Nation-wide human mobility prediction based on graph neural networks

Terroso-Sáenz, Fernando; Muñoz, Andrés

doi:10.1007/s10489-021-02645-3

Cited by 11 publications

(11 citation statements)

References 37 publications

(49 reference statements)

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“…In summary, this study revealed that geotagged Weibo data is representative of human mobility, which is consistent with recent research [43]. However, our research raises the issue of multi-dimensional heterogeneity which would be explored in the future with relevant learnings and other related models [44].…”

Section: Discussionsupporting

confidence: 91%

Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility

Liu

Wang

Guan

et al. 2022

IJGI

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Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies.

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Section: Discussionsupporting

confidence: 91%

Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility

Liu

Wang

Guan

et al. 2022

IJGI

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“…With the development of information technology and statistics, many forecasting methods have emerged, and these mainly focus on econometrics, time series, Bayesian statistics, etc. In recent years, with the rapid development of artificial intelligence (AI) technology with big data and machine learning (ML) as the core, some scholars have tried to use ML technology for HMP [67][68][69][70][71][72][73]; however, the limitations include the limited amount of HM data, different standards, and difficulties in access. In addition, the uncertainty of HM drivers and the difficulty of their quantification have led to the slow development of HMP research [4].…”

Section: Of 19mentioning

confidence: 99%

Transcending Time and Space: A Survey Methods, Uncertainty, and Development in Human Migration Prediction

Pu¹,

Huang²,

Yang³

et al. 2023

Preprint

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As a fundamental, overall, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities given constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Given the effect of human migration prediction, in combination with artificial intelligence and big data technology, the paper concludes with specific suggestions and countermeasures aimed at enhancing human migration prediction research results to serve economic and social development and national strategy.

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“…While previous works [29,40,46,53] have used variations of DNNs to model mobility, they are limited due to the use of global information and do not handle significant link dynamics. While several works [1,17,37] leverage the generalization capability of Graph Neural Networks [5,24,39] for routing or network optimization they again do not handle link dynamics or device mobility.…”

Section: Dnns To Model Network Dynamicsmentioning

confidence: 99%

Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency

Manfredi¹,

Wolfe²,

Zhang³

et al. 2022

Preprint

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Designing effective routing strategies for mobile wireless networks is challenging due to the need to seamlessly adapt routing behavior to spatially diverse and temporally changing network conditions. In this work, we use deep reinforcement learning (DeepRL) to learn a scalable and generalizable single-copy routing strategy for such networks. We make the following contributions: i) we design a reward function that enables the DeepRL agent to explicitly trade-off competing network goals, such as minimizing delay vs. the number of transmissions per packet; ii) we propose a novel set of relational neighborhood, path, and context features to characterize mobile wireless networks and model device mobility independently of a specific network topology; and iii) we use a flexible training approach that allows us to combine data from all packets and devices into a single offline centralized training set to train a single DeepRL agent. To evaluate generalizeability and scalability, we train our DeepRL agent on one mobile network scenario and then test it on other mobile scenarios, varying the number of devices and transmission ranges. Our results show our learned single-copy routing strategy outperforms all other strategies in terms of delay except for the optimal strategy, even on scenarios on which the DeepRL agent was not trained. CCS CONCEPTS• Networks → Routing protocols; Network protocol design; Mobile ad hoc networks; • Computing methodologies → Sequential decision making; Neural networks.

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Nation-wide human mobility prediction based on graph neural networks

Cited by 11 publications

References 37 publications

Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility

Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility

Transcending Time and Space: A Survey Methods, Uncertainty, and Development in Human Migration Prediction

Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency

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