A Survey on Deep Learning for Human Mobility (2012.02825v2)

Abstract: The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.

• The paper surveys deep learning methods for human mobility, detailing applications across four core tasks: location prediction, crowd flow, trajectory, and flow generation.
• It details how deep learning models like RNNs, CNNs, and GANs capture complex patterns in mobility data, providing advantages over traditional methods.
• The survey highlights deep learning's potential to enhance urban systems and public health, identifying key challenges like model interpretability and data privacy.

Overview of "A Survey on Deep Learning for Human Mobility"

The paper "A Survey on Deep Learning for Human Mobility" by Massimiliano Luca et al. presents a comprehensive survey focused on deep learning (DL) applications in human mobility, a key area with significant societal implications. This survey covers a variety of tasks and challenges, highlighting how DL solutions are applied to four primary mobility tasks: next-location prediction, crowd flow prediction, trajectory generation, and flow generation. By systematically organizing existing research, this paper serves as an essential guide for researchers aiming to leverage DL technologies to address mobility-related challenges.

Key Contributions

The authors emphasize the importance of understanding human mobility due to its broad impacts on urban planning, disease control, traffic management, pollution reduction, and social wellbeing. With the increasing availability of digital mobility data from sources like GPS, phone records, and social media, there has been a significant shift towards using AI, particularly DL, to analyze and predict human movement patterns.

Unlike surveys that focus solely on single tasks or traditional methods, this paper presents:

• A Taxonomy of Mobility Tasks: The paper categorizes human mobility tasks into predictive tasks (next-location and crowd flow prediction) and generative tasks (trajectory and flow generation), providing clarity and structure to the field.
• Challenges and Solutions: It discusses key challenges specific to each task and reviews how DL models, such as RNNs, CNNs, attention mechanisms, GANs, and VAEs, offer advantages over traditional models by capturing complex dependencies in human mobility data.
• Open Challenges and Future Directions: The authors identify ongoing issues in DL applications for mobility, such as geographic transferability, explainability, privacy, tunability, and interaction dimensions.

Insights on Deep Learning Approaches

Next-Location Prediction

Next-location prediction aims to forecast future individual destinations using historical mobility data. The survey reveals how DL methods surpass traditional models by efficiently capturing spatial and temporal dependencies, user preferences, and exogenous factors through architectures like RNNs and CNNs combined with attention mechanisms.

Crowd Flow Prediction

This task focuses on predicting the movement of crowds in urban areas. DL models integrate spatial and temporal dependencies using advanced architectures such as ConvLSTMs, which blend CNNs and RNNs, thus significantly enhancing the ability to model complex urban dynamics compared to classic time-series approaches.

Trajectory Generation

Trajectory generation is about creating synthetic mobility data that replicate real-world patterns. The paper highlights recent efforts using GANs and VAEs to address this task, overcoming limitations of traditional mechanistic models by providing versatile frameworks for synthetic yet realistic data generation.

Flow Generation

The field of flow generation predicts the interactions of human flows between locations based on geographic and demographic features. DL models in this area, although recent, offer promising improvements over traditional gravity models by including non-linear relationships and integrating richer contextual data.

Implications and Future Developments

The application of deep learning to human mobility provides substantial potential to enhance urban systems, improve emergency response strategies, optimize resources, and contribute to public health initiatives. The survey suggests several directions for future research, including improving model interpretability for better understanding of mobility patterns and integrating social interactions into mobility models.

Overall, this paper represents a valuable resource for researchers and practitioners seeking to understand and apply the latest DL techniques in the diverse and impactful field of human mobility. As DL solutions mature and data availability increases, we can expect significant advancements in addressing complex mobility challenges and enabling smarter cities and healthier communities.