相关论文: The NetMob2024 Dataset: Population Density and OD …
Human mobility has been traditionally studied using surveys that deliver snapshots of population displacement patterns. The growing accessibility to ICT information from portable digital media has recently opened the possibility of…
Stop location detection, within human mobility studies, has an impacts in multiple fields including urban planning, transport network design, epidemiological modeling, and socio-economic segregation analysis. However, it remains a…
This paper documents our release of a large-scale data collection of TikTok posts related to the upcoming 2024 U.S. Presidential Election. Our current data comprises 1.8 million videos published between November 1, 2023, and May 26, 2024.…
Characterizing human mobility patterns is essential for understanding human behaviors and the interactions with socioeconomic and natural environment. With the continuing advancement of location and Web 2.0 technologies, location-based…
The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has many applications. An important example is given by origin-destination matrices which contain the…
The analysis of pedestrian GPS datasets is fundamental to further advance on the study and the design of walkable cities. The highest resolution GPS data can characterize micro-mobility patterns and pedestrians' micro-motives in relation to…
This study employs Long Short-Term Memory (LSTM) networks to forecast key performance indicators (KPIs), Occupancy (OCC), Average Daily Rate (ADR), and Revenue per Available Room (RevPAR), across five major cities: Manchester, Amsterdam,…
Pandemic and epidemic diseases such as CoVID-19, SARS-CoV2, and Ebola have spread to multiple countries and infected thousands of people. Such diseases spread mainly through person-to-person contacts. Health care authorities recommend…
Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360{\deg} perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large-scale…
Urban venues serve as arenas for social mixing. While residential and activity-space segregation have been extensively studied, less is known about how the spatial structure of cities, particularly public transit infrastructure, shapes the…
This paper considers the problem of the private release of sample means of speed values from traffic datasets. Our key contribution is the development of user-level differentially private algorithms that incorporate carefully chosen…
Benchmarking and monitoring urban design and transport features is critical to achieving local and international health and sustainability goals. However, most urban indicator frameworks use coarse spatial scales that only allow…
Predicting human mobility is crucial for urban planning, traffic control, and emergency response. Mobility behaviors can be categorized into individual and collective, and these behaviors are recorded by diverse mobility data, such as…
The migration of scholars is a major driver of innovation and of diffusion of knowledge. Although large-scale bibliometric data have been used to measure international migration of scholars, our understanding of internal migration among…
The rapid spread of COVID-19 infections on a global level has highlighted the need for accurate, transparent and timely information regarding collective mobility patterns to inform de-escalation strategies as well as to provide forecasting…
The COVID-19 pandemic and the implementation of social distancing policies have rapidly changed people's visiting patterns, as reflected in mobility data that tracks mobility traffic using location trackers on cell phones. However, the…
Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a…
In this paper, we introduce the first release of a large-scale dataset capturing discourse on $\mathbb{X}$ (a.k.a., Twitter) related to the upcoming 2024 U.S. Presidential Election. Our dataset comprises 22 million publicly available posts…
An unprecedented information wealth produced by online social networks, further augmented by location/collocation data, is currently fragmented across different proprietary services. Combined, it can accurately represent the social world…
This paper introduces PoSSUM, an open-source protocol for unobtrusive polling of social-media users via multimodal Large Language Models (LLMs). PoSSUM leverages users' real-time posts, images, and other digital traces to create silicon…