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Transformer-based models are popular for time series forecasting and spatiotemporal prediction due to their ability to infer semantic correlations in long sequences. However, for human mobility prediction, temporal correlations, such as location patterns ...

Predicting human mobility has been actively studied for the past decade because of its various possible applications, such as traffic optimization and urban planning. Despite the increasing interest in human mobility prediction, the training and ...

In this paper, I propose personalized spatiotemporal models based human mobility prediction method. The proposed method consists of three steps. Step1: we sin-cos transform the date and time data, and at the same time create variables representing ...

Machine learning models for predicting human mobility often require large datasets for training, which are not always available. As a result, methods capable of learning from limited data are essential. The Human Mobility Challenge 2024 was designed to ...

Human mobility prediction plays a critical role in applications such as disaster response, urban planning, and epidemic forecasting. Traditional methods often rely on designing crafted, domain-specific models, and typically focus on short-term ...

Human mobility generation plays a critical role in urban transportation planning. Existing human mobility generation models often fall short of understanding travelers' demographics and integrating multimodal information, including activity purposes, ...

Understanding the characteristics of various urban areas is crucial for applications such as urban planning, tourism policies, market analysis, and infection control. Techniques for embedding areas as vectors in a latent space based on human mobility ...

Human mobility is critical in urban planning, transportation systems, and public health policies. Modeling human mobility patterns is complex due to the variability in movement behaviors and the intricate spatial-temporal dependencies involved. Graph ...

The great social development of the last few decades has led more and more to free time becoming an essential aspect of daily life. As such, there is the need to maximize free time trying to enjoy it as much as possible and spending it in places ...

A major limiting factor for prediction algorithms is the forecast of new or never before-visited locations. Conventional personal models utterly relying on personal location data perform poorly when it comes to discoveries of new regions. The reason is ...

Data from the cellular network have been proved as one of the most promising way to understand large-scale human mobility for various ubiquitous computing applications due to the high penetration of cellphones and low collection cost. Existing mobility ...

It is a subject worthy of being studied to predict human mobility through the big data of human movement trajectory. The prediction has been widely used in many fields. In this subject, we record and analyze the movement trajectory of students on campus ...

Can health conditions be inferred from an individual's mobility pattern? Existing research has discussed the relationship between individual physical activity/mobility and well-being, yet no systematic study has been done to investigate the ...

Agent-based simulations, combined with large scale mobility data, have been an effective method for understanding urban scale human dynamics. However, collecting such large scale human mobility datasets are especially difficult during rare events (e.g., ...

Cellular network data has been proved as one of the most promising ways to understand large-scale human mobility due to its high penetration of cellphones and low collection cost. Most existing mobility models driven by cellular network data are based on ...

Nowadays, rapid urbanization leads to severe urban challenges, e.g., congestion and energy consumption, related to human mobility. To address them, it is essential to (i) measure and predict human mobility based on data from urban infrastructure, and (...

The high-frequency mobility of a massive population has caused an enormous influence on the urban internal structure, which is unable to be described by traditional data sources. While recent advances in location-based technologies provides new ...

The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and ...

With the wide adoption of multi-community structure in many popular online platforms, human mobility across online communities has drawn increasing attention from both academia and industry. In this work, we study the statistical patterns that ...

In this systematic literature review an overview of the recent developments in the field of Next Place Prediction is given. Next Place Prediction in this work refers to the prediction of where an individual human will go to next, based on continuous ...