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An important task in transportation studies is to accurately map a given set of trajectories representing moving individuals onto specific means of transportation, like trains or buses. In this paper, we consider the following problem: given a trajectory ...

Data spaces consist of trusted frameworks that manage the entire data lifecycle, encompassing various data models, metadata descriptors, ontologies for semantic interpretation, and data services for accessing, processing, and analyzing data. Domain-...

Various relevant aspects of our lives relate to the places we visit and our daily activities. The movement of individuals between regular places, such as work, school, or other important personal locations is getting increasing attention due to the ...

The COVID-19 pandemic has mainstreamed human mobility data into the public domain, with research focused on understanding the impact of mobility reduction policies as well as on regional COVID-19 case prediction models. Nevertheless, current research on ...

In recent years, there has been a surge in the development of models for the generation of synthetic mobility data. These models aim to facilitate the sharing of data while safeguarding privacy, all while ensuring high utility and flexibility regarding ...

Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage and querying data lakes, due to compressed storage and efficient data access via data skipping. However, when applied to spatial or spatio-temporal ...

Sophisticated location and tracking technologies have led to the generation of vast amounts of trajectory data. Of interest is constructing a small set of basic building blocks that can represent a wide range of trajectories, known as a trajectory ...

With growing urban population and urban concentration, various data-driven efforts are being made to achieve sustainable growth to promote equity, inclusion, and well-being. Among abundant urban data, mobility data is a source with rich semantic about ...

Citizen science (CS) projects are conducted with interested volunteers and have already shown promise for large-scale scientific research. However, CS tends to cultivate the sharing of large amounts of data. Towards this, our research aims to understand ...

Time series prediction models have played a vital role in guiding effective policymaking and response during the COVID-19 pandemic by predicting future cases and deaths at the country, state, and county levels. However, for emerging diseases, there is ...

As countries look toward re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to track past activities of infected users, one path to safe reopening is to ...

To manage the COVID-19 epidemic effectively, decision-makers in public health need accurate forecasts of case numbers. A potential near real-time predictor of future case numbers is human mobility; however, research on the predictive power of mobility ...

Predicting crowd flows is crucial for urban planning, traffic management and public safety. However, predicting crowd flows is not trivial because of three challenges: 1) highly heterogeneous mobility data collected by various services; 2) complex spatio-...

Fine population distribution both in space and in time is crucial for epidemic management, disaster prevention, urban planning and more. Human mobility data have a great potential for mapping population distribution at a high level of spatiotemporal ...

We present a scalable solution to link entities across mobility datasets using their spatio-temporal information. This is a fundamental problem in many applications such as linking user identities for security, understanding privacy limitations of ...

Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow changes. In this paper, we propose a unified neural network module to ...

With the rapid process of urbanization, revealing the underlying mechanisms behind urban mobility has become a crucial research problem. The movements of urban dwellers are often constituted by their daily routines, and exhibit distinct and contextual ...

The accelerating urbanization procedure is putting increasing pressure on the management of cities. The administrative zones by which a city is managed are setup based on historical or political reasons, while the dynamics of people is hardly considered ...

Recent developments in sensors, GPS and smart phones have provided us with a large amount of mobility data. At the same time, large-scale crowd-generated social media data, such as geo-tagged tweets, provide rich semantic information about locations and ...

The commercial success of smartphones increases the feasibility of mobile ad hoc networking in daily life; we define such networks as spontaneous smartphone networks SSNs. Efficient data delivery in SSNs is challenging because of the low node density, ...