantitative and qualitative aspects. In particular, we learn a policy to minimize traffic jams (i.e., minimize the time to cross the scenario) and to minimize pollution in a roundabout in Milan, Italy. Through empirical analysis, we demonstrate that the presence of AVs} can reduce time and pollution levels. Furthermore, we qualitatively evaluate the learned policy using a cutting-edge cockpit to assess its performance in near-real-world conditions. To gauge the practicality and acceptability of the policy, we conduct evaluations with human participants using the simulator, focusing on a range of metrics like traffic smoothness and safety perception. In general, our findings show that human-driven vehicles benefit from optimizing AVs dynamics. Also, participants in the study highlight that the scenario with 80% AVs is perceived as safer than the scenario with 20%. The same result is obtained for traffic smoothness perception.

cs.AI↗

arXiv2022

Inequality, Crime and Public Health: A Survey of Emerging Trends in Urban Data Science

Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.

cs.CY↗

arXiv2022

Play&Go Corporate: An End-to-End Solution for Facilitating Urban Cyclability

Mobility plays a fundamental role in modern cities. How citizens experience the urban environment, access city core services, and participate in city life, strongly depends on its mobility organization and efficiency. The challenges that municipalities face are very ambitious: on the one hand, administrators must guarantee their citizens the right to mobility and to easily access local services; on the other hand, they need to minimize the economic, social, and environmental costs of the mobility system. Municipalities are increasingly facing problems of traffic congestion, road safety, energy dependency and air pollution, and therefore encouraging a shift towards sustainable mobility habits based on active mobility is of central importance. Active modes, such as cycling, should be particularly encouraged, especially for local recurrent journeys (e.g., home--to--school, home--to--work). In this context, addressing and mitigating commuter-generated traffic requires engaging public and private stakeholders through innovative and collaborative approaches that focus not only on supply (e.g., roads and vehicles) but also on transportation demand management. In this paper, we present an end-to-end solution, called Play&Go Corporate, for enabling urban cyclability and its concrete exploitation in the realization of a home-to-work sustainable mobility campaign (i.e., Bike2Work) targeting employees of public and private companies. To evaluate the effectiveness of the proposed solution we developed two analyses: the first to carefully analyze the user experience and any behaviour change related to the Bike2Work mobility campaign, and the second to demonstrate how exploiting the collected data we can potentially inform and guide the involved municipality (i.e., Ferrara, a city in Northern Italy) in improving urban cyclability.

cs.CY↗

arXiv2022

Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets

Anticipating audience reaction towards a certain piece of text is integral to several facets of society ranging from politics, research, and commercial industries. Sentiment analysis (SA) is a useful natural language processing (NLP) technique that utilizes both lexical/statistical and deep learning methods to determine whether different sized texts exhibit a positive, negative, or neutral emotion. However, there is currently a lack of tools that can be used to analyse groups of independent texts and extract the primary emotion from the whole set. Therefore, the current paper proposes a novel algorithm referred to as the Multi-Layered Tweet Analyzer (MLTA) that graphically models social media text using multi-layered networks (MLNs) in order to better encode relationships across independent sets of tweets. Graph structures are capable of capturing meaningful relationships in complex ecosystems compared to other representation methods. State of the art Graph Neural Networks (GNNs) are used to extract information from the Tweet-MLN and make predictions based on the extracted graph features. Results show that not only does the MLTA predict from a larger set of possible emotions, delivering a more accurate sentiment compared to the standard positive, negative or neutral, it also allows for accurate group-level predictions of Twitter data.

cs.AI↗

arXiv2022

Enhancing crowd flow prediction in various spatial and temporal granularities

Thanks to the diffusion of the Internet of Things, nowadays it is possible to sense human mobility almost in real time using unconventional methods (e.g., number of bikes in a bike station). Due to the diffusion of such technologies, the last years have witnessed a significant growth of human mobility studies, motivated by their importance in a wide range of applications, from traffic management to public security and computational epidemiology. A mobility task that is becoming prominent is crowd flow prediction, i.e., forecasting aggregated incoming and outgoing flows in the locations of a geographic region. Although several deep learning approaches have been proposed to solve this problem, their usage is limited to specific types of spatial tessellations and cannot provide sufficient explanations of their predictions. We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks. Compared with state-of-the-art solutions, CrowdNet can be used with regions of irregular shapes and provide meaningful explanations of the predicted crowd flows. We conduct experiments on public data varying the spatio-temporal granularity of crowd flows to show the superiority of our model with respect to existing methods, and we investigate CrowdNet's reliability to missing or noisy input data. Our model is a step forward in the design of reliable deep learning models to predict and explain human displacements in urban environments.

cs.CV↗

arXiv2022

Trajectory Test-Train Overlap in Next-Location Prediction Datasets

Next-location prediction, consisting of forecasting a user's location given their historical trajectories, has important implications in several fields, such as urban planning, geo-marketing, and disease spreading. Several predictors have been proposed in the last few years to address it, including last-generation ones based on deep learning. This paper tests the generalization capability of these predictors on public mobility datasets, stratifying the datasets by whether the trajectories in the test set also appear fully or partially in the training set. We consistently discover a severe problem of trajectory overlapping in all analyzed datasets, highlighting that predictors memorize trajectories while having limited generalization capacities. We thus propose a methodology to rerank the outputs of the next-location predictors based on spatial mobility patterns. With these techniques, we significantly improve the predictors' generalization capability, with a relative improvement on the accuracy up to 96.15% on the trajectories that cannot be memorized (i.e., low overlap with the training set).

cs.AI↗